DEV Community: Anastasiia Bernatska

Top computer vision applications in different industries right now

Anastasiia Bernatska — Wed, 26 May 2021 14:41:32 +0000

Computer vision applications have become ubiquitous nowadays. It’s hard to think of a domain where the ability of computers to “see” what’s going on around them has not yet been leveraged. Delivering process automation and accuracy, computer vision technology is expected to have even greater momentum due to the COVID-19 pandemic as organizations have rushed to adopt automation on a larger scale. More nuanced use cases of computer vision in different industries are also predicted to emerge with the next evolutionary leaps in artificial intelligence development, a field computer vision is part of. Let’s take a look at inroads already made by computer vision, its vast applications in different industries, and benefits.

What is computer vision, and how has it evolved?

Computer vision (CV) is a subset of AI that enables systems to interpret information from digital images and react to it with action or recommendations. The goal of computer vision technology is to emulate human vision for performing monotonous or complex visual tasks faster and even more efficiently.

Historically, it all started with a lot of manual coding until machine learning progressed enough to allow developers to program smaller computer vision applications and apply statistical learning algorithms for specific CV-related tasks such as pattern recognition. With AI making major strides, ML algorithms have been largely replaced by deep learning or hybrid models that rely on neural networks to transform patterns into mathematical equations for information classification. After the launch of the first commercial software back in the 1970s, computer vision applications have evolved from enabling reading devices for the blind to transforming entire industries. Some systems powered by computer vision have achieved 99% accuracy and can even surpass human performance (for instance, in diagnostic radiology).

The key drivers behind the surge in computer vision applications are:

Spreading mobile technology that allows us to create and share billions of images a day
Increasingly affordable computing power to process this massive amount of visual data
New hardware
Burst of deep learning algorithms

What can computer vision do? — Major computer vision techniques

With the growth in visual data and advances in computing power to process it, present-day computer vision applications rely on the following technology capabilities:

Object classification to assign objects in a photograph or video to predefined classes. With binary classification, the system answers a simple question whether a particular object belongs to, say, a class of apples, tourist attractions, or cats.
Object localization to locate an object in an image by enclosing it into a bounding box.
Object detection to do both of the above to many objects in an image, assigning labels and localizing objects by drawing bounding boxes around them.
Semantic segmentation to understand every pixel of an image and associate it with a class label (a car, a person, etc.) by creating object masks, with objects of the same class presented as a single entity.
Instance segmentation to do semantic segmentation and additionally identify different instances of the same class so that you don’t get just a one-color mask for three parked cars in a street view photo but label the vehicles with three different colors, identifying their boundaries.

Computer vision applications — Benefits and real-world examples

Applications for computer vision have been expanding at a rapid pace over the last decade to reach a frantic level with the onset of the COVID-19 pandemic. Organizations now invest heavily in AI-driven solutions, from retail software and healthcare platforms to advanced manufacturing and government systems. Many are even concerned that AI technology is moving too fast for them, according to a survey of business leaders conducted by KPMG. While there is still an overall lack of AI regulation that comes with AI explainability challenges and bias risk, computer vision technology is a safer playing field due to its maturity. Given the investment speed and immediate benefits delivered by computer vision implementation, the worldwide market of computer vision solutions is projected to grow at a CAGR of 7.6% to $19.1 billion from 2020 to 2027. We’ve compiled a list of industries and use case examples to demonstrate how companies leverage advanced computer vision techniques to boost their results:

Computer Vision Applications in Retail & Ecommerce
Computer Vision Applications in Education
Computer Vision Applications in Healthcare
Computer Vision Applications in Fitness & Sports
Computer Vision Applications in Precision Agriculture
Computer Vision Applications in Manufacturing & Mining
Computer Vision in Cross-Industry Applications

Computer vision applications in retail and ecommerce

Benefits :

Minimizing human interactions in stores to boost safety, deliver digital shopping experience, and lower labor costs
Personalizing customer experience for increased engagement and more effective upselling and cross-selling strategies
Leveraging next-gen in-store analytics to prevent stockouts, enhance store layout designs, and optimize staff scheduling

Use cases:

Automated checkouts and cashierless stores combine computer vision techniques with shelf sensors and deep learning to recognize shoppers, detect items they place into their carts, and automatically charge them on their accounts for items bought when they leave the store. Apart from the famous Amazon Go example, automated stores using sophisticated computer vision technology have also been launched by Chinese giants Lenovo, JD, and Alibaba. In the startup space, AI provider Tiliter’s self-scanning scale solution can automatically identify fresh produce, allowing customers to check out without the need for barcodes or packaging.
In-store navigation systems, powered by computer vision algorithms, can find the most efficient route to products on a customer’s in-app shopping list and recalculate the route if the person decides to look at other items. Currently, home improvement chain Lowe’s tests such an indoor mapping app developed with Google. Combining computer vision and augmented reality software, the app reportedly locates users in an aisle more accurately than Wi-Fi or mobile phone systems while also providing access to product reviews.
Product information display apps feature Scandit’s mobile barcode scanning software based on computer vision that recognizes objects for shoppers to get a personalized offer or information on the product in the store.
Visual search solutions that leverage deep learning algorithms make it more convenient and easier for online shoppers to discover products, returning visually similar results. eBay was among the first to offer its customers this engaging computer vision experience.
Personal recommendations are enabled by an array of computer vision-based systems. Among them are virtual mirrors that also use augmented reality, allowing shoppers to try on various clothes virtually (like on Amazon’s site) or experiment with makeup products (like via Sephora’s app or the Bourjois Magic Mirror). In-store virtual dressing room technology, embedded in Findmine’s Complete the Look solution, offers shoppers a touchscreen display with a CV-powered camera that recognizes items they are wearing to create an outfit based on images from the retailer’s catalog. Ecommerce sites are eyeing computer vision-enabled body scanning technology like the Bodygram service to provide size or clothing recommendations. The skin care brand Neutrogena invites their customers to try the Skin360 app that measures their skin health by assessing facial attributes and skin pixels and recommends a skin care routine. The Lolli & Pops candy store uses facial recognition to identify regular shoppers for offering them personalized product recommendations or loyalty discounts.
Inspection systems based on computer vision methods help retailers improve inventory management. Examples range from the Shelfie technology that uses shelf-mounted computer vision cameras to alert staff about out-of-stock or incorrectly displayed items to the mobile robot Tally that not only notifies shop assistants of gaps on shelves but also can detect damaged packaging and inaccurate pricing.
Connected stores use computer vision-powered cameras to analyze customer journeys and get product-related insights. Among them are Samsung’s famous pop-up store infused with capabilities to analyze dwell time, demographics and other customer data, and Serbian fashion retailer Legend World Wide’s store built in collaboration with Deloitte to gauge customer movement heatmaps.

Computer vision applications in education

Benefits:

Understanding students’ learning behaviors to drive personalization and improve learning experiences
Automating classroom monitoring to deter cheating in tests
Assessing students’ papers to reduce the burden on educators

Use cases:

Student engagement detection and personalization. With computer vision-powered platforms, educators can measure students’ mood and behavior to capture signs of engagement or distraction both online and in a classroom. Solutions in this space include Emotuit that uses facial recognition to analyze students’ emotional response to online content and the Little Dragon learning app that also reads facial expressions to detect frustration or boredom and adapt learning content. In addition, computer vision-driven insights help teachers regroup students into a more comfortable environment to improve learning.
Attendance monitoring and automated online proctoring has been made easy with computer vision-powered webcams that are used to identify students and flag cheating behaviors by tracking their postures or eye movements. Examples include UAuto with multi-factor authentication launched by the ProctorU proctoring service and automated proctoring solutions like Respondus Monitor and Mettl.
Handwritten character recognition is an area where computer vision is also expected to shine, as advanced algorithms are able to not only recognize responses written by students but also assist with their automatic evaluation.

Computer vision applications in healthcare

Benefits:

Improving patient identification to prevent wrong person procedures
Delivering more accurate diagnosis through medical imaging analysis
Providing assistance in surgery training and real-world surgeries for better outcomes
Delivering rehabilitation assistance to patients

Use cases:

Patient identification systems use computer vision-based cameras that help improve facial authentication of patients from check-in to discharge to prevent wrong person procedures.
Medical image analysis assisted by computer vision is transforming radiology, helping practitioners interpret X-ray, CT scans, MRIs, and even microscopic images of cellular structures more accurately when diagnosing breast, brain, lung, or skin cancer. Computer vision applications in medical imaging also feature solutions to estimate a human pose in analyzing symptoms of neurological and neuropsychiatric disorders, monitor blood loss to optimize blood transfusions, diagnose eye conditions, and even detect COVID-19 (a deep convolutional neural network called COVID-Net has showed 90% accuracy in diagnosing COVID-19 based on chest X-ray images).
Surgical simulation and assistance leverages computer vision technology to increase surgical precision. Apart from assistant surgical robot systems, there are solutions like Proprio Vision that combine computer vision with ML and VR to create 3D visualizations for surgeons in the operating room. In the surgical training space, Touch Surgery is a famous mobile simulator that provides a detailed guide to surgical procedures.
Rehabilitation applications feature computer vision systems that are being developed to supervise exercise routines as part of at-home rehabilitation for rheumatoid arthritis, sports injury, brain injury, or stroke.

Computer vision applications in fitness and sports

Benefits:

Capturing performance data to aid coaches in training sessions and athletes in self-training
Introducing advanced player or ball tracking methods to improve viewing experience or help referees in decision-making
Collecting performance statistics for scouts, sports betting sites, and other industry professionals

Use cases:

Tracking systems powered by computer vision-enabled cameras detect and track moving players or balls in an array of games such as soccer, tennis, baseball or golf. Top examples include SentioScope, designed by Sentio for soccer player tracking and analysis, and the SportVU 2.0 optical tracking technology that gives football coaches a holistic view of matches. Computer vision-based systems are also used for improving shooting accuracy in basketball training (Noah System), help swimmers improve their techniques by collecting data from stroke rates to real-time velocity and turn times (FINIS LaneVision), and even can take over in part the job of umpire in professional tennis matches (the Hawk-Eye ball tracking solution).
Self-training solutions that are based on computer vision techniques like pose estimation for motion analysis can help users improve their self-training, with Zenia, for instance, claiming that its fitness app powered by computer vision and ML can recognize yoga asanas with 95% accuracy. Such fitness solutions can also use markers as the Carbon intelligent fitness mirror does, unlocking data on user performance with the help of weight sensors to offer intelligent suggestions.
In-depth data analytics platforms analyze actions of players on the ice or field, producing meaningful insights either for building better game strategies or making smart decisions on players in the scouting market or engaging viewers. Among famous examples are the Sportlogiq system that collects raw data from video feeds to produce game models and the AutoSTATS solution that can analyze any recorded basketball game for performance improvement and scouting insights.

Computer vision applications in precision agriculture

Benefits:

Identifying pests and weeds with greater accuracy to optimize the application of chemicals
Monitoring crop development and the environment to maximize yields and produce better quality according to rising customer expectations
Automating livestock management to prevent flock and herd losses and reduce the need for feet in the field

Use cases:

Pest and weed detection systems feature Blue River Technology’s See & Spray solution equipped with intelligent cameras that can distinguish between crops and weeds to apply herbicides to the right plants. Computer vision methods are also used for automatic identification and counting of flying insects and even for identifying apple diseases early.
Observation, harvesting, and prediction systems are developed to detect the ripeness of fruit, including through color ratings of cherries in an outdoor environment, or pick vegetables in a greenhouse using robots. Computer vision-aided monitoring solutions include SolarXOne, a 100% autonomous solar-powered drone system from XSun, that provides farmers with HD images capturing crop and soil conditions. AI solutions from Brazil’s Cromai can gauge information about the color, shape, and texture of crops. High-definition cameras from SWIR Vision Systems are equipped with sensors to help monitor soil moisture for yield prediction.
Livestock management systems using drone technology can perform automatic counts, detect sick or injured animals, find strays, spot grazed areas, and even move cattle. A fair example is autonomous drone technology developed by Israeli firm BeeFree Agro to herd cattle.

Computer vision applications in manufacturing and mining

Benefits:

Implementing automated quality control to increase manufacturing accuracy, improve productivity, and produce better quality
Deploying monitoring solutions to cut inspection time, minimize safety risks, improve operator productivity, and increase cost efficiency
Reduce human involvement to protect workers from hazardous environments

Use cases:

Next-level quality control can be enabled by intelligent computer vision-powered cameras directed at a manufacturing line. Examples include Pharma Packaging Systems’ machinery for the pharmaceutical industry to automatically count tablets or capsules on production lines and WebSPECTOR, a surface inspection system that identifies defects in items, stores images, and collects image-related metadata to classify errors by type and grade. Сomputer vision methods are also used to guide assembly operations, like assembly verification solutions from Acquire Automation that measure product components versus manufacturing specifications, check caps and fill levels, and verify packaging components.
Robot palletizing systems guided by machine vision automatically load or unload boxes and items to and from pallets.
Predictive maintenance systems assisted by computer vision technology and sensors have made it much easier to track the condition of critical infrastructure and determine when maintenance is needed. For example, FANUC’s Zero Down Time takes photos and collects metadata to uncover any potential problems in the machine. Oil and gas giants such as Shell, ExxonMobil, and BP are using computer vision-powered predictive maintenance to anticipate failures in their equipment.
Intelligent monitoring solutions, including drone-assisted systems, allow companies to conduct remote inspections of their sites and assets. This application of computer vision is especially important in mining, an unsafe industry for workers, where operators need to collect visual data in difficult areas. Visual inspections of well sites using the Osprey Reach system, for instance, have enabled operators to reduce routine site visits by half.

Computer vision in cross-industry applications

Warehouse management systems using computer vision technology reduce inventory times from hours to minutes, delivering huge savings on operational costs. Such systems feature the Gather AI computer vision-based platform that uses drones and connects to IoT devices for scanning and counting inventory. Another example is Amazon that is rolling out the Pegasus robot technology at its sorting centers, claiming that the robot can improve sorting accuracy by 50%.
Mobile computer vision enables a contactless delivery process for retail, logistics, post, and parcel businesses, transforming smartphones and other smart devices into computer vision-enabled barcode scanners like solutions from Scandit. The frictionless way to pick items from warehouses, distribution centers, or retail rooms is growing in popularity in the age of COVID-19. In retail, barcode scanning software is used for order fulfillment, while allowing shoppers to safely collect items with one contactless mobile scan.
Safety monitoring solutions using computer vision help keep public spaces safe during the pandemic by detecting ill employees or students as well as monitoring social distancing or exposure times. At construction sites and on the manufacturing floor, computer vision systems like IRIS monitor behavior-based safety and can protect people working around hazardous zones by alerting machine operators of dangerous events or incidents.

Looking forward

It might be hard to believe but we can uncover more computer vision applications and benefits with the advancement of technology such as edge computing, emotion AI, mixed reality, and embedded vision. And they can be quite incredible as artificial intelligence will get as sophisticated as we humans are.

Drop ITRex a line if you want to explore the benefits that computer vision can bring to your organization. Their AI experts will be happy to help you with complex or straightforward computer vision initiatives to address your specific business needs, and may as well develop something niche for you.

Big Data in healthcare: where does the value come from?

Anastasiia Bernatska — Mon, 24 May 2021 15:29:53 +0000

The pandemic is having an enormous impact on the healthcare sector. Between overwhelming hospitalization rates, intensifying cybersecurity threats, and an aggravating number of mental illnesses due to strict lockdown measures, hospitals are desperately searching for help. Big data in healthcare seems like a viable solution. It can proactively provide meaningful, up-to-date information enabling clinics to address pressing issues and prepare for what’s coming. Hospitals are increasingly turning to big data development service providers to make sense of their operational data. According to Healthcare Weekly, the global big data market in the healthcare industry is expected to reach $34.3 billion by 2022, growing at a CAGR of 22.1%. So, what is the role of big data analytics in healthcare? Which challenges to expect? And how to set yourself up for success?

What is big data in healthcare, and how is it used?

Big data has several accepted definitions. Here are two popular ones:

Douglas Laney’s definition. Laney is a former Chief Data Officer at Gartner. He states that big data is characterized by 3 Vs: volume, velocity, and variety. Volume stands for large amounts of data. Velocity refers to the speed of collecting data and making it accessible, while variety indicates the different types of data, such as text, video, logs, audio, etc.McKinsey’s definition. The renowned consulting firm defines big data as datasets whose size is beyond the ability of typical database software tools to capture, store, manage, and analyze.

According to an IDC report, the volume of big data is expected to reach 175 Zettabytes by 2025. To put it in perspective, it will take 1.8 billion years to download this amount of data with the average internet speed available nowadays.

Big data in healthcare: sources and benefits

Healthcare big data refers to the massive amounts of health-related data coming from various sources, such as electronic health records (EHRs), genomic sequencing, medical research, wearables, and medical imaging, to mention a few. This data is enormous in its volume and diverse in its format, making it difficult to store in conventional databases, and is too complex for traditional data processing technologies. There is an emergent discussion that “big” is no longer the defining parameter. For the healthcare sector, it is more important how “smart” the data is and which type of insights it provides.

Source

Benefits of big data in healthcare

The healthcare sector is lagging in big data adoption due to the sensitivity of healthcare information. Despite big data problems in healthcare, hospitals are eager to deploy innovative technology to unlock the benefits of big data in medicine.

These benefits include:

Reducing costs
Improving patient outcomes
Advancing competitive position
Understanding an organization’s performance
Detecting illnesses at early stages
Enhancing personnel management

Two main trends are currently pushing big data adoption in the medical field:

The movement towards a value-based care model that rewards clinics for their patient population health.
The need for evidence-based information to understand the best practices related to diseases and injuries management.

Big data analytics for healthcare

To extract meaningful insights from big data, healthcare organizations resort to analytics. Big data analytics is the process of uncovering patterns in large amounts of data with the help of different techniques originating in mathematics, statistics, computer science, and economics. McKinsey’s big data report enumerates the most prominent ones:

Machine learning: it is a subset of AI. This technique trains algorithms on labeled data.
Data mining: uses statistics and machine learning to extract patterns from datasets.
Natural language processing: this technique is a subdomain of AI and linguistics. It uses algorithms to analyze human language.
A/B testing: compares test groups with a control group to identify which changes have impact on the examined variable.
Data fusion and data integration: techniques that integrate data from diverse sources and analyze it.
Statistics: collect, classify, and interpret data from experiments and surveys.

Types of big data analytics in healthcare

The role of big data analytics for healthcare is to separate information from the noise and extract insights that will benefit all stockholders, from doctors to patients to hospital managers to medical tech startups. There are four main types of healthcare big data analytics:

Discovery analytics: analyzes raw, incoming data to identify patterns and outliers. If the incoming data is incomplete and full of noise, the results will be questionable
Descriptive analytics: uses historical data to analyze trends and help understand what happened in the past. It doesn’t predict future events.
Predictive analytics: depends on modeling techniques to forecast what will happen in the future, given that all conditions will remain constant. If circumstances change, the prediction loses its validity.
Prescriptive analytics: employs machine learning to analyze data from multiple sources and suggest a course of action. These techniques are still limited in their capabilities.

7 use cases of big data in the healthcare industry

Enabling real-time alerts
Improving population health
Facilitating medical research
Contributing to cancer treatment
Preventing cyberattacks and fraud
Managing mental health conditions
Mitigating hospitalization risks

1. Enabling real-time alerts

Clinical decision support (CDS) software and other healthcare software solutions analyze data in real time, offering timely advice to medical professionals while diagnosing patients or composing treatment plans. This example of big data in healthcare is particularly relevant when speaking of wearable medical devices. Wearables constantly collect patient data, which is then analyzed and presented to doctors, enabling them to react immediately if the results are alarming. For example, if a patient’s blood pressure suddenly increases, their doctor will receive an alert on the spot and administer measures to help the patient. Furthermore, real-time alerts can be useful to monitor hospital staff and ensure compliance. One example comes from the collaboration between ITRex and Connecticut-based Hygenix Inc., a patient safety technology company. Hygenix wanted to monitor healthcare employees’ handwashing compliance and give timely notifications if the hygiene standards were breached. ITRex built an IoT ecosystem, which included a smart wristband, software application for data management, data analytics and visualization tool. The results of deploying this solution in hospitals were astonishing. The clinics witnessed a 70% increase in hand hygiene compliance throughout the first week, the duration of handwashing has doubled, while sanitizer use has tripled.

2. Improving population health

Population health research helps medical clinics understand the vulnerabilities of specific cohorts of patients and address the issues before they escalate. One example of this big data application in healthcare is developing models that predict the risk of falling for seniors in the age group of 75 to 85 years old. For an accurate prediction, one needs to combine data from diverse sources, such as EHRs, to understand their medical history, and social factors to see their living conditions. Demographics do not restrict population cohorts. They can also be formed based on shared medical conditions, lifestyle, risks, etc. Connecticut-based SCIO Health provides analytics solutions on a global scale. Their technology uses big data to identify care gaps that result in increased costs and bad patient outcomes. After seeing these gaps, doctors can preemptively target at-risk patients and avoid hospitalization. Another example comes from Massachusetts-based Linguamatics, which uses predictive analytics and natural language processing to interpret unstructured health data and identify lifestyle factors that can lead to health complications in the future. Moreover, medical researchers can use predictive modeling to analyze disease outbreaks. This gives an opportunity to develop targeted vaccines faster and prevent a health crisis. At the moment, big data has the potential to predict COVID-19 hotspots as algorithms can analyze medical history and trace contacts of infected patients.

3. Facilitating medical research

Another impact of big data in healthcare is contributing to drug production. Drug discovery, creation, and acceptance is a tedious process regulated by strict protocols. It involves multiple rounds of testing, and the time to market can be rather lengthy. With big data and machine learning, scientists apply data models to predict potential drug effects instead of carrying out actual lab experiments, which are time-consuming. Moreover, conventional pre-clinical drug testing is performed on animals and is not fully representative of human outcomes. Artificial intelligence and big data in healthcare allow simulating drug effects on virtual humans. When the time comes, big data repositories help researchers locate and recruit suitable patients for advanced stages of drug testing. Identifying drug molecules Another use case of big data in medical research is finding the right drug molecules. After scientists identify a biological target, they start researching molecules that can interact with it and produce the desirable effect. A health tech startup, Atomwise, employs deep learning neural networks for medicine discovery. With their software, the company managed to go through 8.2 million compounds in a few days to find a potential cure for multiple sclerosis. Organizing medical knowledge Big data in healthcare analytics also contributes to organizing the vast medical knowledge base. For instance, New Jersey-based Innoplexus developed a discovery tool that arranges medical dissertations, articles, clinical trial documentations, etc. and makes these materials searchable for pharmaceutical companies developing new drugs.

4. Contributing to cancer treatment

Big data analytics benefits cancer research as scientists need to go through vast amounts of data to unveil remedies with the highest success rate. They examine tumor samples from biobanks and link them to patients’ medical records to discover how cancer proteins interact with different treatments. This big data examination can yield unexpected results. One such study discovered that Desipramine, an antidepressant, can aid in curing certain types of lung cancer. However, this type of big data analytics requires a comprehensive cancer database that would integrate information from hospitals, medical universities, and other relevant organizations. New York-based Flatiron Health developed a solution that connects different players in this field, from oncologists to academics, and allows them to learn from each other. The company offers access to billions of data points from different cancer patients. Big data analytics and breast cancer Big data contributes to breast cancer detection. The traditional breast cancer prediction models consider only a limited set of factors, such as family history, past biopsies, and breast density. Big data analytics can accommodate a higher level of detail from mammograms and patient records. Recently, researchers at Massachusetts General Hospital developed machine learning algorithms that can evaluate the risk of breast cancer by identifying imaging biomarkers on mammograms. Here is what Leslie Lamb, Division Chief of Breast Imaging at the hospital, said about this big data tool: “Why should we limit ourselves to only breast density when there is such rich digital data embedded in every woman's mammogram? Every woman's mammogram is unique to her just like her thumbprint. It contains imaging biomarkers that are highly predictive of future cancer risk, but until we had the tools of deep learning, we were not able to extract this information to improve patient care."

5. Preventing cyberattacks and fraud

Healthcare providers are among the most targeted organizations when it comes to cybersecurity breaches. This sector accounts for nearly four out of five industrial breaches. The costs of these security violations amounted to $4 billion in 2019. This is not surprising, considering the value of personal health data. A healthcare record can be sold for $250 on the black market, compared to only $5.4 for payment card information. Healthcare organizations are opting for big data analytics in healthcare to block security threats by monitoring changes in network traffic or any other relevant behavior. Predictive analytics in healthcare using big data also help prevent insurance claims fraud as they use a combination of rules, data and text mining, and database searches.

6. Managing mental health conditions

This is one of the most relevant big data examples in healthcare to COVID-19. According to the National Institute of Mental Health, one in five adults in the US lives with mental illness. Big data and AI can advance mental health education and understanding. The powerful duo enables therapists to analyze data from social media, wearable devices, internet activities, and other sources using natural language processing tools to identify psychological markers associated with particular mental conditions. The World Psychiatry journal published a study showing that speech elements can hint at mental disorders. For instance, the frequency of using possessive nouns is an indicator of whether someone at risk of psychosis will actually become psychotic. The tool presented in the journal had 83% accuracy. Suicide prevention How big data helps healthcare in suicide prevention? Crisis Text Line provides crisis counseling via text to English-speaking countries. It uses big data and machine learning models to prioritize people who need help. As the demand often exceeds the staff capacity, not everyone can be helped on time. The algorithms analyze unstructured text to spot patients who need support urgently. The Crisis Text Line tool presented some unexpected findings. For example, the words “Ibuprofen” and “Advil” are 14 times more predictive of suicide than “die” and “cut”. Also, the crying-face emoji is 11 times more predictive of suicide than the word “suicide” itself. If you don’t know this in advance, you can give priority to the wrong people and fail to offer support to the ones who really need it.

7. Mitigating hospitalization risks

Big data and healthcare analytics can help track hospitalization risks for patients with chronic diseases. By analyzing various criteria, such as symptom severity, medication intake, and the frequency of doctor visits, clinics can provide targeted preventive care to reduce admissions. Simultaneously, by predicting who might be admitted, hospitals can allocate space and resources to prospective patients. During the pandemic, doctors are using big data in healthcare to gather data from all over the country to identify the spots that are at the greatest risk. According to Sampson David, an Emergency Medicine Physician, inspirational speaker, and a co-founder of The Three Doctors Foundation, “So now, we have to think about how we gather and share data across the country to pinpoint where the need is the greatest. And by knowing where the need is the greatest, we can then say that if we have a vaccine or treatment, we can start there to see what type of impact this treatment may have.”

Challenges of big data in healthcare

The importance of big data in healthcare is undeniable, but many challenges surround its implementation. Deloitte recently conducted the State of AI survey to evaluate how different organizations worldwide employ/plan on using AI. This survey revealed that AI costs are still among the most prominent obstacles.

Source: Deloitte’s State of AI Survey

Here are six challenges of big data relevant to the healthcare industry:

Implementation costs
Data aggregation and cleaning
Security
Communication gap
Management-related issues
Interoperability

1. Implementation costs

Costs still present a considerable challenge to adopting big data in healthcare. Medical clinics will need to purchase technology, acquire computational tools and software to manage the data, and purchase/develop custom applications to benefit from big data analytics. For example, building a custom patient engagement solution that works with big data can cost at least $120k. Another item on the expenditure list is salary payments. Organizations will have to hire data scientists and teach their staff how to work with data, which is also paid time that employees spend on learning instead of doing productive work. There is no way to bypass initial investment, but if hospitals consider all the possible expenses (even the indirect ones) beforehand, they will avoid unpleasant surprises in the future.

2. Data aggregation and cleaning

Patient data is spread across multiple sources, including payors and other medical organizations where patients received care. Pulling the data together will require collaboration between different parties and agreeing on a particular data format. Also, big data is heterogeneous and unstructured. Organizations will need to apply classification techniques to make this data suitable for analysis. Moreover, you will need to clean the aggregated data to ensure its consistency, accuracy, and correctness. The cleaning process can be either manual or automated based on logic rules. You will also need to determine how to deal with the new data coming in real time. Medical imaging big data requires special attention when it comes to storage and processing. Image mishandling can cause the introduction of noise, which, for example, can lead to the delineation of anatomical structures. As a result, the image will no longer correspond to reality.

3. Security

As mentioned above, data breaches are common in the healthcare sector, and they are costly in terms of finances and reputation. It is important to choose well-trusted vendors for big data in healthcare technology and implement encryption and other security measures. It is also essential to secure all the connected devices on the hospital network. When necessary, restrict access to some applications to predefined devices only, and limit the authorized personnel. Hospitals need to educate their staff members on data usage rules. Studies reveal that human error accounts for 31% of data breaches in the healthcare sector. Hence, it is crucial to explain how to access data safely and secure personal mobile devices (if used for work). Last but not least, ensure your antivirus software and firewall are up to date. Also, promptly update all data management programs as soon as the new version is available.

4. Communication gap

Miscommunication between data scientists and data users presents another common challenge of big data in healthcare. Doctors may not be able to adequately explain which data they need and how they prefer to store and access it. As a result, they will not understand the formats generated by data scientists and will avoid using this data and analytics to the full ability.

5. Organizational issues

Big data is changing the healthcare sector, and becoming a data-oriented organization requires alterations in the company’s culture and the way it makes decisions and conducts business. Recruiting new talent will be unavoidable. Existing employees will also need training and an adjustment period. Some organizations might be forced to replace their IT infrastructure, which means additional expenses and training hours. When using big data in decision-support activities, staff members might not be immediately eager to trust the recommendations delivered by data-based solutions.

6. Interoperability

This challenge will arise if a healthcare organization wants to cooperate with other parties within its ecosystem. Many clinics are still following the old-school approach, resorting to pen and paper. So, if you digitalized your data, do not assume that others have done the same. Even if your selected partner is fully digital, you can face semantic challenges as their applications are likely to use different terminology to describe the same thing. All this needs to be discussed before using big data in healthcare. Moreover, if your cooperation is on an international scale, you might encounter discrepancies in privacy laws.

How to move forward with big data in healthcare

Multiple problems are surrounding big data in healthcare, but if implemented correctly, it will pay off. You can set yourself up for success by following these steps:

On the managerial level:

Commit to a vision of becoming a data-driven organization. Identify the current processes that will need adjustment and be ready to implement the change. Communicate your ideas to the employees.
Ensure that you have the key stakeholders’ support to avoid misunderstandings at later stages of the project.
Prepare to recruit new talent, including data scientists and developers, or collaborate with a trusted tech partner.
Commit to training your existing employees. Allocate time and budget for this purpose.

On the data level:

Decide on data storage options, whether it's in the cloud or on-premises. The on-premises approach gives you control over access and security. But the cloud is cheaper, offers recovery plans, and facilitates expansion. It has been the preferred option for most healthcare organizations. Specially that cloud technologies are gaining in security and reliability. If you opt for the cloud, search for a cloud partner who understands healthcare-specific compliance and security requirements.
Make sure your infrastructure is scalable and can follow along with the unavoidable growth of data volumes.
Set up strong data governance practices, define the desired level of data quality and accuracy, and determine how the incoming data will be filtered.
Define data ownership and appoint owners of different datasets. These people will be accountable for data quality, completeness, etc.

In the end, big data in healthcare is purely a means to reach your strategic goals. There are several factors that you need to consider in the process.

As Robert Millette, Executive Director of Population Health at Lee Health, said during his interview with HealthITAnalytics: “Number one, you need to have a strategy. You need to know how you're going to differentiate yourself in whatever area you're looking to support with that tool. You should also remember that the unified data platform is simply a tool – it’s a means to an end. It is by far not the only thing. You have to have network infrastructures, and you have to have the right contracts with payers to move and perform in value. There are a number of different levers you need to have, and they're all important.”

Wondering how big data can benefit your medical facility? Get in touch with ITREx team, and their healthcare analytics experts will help you extract value from your data.

What is livestream shopping, and how does it impact retail?

Anastasiia Bernatska — Thu, 20 May 2021 17:06:05 +0000

With the introduction of lockdown measures and brick-and-mortar stores closing their doors, retailers’ reliance on ecommerce solutions has intensified. The push to online and the need to attract and retain customers has made retail brands invest more in digital advertising and bring livestream shopping to their marketing mixes. A hot trend, livestream shopping allows making otherwise feelingless online shopping more engaging and experiential while taking conversions way up. In this article, we’ll examine the phenomenon of livestream shopping, evaluate its impact on the retail sector, and zoom in on out-of-the-box and custom live video shopping tools.

The introduction to livestream shopping

What is livestream shopping?

The concept of live shopping goes back to the 80s and builds on the idea of video shopping. You are likely to remember QVC’s Lisa Robertson casually chatting with the viewers while demonstrating jewelry or HSN's Bobby Ray Carter showcasing fashion pieces with the viewers tuning in, seeing what’s for sale, and calling in to make a purchase. Livestream shopping — using video streaming tools to demonstrate products, communicate with an audience, and sell, all online — is a natural offshoot of this original TV-oriented model.

Combining the best of ecommerce, video streaming, and social media, live commerce lets people shop anytime and from anywhere, encourages real-time communication, and sparks interactivity, appealing to today’s tech-savvy, sit-at-home customers

Livestream shopping can take different forms, from live events and auctions to online marketplaces and influencer streams. Irrespective of the format, viewers can tune into a live shopping session, leave comments, ask questions, and communicate with a host or with one another. And once they are ready to purchase, they can click a link embedded in a shoppable video and proceed to checkout. Live shopping provides for personalization, too. Although one-to-many live streams, when products are marketed to mass online audiences, are more popular, brands can pay special treatment to their repeat buyers through one-to-one livestream sessions. With private dialogues between a host and a customer, brands can produce higher conversion rates and average order values and increase the customers’ chance of returning.

How does livestream shopping work?

Suppose you want to tap into livestream shopping. In that case, you have three options:

Leverage social media’s streaming functionality conditions and predicting patient outcomes
Market your products on an existing video commerce platform or a marketplace with streaming features
Opt for a custom livestream shopping solution

1. Social media

The most popular and perhaps most straightforward way to start with livestream shopping is going live on popular social media apps. Luckily, most social media platforms have introduced livestream and ecommerce functionality in response to the video shopping trend. Instagram, for instance, has added built-in checkout capabilities throughout its apps, including Instagram Live, which, by the way, has seen a 70% increase in the number of viewers in 2020. Facebook, too, is developing native livestream shopping tools. In turn, TikTok is partnering with ecommerce giant Walmart for the latter to market their products online. The critical advantage of turning to social media is the possibility to team up with influencers, who play a crucial role in the success of livestream shopping, right where their audiences are. A study from Adweek reports that 49% of respondents actively follow social media influencers and have made a purchase based upon an influencer’s recommendation. So, having influencers as hosts may help in developing trust and loyalty among customers. However, as social media platforms generate income via media buy, they focus on being social. So, ecommerce functionality is still limited to select brands. As a result, shoppers have to be routed to other platforms or websites to finalize purchases, which disrupts the whole retailtainment experience.

2. Out-of-the-box live shopping apps

Another way to start capitalizing on the video shopping trend involves joining an existing livestream shopping platform or an ecommerce marketplace with livestreaming features. And these span from video commerce giants like Amazon Live and Taobao, live shopping startups like Bambuser and Spin, and niche live commerce apps like NTWRK. The essential advantage is that most out-of-the-box video shopping apps feature built-in integrations with popular ecommerce platforms and payment gateways as compared to the previous option. So, users don’t have to be routed to other sites to complete a purchase and can enjoy an uninterrupted video shopping experience. The fundamental limitation is that some livestream shopping platforms, especially larger ones and those with established audiences, expect a brand to meet a range of requirements, like having a steady business history, and charge commissions. Amazon Live, for instance, charges up to 10% on each purchase. Another downside is rising competition — with millions of livestream views, it can be tricky for retailers to stand out.

3. Custom live video shopping applications

Retailers who seek to make video shopping an essential part of their marketing kit may consider developing a custom retail solution with livestream functionality. Going custom is undoubtedly more effort-intensive than the previous two options. But a custom app can let retailers take maximum advantage of the video shopping trend with tailored functionality, high-quality streaming, advanced analytics, and maybe even some extra features, like AI-powered video content personalization. If you do opt for a custom livestream shopping app, we recommend starting with an MVP. However, it is essential to plan for the future and make sure your app will handle the growing number of shoppers. For that, make time to choose a reliable content delivery network (CDN). Looking for the perfect CDN, make sure it has a robust server infrastructure close to your target locations and pay attention to the bandwidth. With the basics in place, you can think about MVP features. The essentials to include in the first app version are:

User registration and authorization
Online streaming as such
Live chatting
Checkout and payment
Server-side stream processing

With the subsequent iterations, you can add everything from different signup options, personal customer profiles, search options, the possibility to save streams as on-demand videos, advanced analytics, streaming content personalization, and more

What’s the livestream shopping market size?

The current livestream shopping trend has its roots in China. China’s ecommerce giants started holding live shopping events hosted by local celebrities back in the 2010s. The US has begun to catch up with the trend, too. The US livestreaming market is expected to reach $11 billion by the end of 2021 and hit $25 billion by 2023, according to Coresight Research. The market value is still incomparable to that in China, where it comprised $150 billion in 2020 and is expected to reach $300 billion in 2021. The companies that look prominent on the US livestream shopping market today include:

TalkShopLive. Launched in 2018, the platform has a 2-million follower base, which is constantly growing. According to Bloomberg, since its premiere, TalkShopLive’s sales have increased sevenfold
ShopShops. Having started as a directory for American retailers to reach Chinese travelers, the platform has turned into a profitable livestream shopping endeavor that has recently expanded into and is gaining popularity on the American market
NTWRK. The mobile app uses live shows to sell limited edition products. The company has seen its sales double in 2020, with some shows reaching $1 million in sales in approximately 10 minutes
Amazon. The ecommerce giant has rolled out its video streaming service with lifestyle, fitness, and cooking streams
Google. The company has introduced Shoploop — a video shopping platform that features 90-second shoppable stories
Instagram. Its Live Shopping feature allows creators and brands to sell products during Instagram Live broadcasts. The functionality is available to business accounts

An explosion of digital media and video content consumption, an increasing impact celebrities and influencers have on shopping decisions, and the push to online boosted by the COVID-19 pandemic allow projecting that more US retail brands will move on the live shopping trend in the near future.

Companies profiting from livestream shopping today

Nordstrom

Nordstrom, an American department store chain, launched its own shoppable livestream channel in 2020. Following the launch, the brand hosted over 50 virtual events, translating the store experience into the digital world, and plans on carrying out their livestream shopping endeavors through 2021. During each event, customers can shop the fashion products mentioned in the video and participate in a live chat. The company’s efforts are paying off. In 2020, 54% of the brand’s sales came from digital channels, while virtual styling accounted for 30% of all styling appointments.

CAIA Cosmetics, Lyko

CAIA Cosmetics and Lyko, Swedish cosmetic and hair care brands, have partnered with Bambuser, a video commerce startup, to power their marketing with live shopping. CAIA Cosmetics, for instance, holds make-up tutorials with the brand’s founder Bianca Ingrosso and claims to be very satisfied with the customers’ response. The average stream viewing time comprises 11 minutes, with 17% of the viewers participating in the live chat and 60% — engaging through likes. The first live stream ended up with a 5% conversion rate, which is higher than the brand’s website conversions. Lyko, in turn, chose to kick off their live broadcasts by offering tips and gifts during the holiday season. The brand had an average viewing time of 16.5 minutes, 17% chatting and 29% liking engagement, and a 22% conversion rate.

Quivr

Quivr, a coffee and tea manufacturer, has tried social media to market their products via video streams but eventually landed on Amazon Live as their video commerce platform. Quivr’s co-founder Ash Crawford says that the brand sees a guaranteed increase in sales of about 150% the next 24 hours after a live stream.

Livestream shopping trends and predictions

The possibilities for further evolution and expansion of livestream shopping are numerous. Introducing AI, for example, can help retailers create highly personalized channels of live, interactive content created or curated specifically for a given customer. Leveraging AR and VR, in turn, would be of help in creating immersive video shopping experiences for sit-at-home customers, for example, transporting them to the front row of a fashion show with a host ready to answer questions. In the shorter perspective, brands will continue to extend their reach to more customers through live streams, analysts say. Considering the drastic change in shopping habits and the push to online spaces that are expected to last, livestream shopping is likely to extend in the post-pandemic world.

Livestream shopping offers an opportunity to bring the best of physical shopping to online customers. If you’re ready to seize the opportunity, contact our team!

Supermarkets of the future: deploying AI in your grocery store

Anastasiia Bernatska — Tue, 11 May 2021 11:22:39 +0000

COVID-19 is ruthlessly shaping ordinary stores into supermarkets of the future. They must accommodate all the new grocery shopping trends from panic-buying to delivery and pickup options, all with disrupted supply chains. Grocery retailers are responding differently. Some are cutting back on the number of products they sell, some are skipping distributors and contacting product manufacturers directly, and some are giving their brick-and-mortar stores a complete digital overhaul. No matter which line of action your company has selected, AI retail solutions will enhance your operations significantly. Stephen Hawking predicted that artificial intelligence will transform every aspect of our lives. Grocery shops are no exception. The global AI in retail market is expected to reach $24 billion by 2027, rising from $3 billion in 2020 and displaying a year-over-year growth rate of 29.7%. According to Statista, the consumer goods and retail sectors have been using AI throughout 2020 for various purposes:

Statista

If you want to follow the supermarket industry trends and take your existing retail software solutions to the next level with AI, this article will show you some compelling use cases, warn you of possible implementation challenges, and share five tips to help you through deployment.

Physical stores adopting AI to enhance the shopping experience and become more efficient

McKinsey & Company found that even during the pandemic 85% of the US grocery sales still occurred in physical stores. It appears that brick-and-mortar stores are part of the future of grocery shopping. People still like to see, touch, and smell goods. Remember how you feel when you look at colorful packages of sweets or smell freshly baked pastries. Fabio Parasecoli, a professor at NYU’s Department of Nutrition and Food Studies, says that this kind of experience “gives you an idea of choice, abundance, quality, and pleasure.”

Contrary to popular assumptions, physical stores will not be replaced by their online alternatives. But they will rely on AI to tackle the pandemic-induced challenges.

However, this doesn’t mean that grocers are expected to go all in with technology and leave their less technical clients behind. Walmart is an excellent example of a grocery store that does not aim for tech-savvy buyers only but is willing to improve the shopping experience of the average customer. The chain experimented with different technologies to find what matches customer comfort levels. With this learning approach, Walmart discovered that using cameras and real-time data helped them increase overall shelf-stocking efficiency and sales in the meat aisle by 90% and 30%, respectively.

5 ways AI is shaping the future of supermarkets and grocery stores

1. Sparking consumer interest through personalization

Personalized promotions

A few years ago, promotions were advertised in catalogs or through broadcasts. Both options were rather expensive and displayed the same information for all consumers coming to the store. In supermarkets of the future, AI and advanced analytics offer plenty of information on every individual buyer, such as their meal preferences, food allergies, and motives behind their purchases. By employing AI in grocery personalization, retailers gain extensive knowledge of who is walking down their aisles. This approach enables retailers to craft customized promotions to attract buyers and increase sales. Attesting to the capabilities of AI is Gary Hawkins, the CEO of the Center for Advanced Retail and Technology based in Los Angeles: “AI technology can go incredibly deep and continues to learn over time, so it gets better at knowing which items to promote at what price. This will likely lead to different prices for different people, thanks to offers they are sent.” For example, Woolworth in Australia employs supermarket technology of the future to customize its marketing emails considering not only consumers’ taste but also their past shopping behavior. The chain can predict which items every shopper is likely to run out of based on their previous purchases and suggests these items as well. Another example of customization comes from Kroger, an American retail company. When consumers activate the store’s mobile app while inside, sensors detect them and send a personalized selection of items together with the prices via their preferred communication channel (e.g., video, voice, text).

In-store navigation

Effortless, time-saving navigation is a part of supermarkets of the future — especially if we’re talking about spacious retail facilities like the Mall of America, which incorporates over 520 stores and 60 restaurants. It can be daunting to move around such a space. To relieve its customers, the Mall of America deploys location-based AI chatbots, which operate through Facebook or a mobile app and assist customers in finding products and services.

Dynamic pricing

The dynamic pricing concept revolves around using machine learning and artificial intelligence in shopping to determine the best pricing strategy for different products. For this, algorithms analyze data from different sources, such as historical sales, competitor prices, stock levels, and special occasions. One of the tactics of dynamic pricing is cross selling a discounted item (e.g., buns) with a complimentary product (hot dogs) at a full price. This strategy helps reduce food waste by lowering the prices of goods nearing their expiration date.

2. Improving inventory management

Grocery store robots

Supermarkets of the future increasingly adopt robots to tackle inventory-related problems, such as preventing out-of-stock items, incorrect labeling, and pricing. California-based Fellow Robots developed an autonomous retail robot, which can scan your store shelves as high as 2.4 m above the floor daily taking high-definition pictures of products and their prices. Machine learning algorithms examine these pictures, searching for misplaced products and price discrepancies. Store employees can view the results through dedicated dashboards and take immediate action. Additionally, robots can help futuristic grocery store employees cope with the increasing demand for deliveries. Ocado, a UK-based online supermarket, reported a ten times increase in demand since the lockdown in March 2020. The company is using robots to scan the inventory and pick the right items for each delivery.

AI-based forecasting in grocery stores

Another grocery shopping trend is using AI-powered forecasting systems. AI algorithms do not purely depend on the historical data available at stores. They can self-learn and create forecasts even when the data is limited — for example when introducing a new product or testing a new promotion technique. Some older forecasting techniques work on products as clusters and can’t take the local dynamics into account. A good AI model can make predictions about products at a granular level considering local and regional trends.

3. Reducing theft

According to a National Retail Federation survey, retailers lost $62 billion in 2019 due to theft, which is almost $10 billion increase from 2018. AI-powered supermarket technology can detect inappropriate behavior among both buyers and cashiers.

Shoplifting

Incorporating computer vision in shopping can help store security managers identify theft attempts. Sainsbury’s, a UK-based supermarket chain, employed ThirdEye’s concealment detector, which uses computer vision to detect shoppers who take an item and place it in their pocket. The system records suspicious activities and notifies security personnel. Thanks to this technology, Sainsbury’s stopped almost 6,000 theft attempts between September 2019 and March 2020.

Sweethearting

Sweethearting is another form of theft when cashiers fake the act of scanning items at checkout in favor of consumers. Smart store technology can help spot such behavior. Computer vision systems, such as ScanItAll, use ceiling-mounted video cameras to “watch” cashiers and detect sweethearting events, like covering barcodes and stacking items on top of each other. The US-based supermarket chain Piggly Wiggly reported a loss of almost $10,000 per month due to checkout shrinkage at one of its locations. After installing ScanItAll and retraining cashiers, shrinkage costs declined to $1,000.

4. Improving social distancing

As social distancing is here to stay, supermarkets of the future are looking for ways to control the number of people indoors.

Trackers monitoring social distancing

In-store customer tracking is a grocery store technology of the future that can prevent overcrowding inside your store. A German supermarket company Aldi is using AI in shopping through an automated traffic light system, which controls people flow to its stores. When the number of shoppers in a particular location is below a predefined threshold, the light is green, and the door is open for others to come in. After the threshold is reached, the light turns red and the door closes.

Robots replacing human employees in grocery stores

COVID-19 speeds up grocery store robots’ deployment. Big supermarkets see robots as an opportunity to reduce the number of human workers, which makes consumers perceive the store as a safer place. For example, Walmart uses robots to wipe the floor so that buyers will not be surprised by a human worker disregarding social distancing to clean up a stain under consumers’ feet. A common complaint among warehouse workers is that they cannot maintain distancing given their job conditions. AI-enabled supermarkets can incorporate robots in such an environment to reduce the friction among human employees.

5. Improving the checkout process

Statistically, people spend about 60 hours annually waiting in checkout lines.

“In response to the COVID-19 pandemic, the demand for autonomous checkout technology is driving grocers and retailers to innovate and adopt new technologies that keep shoppers safe and streamline checkout.”

- Lindon Gao, Co-founder and CEO of Caper AI, a software company specializing in AI-powered solutions for retail and grocers. Grocery stores equipped with AI have the tools to correctly identify all the items collected by a particular consumer and charge that person’s bank card upon exit without any intervention from store employees. If you don’t want to let go of your staff or deprive your customers of human interaction, you can mirror what Walmart is doing. Cashiers at this AI store assume a new role of a “Host”. The host’s responsibility is to make sure all consumers are treated in their preferred way. If they favor self-checkout, a host will guide them to an open register. If they like the traditional checkout method, a host will pack their items and process the payment.

AI for smart shopping carts

Caper AI produced a shopping cart that uses artificial intelligence to instantly recognize items and measure their weight (if relevant). The cart contains a built-in navigation and product location system, helping customers to traverse the store. It displays available promotions and can compose a list of recommendations. Caper cart also incorporates a credit card reader, enabling consumers to checkout without cashiers. Caper AI has already partnered with the US-based Kroger and Foodcellar & Co, and Canadian Sobeys Inc.

AI-powered cameras for self-checkout

Supermarkets of the future use cameras and sensors to support checkout-free shopping. For example, Amazon Go has 26 cashier-free locations equipped with hundreds of cameras and computer vision algorithms to monitor which products every shopper picks to charge the total amount off their credit card immediately after they leave the store. Additionally, store owners can use AI and video to facilitate grocery checkout. Ireland-based Everseen developed a visual platform where AI watches videos of customers performing self-checkout in real time. The program can identify errors and correct users immediately. For instance, if a customer encounters an item that doesn’t scan properly at the self-checkout AI kiosk, Everseen’s system will register this incident and notify one of the employees to assist the troubled client. Kroger began deploying this system at its stores in 2020.

Smart shelves technology

An intelligent shelf is another application of AI in shopping that facilitates the checkout process. Such a system typically includes computer vision and sensors mounted on shelves. This technology can identify shoppers picking items from a shelf or returning them. It correctly matches buyers and products and charges consumers for their purchase at the end of the shopping journey.

This grocery shopping technology has additional applications. It can display relevant promotions as consumers pass by, monitor stock levels, and notify employees when the store runs out of some items, and detect the presence of ethylene gas, which is released when fresh items start to spoil.

Autonomous self-driving stores

The idea of self-checkout was appealing before the pandemic. A retail startup Wheelys designed a prototype of another grocery technology — a self-driving grocery store with no employees. The store would move autonomously from one location to another. Consumers would enter the shop through a sliding glass door using the Wheelys’ app. They would pick their items, scan them through the app, pack, and exit the store. Their bank card would be charged automatically.

Barriers to adopting AI in grocery shopping

Independent of the industry, implementing AI is a challenge. According to IDC’s 2019 Global AI Survey of 161 retailers, the major barriers to adopting AI in retail include:

High cost of AI solutions: costs of developing AI systems vary greatly depending on the software type, its intelligence level, the quality and quantity of data that you want your program to process, and the accuracy of algorithmic predictions. To implement an AI application from scratch, you can easily spend $50,000 on a basic version.
Lack of skilled personnel: The IDC’s authors interpret it in two ways: there is either a general lack of credible AI talent on the labor market, or the retail sector (including grocers) is struggling to attract AI talent.
Data-related trust issues: Artificial intelligence in shopping applications heavily depends on data, while grocers need to show transparency in data usage to win consumer loyalty. If AI algorithms abuse client data, the relationship will be damaged beyond repair. According to a recent survey by Deloitte, 70% of responding consumers agreed to share their data with grocery stores. This survey demonstrates that the grocery sector comes third after hospitals and governmental institutions if we look at people’s willingness to share their private data. This shows that consumers trust grocers, but there will be dire consequences if this trust is breached.
Unclear business objectives: AI demands a considerable investment upfront, while it is difficult to find compelling use cases to present to investors. Udai Chilamkurthi, the Chief Architect of Technology Strategy & Architecture at Sainsbury’s confirmed this challenge at the AI Summit London saying: “I haven’t seen a sensible business case that makes sense yet. The technology itself isn’t mature and comes with its own problems.”

How supermarkets of the future can succeed in AI adoption

Despite numerous challenges, the future of AI in grocery shopping looks bright. Here are the five steps that will help you painlessly employ AI at your store and reap the benefits faster.

Define your vision for the role you expect this technology to play in your organization and the returns on investment. Deloitte recommends adopting the following paradigm: Start small, scale fast, and build iteratively.
Find the right talent to fill different expert roles. Throwing data scientists in a data lake and expecting them to present new strategies will not yield results. You will need to recruit diverse internal and external talent who can derive, transform, and sustain value in the long term.
Adapt the organization’s culture to develop the right attitude towards AI. Many employees still view AI as an intimidating black box, which they fear and cannot understand. Make it clear that AI is there mainly to support humans and help them make informed decisions.
Clean up your data. The amount of data that AI can use is constantly increasing as grocers gain access to information coming in video format, from social media, and via geo-location apps and devices. Despite the data growing in complexity, many retailers overlook the need for a clear data strategy. Brian Kilcourse, Managing Partner at Retail Systems Research emphasizes the importance of data in a successful AI adoption by saying:

"The top challenge is dirty data (it's the elephant in the room). Models are only as good as the data that creates them. One recent study estimated that over 80% of the effort of implementing AI relates to data cleansing.”

Gain an ecosystem advantage. Artificial intelligence capabilities for grocery stores are evolving fast, and working with an ecosystem partner will give you access to the right team to get the job done. For example, a grocery retailer can join forces with predictive analytics and demand-sensing company.

Final thoughts

Supermarkets of the future are not necessarily online stores. Implementing AI in grocery shopping enables you to overcome all the pandemic-related hurdles while still letting your customers enjoy the sight, smell, and feel of products. However, incorporating AI is not a one-time task. It is a long process that requires changes of a store’s internal processes and culture. As Sanjeev Sularia, the CEO of Intelligence Node, puts it: “Retail organizations often get deterred by the costs of building the infrastructure and data processing capabilities needed for AI adoption. However, flexible businesses have successfully integrated AI across all business functions and upskilled their people to efficiently reorient to a data-driven mindset without trying to build everything from scratch.”

Thinking to enhance your grocery store with AI capabilities? Feel free to contact ITRex retail and AI experts, and they will make sure your supermarket is well-equipped to face challenges of the future.

The big promise AI holds for mental health

Anastasiia Bernatska — Thu, 06 May 2021 10:45:17 +0000

The future is already here – it's just not very evenly distributed,” ― William Gibson.

Artificial intelligence is creeping into mental healthcare. “How does it make you feel to hear that?” is the question that you might be already discussing with an AI bot. Even more, your AI therapist might be quite successful at helping you overcome any feelings of worry about a direction our future can take with the rise of AI. If looking beyond the scary headlines about Skynet coming true, an increasing use of AI in mental healthcare is actually great news for many of us. Today, we are experiencing a severe mental health crisis aggravated by the COVID-19 pandemic. Called the plague of modernity, mental health disorders affect one-fifths of people in the US, with 40% of Americans developing symptoms of anxiety or depression or both last year due to the fear of illness, self-isolation, grief, or deepening financial concerns. The landscape around this crisis is no less daunting. Its longtime hallmarks have been the societal stigma around mental illness and an acute shortage of mental health professionals. AI is already disrupting major industries, from healthcare to education, addressing challenges and opening thrilling opportunities. Mental health may well be the next candidate. Thanks to rapid technological advances, AI solutions for healthcare have all the potential to spearhead a positive change in this space that has been long ripe for innovation.

Mental health stats ― The crisis we are in

Mental health disorders are among the leading causes of morbidity and mortality, expected to cost the world’s economy some $16 trillion by 2030. They affect at least 10% of the population, with up to 20% of children and adolescents suffering from some type of mental disorder and women more likely than men to be diagnosed with depression. In the US, around 19% of people experienced a mental illness in 2017-18, which was a year-on-year increase of 1.5 million people. The number of the young reporting mental distress is rising more significantly. No one seems to know exactly why depression and anxiety are so common nowadays. Many experts even dismiss any upswing, arguing that what we see is a surge of people actively seeking treatment. Indeed, the number of people applying for help with depression or anxiety in the US has soared, according to the 2021 State of Mental Health in America report. As many as 315,220 people took anxiety tests in January to September 2020, 93% up from the entire 2019. The number of people taking depression screens increased by 62% to 534,784 people. At the same time, only around 40% of US adults with mental illness received treatment in 2019. One of the reasons is a shortage of mental health providers, which the US National Council for Behavioral Health predicts can reach over 15,000 clinicians in the next couple of years. The need for solutions that can scale access to mental health treatment is desperate, while such solutions are already here, thanks to AI.

Can AI help with mental health and how?

Unlike medical areas such as radiology or pathology where AI can outperform doctors, mental healthcare is thought by many as an exclusively human field where emotional intelligence is essential. Most mental health practitioners doubt that artificial intelligence solutions for mental health will ever be able to provide emphatic care. While clinicians may be overwhelmingly skeptical about the use of AI in mental health care, people emotionally connect to technology because they tend to treat robots as living beings, researchers say. We are not talking here about the unsettling intimate bond developed between a lonely man and an AI operating system in the movie Her, but rather about people’s willingness to pour their hearts out anonymously to an AI bot companion, as found out by creators of Eliza, one of the first chatbots designed in the 1960s. Moreover, people do prefer to talk to robots. Only 18% would favor humans over technology to discuss their mental health struggles compared to 68% who would choose robots, believing that robots don’t judge, are unbiased, and can provide an instant answer to a health-related question, according to a recent study conducted by Oracle and Workplace Intelligence. Just as important, talking to technology helps. Multiple meta-analyses have confirmed that computer-aided cognitive behavioral therapy (CBT) delivered via desktop or mobile apps is equivalent to or even more effective than standard CBT. The National Institute for Health and Clinical Excellence (NICE) in England first recommended computerized CBT packages for depression, panic, and phobias back in 2006 on the grounds of clinical and cost effectiveness.” In the US, the COVID-19 pandemic has prompted the Food and Drug Administration (FDA) to relax policies for a broader use of digital therapeutic tools to treat mental conditions. Particularly important to mental health care are the following AI technologies:

Machine learning (ML) and deep learning (DL) that provide greater accuracy in diagnosing mental health conditions and predicting patient outcomes
Natural language processing (NLP) for speech recognition and text analysis that is used for simulating human conversations via chatbot computer programs, as well as for creating and understanding clinical documentation
Computer vision for imaging data analysis and understanding non-verbal cues, such as facial expression, gestures, eye gaze, or voice tenseness

Examples of how AI is already revolutionizing mental healthcare

Currently, the applications of artificial intelligence in mental healthcare revolve around:

Analyzing patient data to assess the probability of developing mental health conditions, classify disorders, and suggest optimum treatment plans The data subject to analysis may include electronic health records (alongside blood tests and brain images), questionnaires, voice recordings, and even information sourced from a patient’s social media accounts. Data scientists employ a variety of techniques, such as supervised machine learning and natural language processing, to parse patient data and flag mental and physical states — i.e., pain, boredom, mind-wandering, stress, or suicidal thoughts — associated with particular mental health conditions. A group of researchers from IBM and University of California have recently analyzed 28 studies exploring the use of artificial intelligence in mental health and arrived at a conclusion that, depending on the choice of an AI technique and quality of training data, algorithms manage to detect an array of mental illnesses with 63-92% accuracy.
Conducting self-assessment and therapy sessions This category is largely represented by keyword-triggered and NLP chatbots that help patients evaluate the progression and severity of a mental illness and cope with its symptoms —either on their own or with the help of a certified psychiatrist waiting on the other end of the virtual line. An example of this would be Ellie, a digital avatar that was initially designed to help war veterans struggling with depression and PTSD. The AI therapist not only understands words but can also interpret non-verbal signs, such as facial expression, posture, or gestures to comprehend a patient’s emotional state and choose the right words to alleviate stress and anxiety. AI-powered diagnostic solutions may also work in tandem with wearable devices that measure heart rate, blood pressure, oxygen levels, and other vital signs that indicate changes in a person’s physical and mental well-being. One of such solutions is BioBase, a mental health app that leverages AI to interpret sensor data coming from a wearable. Designed to help companies prevent employee burnout, the mental health tracker reportedly helps reduce the length and number of sick days by up to 31%.
Making psychological interventions by automatically giving appropriate information to the patient Often deployed as part of a hospital’s digital patient engagement strategy, solutions like these help patients navigate a mental health crisis by providing relevant information and friendly assistance. An international team of scholars achieved impressive results with Tess, an artificial intelligence chatbot that delivers highly personalized psychotherapy based on CBT and other clinically proven methods, along with psychoeducation and health-related reminders. The interventions are delivered via text message conversation, meaning that emotion identification relies solely on language processing. The researchers tested the chatbot among a group of students to find out that the individuals who conversed with Tess daily over a period of two weeks displayed a significant reduction in mental health symptoms compared to participants who had sessions less frequently. Although Tess cannot possibly fill the shoes of a trained psychiatrist, the chatbot proves to be a viable alternative to human specialists — not the least thanks to its ability to tailor content based on a patient’s diagnosis and demographic data.
Equipping therapists with technology to automate workflows, monitor the treatment process, and improve medication adherence Due to the very nature of mental health conditions, psychiatrists can seldom rely on legacy tech tools or other physicians’ advice when interpreting medical data and devising treatment plans for patients. One way to lessen the administrative burden could be the implementation of AI-driven mental health platforms that automatically retrieve information from miscellaneous IT systems within a hospital and generate on-demand reports about every single patient’s progress, current condition, and possible outcomes. An early example of such systems is OPTT, an AI platform that provides a rich selection of tools for mental health professionals looking to increase the capacity of their clinic. Preliminary research indicates that OPTT could improve access to quality mental healthcare by up to 400%.

Outside the professional psychiatry realm, there are a plethora of AI-powered mental health apps like Woebot, Replika, and Elomia on the App Store and Google Play that target individuals with mild mental health disorders and extend psychiatric and psychological care beyond hospital doors.

Benefits of using AI in mental health treatment

The current success of artificial intelligence apps and platforms for mental health care can be attributed to the following benefits:

Affordability. Unlike traditional counseling where you need to schedule and travel for appointments, AI-based and other mental health apps allow users to access therapeutic help anywhere, anytime. Moreover, they provide help at little or no cost, compared to in-person therapy rates, missed work, and the need to make other arrangements and commute.
Accessibility. AI-based apps remove such barriers to mental health treatment as staff shortages across the board and a lack of providers in rural and remote areas. This is important, since more than 100 million people in the US live in so-called Health Care Professional Shortage Areas. Location-agnostic AI chatbots and platforms can see you whenever you need and spend as much time with you as you need.
Efficiency. Artificial intelligence algorithms used in mental healthcare have already been proven to be successful in detecting symptoms of depression, PTSD, and other conditions by analyzing behavioral signals. Other studies have shown that algorithms make more accurate assumptions than clinicians to distinguish between genuine and fake suicide notes and are 100% accurate at predicting who among at-risk teens are likely to develop psychosis. They also help patients struggling with mental distress: a randomized controlled trial conducted by Woebot researchers has revealed that participants experienced a substantial decrease in depression and anxiety after just two weeks of using the app.
Privacy and ease to open up. AI-based therapists make people feel less self-restrained to share embarrassing things. This is especially important for those who can feel shame in face-to-face interactions because of stigma or fear of being judged. Actually, almost a quarter of people lie to doctors, with the most hushed topics being smoking, drinking habits, and sexual activity. For many, it’s easier to admit the true extent of their behavior to a robot because the robot won’t judge.
Support for therapists. “AI could be an effective way for clinicians to make the best the time they have with patients,” says Peter Foltz, a research professor at the University of Colorado Boulder. This is because AI can track and analyze substantial amounts of data faster and even more efficiently than any human. As a result, algorithms help with more accurate diagnoses. They can also spot early signs of trouble by monitoring the patient’s mood and behavior and alert clinicians so that they can quickly adjust treatment plans. This can be lifesaving for suicidal patients who need regular check-ins.

AI trends in mental health

According to a recent report, funding in digital mental health increased from $1,136 million year on year to $1,974 million globally in 2020, with mental health seeing an all-time record number of mentions in the news in March 2021. AI-powered mental healthcare also sees record levels of venture capital funding. Amid the crisis, researchers have made a few big steps forward in their artificial intelligence projects in mental health that may be a hint of what lies ahead for us in the very near future. We are likely to see the emergence of more AI therapists like Ellie and some major developments driven by AI prediction and detection capabilities.

For instance, researchers at Vanderbilt University Medical Center in Tennessee, US, have developed an ML algorithm that uses a person’s hospital admission data, including age, gender, and past medical diagnoses, to make an 80% accurate prediction of whether this individual is likely to take their own life.

University of Florida researchers are about to test their new AI platform aimed at making accurate diagnosis in patients with early Parkinson’s disease.

Another team of researchers are working on an AI tool to predict schizophrenia based on analysis of brain scans. Research is also underway to develop a tool combining explainable AI and deep learning to prescribe personalized treatment plans for children with schizophrenia. So, it looks like we have the most promising technologies today to overcome the crisis and transform the delivery of mental healthcare.

A final note

AI holds both incredible promises and many next-level complexities, including an array of ethical pitfalls. Like with many healthcare apps, innovators may need to be compliant with the GDPR, HIPAA, and other industry-specific guidelines. But there’s much more to that with artificial intelligence. Creators of AI mental healthcare technology should be wary of AI challenges, including bias that may come with insufficient and poor quality databases, increased risk to patient privacy and data security, a lack of guidance on development of AI tools, calls for greater transparency over the use of algorithms and their decision-making logic, complexities with integration into clinical practice, and the need to train mental health professionals in emerging technologies. Scaling access to mental healthcare is critical, but it should be scaled ethically not to lead to increased risk to patients.

If you are interested in learning more about AI opportunities and challenges in mental healthcare or want to innovate in this space, contact our AI experts.

How much does artificial intelligence cost? Well, it depends

Anastasiia Bernatska — Fri, 30 Apr 2021 13:55:53 +0000

How much does it cost to create a custom artificial intelligence system? The honest answer would be “it depends”, as the price of developing, implementing, and maintaining custom artificial intelligence systems is driven by a number of factors and can only be evaluated on a case-by-case basis. In this article, however, we’ll figure out what these factors are and provide ballpark estimates of several AI-based solutions from our portfolio. Additionally, we’ll give you several tips on how to approach your first artificial intelligence project and get the most value out of your AI investments.

These are the top 5 factors behind AI cost

1. The type of software you’re eyeing to build. Artificial intelligence is an umbrella term that refers to any device or application that makes decisions based on the information it consumes, thus mimicking human intelligence. Voice assistants that understand questions uttered in natural language, security cameras recognizing people in live video footage, and expert systems that spot cancerous tumors in CT scans can all be described as artificial intelligence. However, their complexity, performance requirements, and, subsequently, costs vary greatly

2. The level of intelligence you’re aiming for. When talking about AI, people tend to envision Boston Dynamics robots and holographic avatars from Blade Runner 2049. In reality, most business AI solutions can be described as narrow artificial intelligence, meaning they’re only programmed to perform a particular task — for example, recognize text in PDF files and convert them into editable documents. To classify as truly intelligent, AI algorithms should be able to uncover patterns in data with little to no human intervention, assess the probability or improbability of an event, justify their assumptions, continuously process new data, and learn from it.

3. The amount and quality of data you’re going to feed your system. Artificial intelligence is only as good as the data it’s been trained on, and the more data algorithms consume, the better they get.

AI can ingest both structured data, which is properly organized and stored in relational database management systems (RDBMs), and unstructured data like emails, images, and videos, which is typically bulk-uploaded to data lakes.

As far as AI cost is concerned, it is cheaper to work with structured data — especially if there is a substantial quantity of information to boost your algorithms’ accuracy. With unstructured data, AI experts have to go the extra mile to organize and label it, while software engineers need to set up the complete infrastructure ensuring continuous data flow between the components of your system. In some cases, such as training AI-powered medical imaging solutions, data can be hard to obtain due to privacy or security reasons.

To overcome this hurdle, AI engineers may artificially expand the size of a limited dataset or reuse existing classification algorithms. Operations like these are bound to eventually increase the cost of building an AI program.

4. The algorithm accuracy you’re hoping to achieve. The accuracy of your AI solution and its predictions depends directly on the type of application and the requirements you impose on it. A customer support chatbot, for example, is only expected to handle up to 60% of routine user queries; for complex issues, there’s always a human specialist waiting on the other end of the line. A pilotless delivery drone that transports blood and human organs, on the other hand, should be able to maneuver around objects with immaculate precision. Higher accuracy and reliability of artificial intelligence predictions directly affect your project’s lifespan and increases AI development cost. Also, it should be noted that AI algorithms will continue to absorb new data as they work alongside human specialists, which may carry additional training and maintenance expenses.

5. The complexity of an AI solution you’re working on. Artificial intelligence is the brain of a technology system that pushes data in and out of your business app and presents insights to users — including those who don’t have a technical background. When we discuss the cost of artificial intelligence, we should be talking about the price of creating proper software, with a cloud-driven back end, ETL/streaming tools, APIs supporting integration with internal and external systems, and some kind of interface, be it a cloud dashboard, mobile app, or voice assistant. Lightweight AI, like the customer support chatbots mentioned in the previous section, may live inside a corporate messenger and does not require a complex infrastructure to function. AI-powered data ecosystems providing a 360-degree view into your company’s operations are a whole different story. Additional AI implementation challenges will arise when you start scaling your intelligent system from one or several use cases (think predicting customer churn rate or analyzing sales data in a particular brick-and-mortar store) to a company-wide deployment. In fact, that’s the reason why only 53% of enterprise AI projects make it from prototypes to production.

Speaking of failures, it should be noted that only a tiny fraction of AI projects (Gartner believes it’s 20%; VentureBeat is even less optimistic) eventually deliver on their promise. A staggering failure rate can be attributed to several factors, including a lack of collaboration between data scientists and software engineers, limited or low-quality training data, and the absence of a company-wide data strategy. Most often, however, failed AI projects are characterized as “moon shots” — i.e., over-ambitious endeavors led by starry-eyed data scientists and CIOs seeking to “completely change the way our company has been operating for decades.” Such projects may take forever to complete, and it’s only natural that, at some point, a company’s C-Suite stops pouring money into the bottomless pit without seeing a glimpse of real value.

How much does AI cost? These examples from the ITRex portfolio might give you a hintProject

1: AI-powered telemedicine solution

A healthcare technology company approached ITRex to upgrade an existing telehealth system with video recording capabilities, which is implemented in multiple hospitals across the USA. The new version of the system would allow healthcare providers to apply facial recognition and natural language processing technologies to analyze videos filmed during consultations and potentially improve doctor-patient interactions. During the discovery phase, we ruled out possible technology barriers and selected the optimum tools for the project — primarily Python and the accompanying frameworks and SDKs for speech recognition and analysis. For the pilot version of the telemedicine system, the client selected the speech-to-text functionality only, with no user-facing components expected to ship. The solution conducts linguistic analysis of video recordings to detect possible changes in the communication style that could shed light on patients’ well-being and help physicians come up with better treatment plans. A basic version of a video/speech analysis AI platform may cost $36–56 thousand.

Project 2: An intelligent recommendation engine

A technopreneur was looking to add AI capabilities to a B2C platform connecting users with local service providers. Our client’s idea revolved around replacing cumbersome search filters with advanced machine learning algorithms that would analyze input text and come up with a list of service providers that match a user’s query. We selected Amazon Personalize as the primary technology stack for the AI part of the project. In addition to providing personalized recommendations based on user inquiries, the solution comes along with a fully managed cloud infrastructure for training, deploying, and hosting ML models. The system’s back end would be written in Python, while user data would be securely stored in the cloud (Amazon S3). The development, testing, and deployment of a similar artificial intelligence platform (MVP) would cost you anything between $20 thousand and $35 thousand.

Project 3: An AI-driven art generator

A renowned visual artist turned to ITRex to create an AI solution that would generate new paintings based on his works and the works of other authors who inspire him. The client was looking to build a minimum viable product (MVP) version of the system over the course of several weeks to present it at an exhibition. The ITRex team proposed creating a neural network based on Python frameworks (PyTorch, TensorFlow) that would process abstract paintings, learn the artist’s trademark style, produce similar pictures, and display them on the artist’s official website. For the MVP version, we suggested using the Instagram-like 1000 x 1000 image resolution and deploy the AI solution locally, leaving an option to port the system to the cloud in the future. Depending on the type of training data (e.g., abstract vs. figurative art), image resolution (HD vs. low-resolution output images), and deployment approach, the cost of building an MVP version of an artificial intelligence system like this could reach $19–34 thousand.

How to reduce AI costs — and start benefiting from artificial intelligence ASAP

A recent article published by the Forbes Technology Council suggests that building and deploying an AI solution will ultimately cost your company 15 times more than you planned originally — unless you already have an efficiently built data ecosystem in place. Larger AI development costs typically stem from significant infrastructure optimization, data integration, security, and artificial intelligence management and control efforts. However, you can minimize these expenses by thoroughly planning your project and starting small while having a bigger picture in the corner of your mind.

To help you build an artificial intelligence system at a lower cost and start reaping its benefits from day one, the ITRex team prepared a high-level AI development and implementation guide.

Its general idea revolves around taking an Agile approach as it might be hard to elicit all the requirements for a custom AI solution at the beginning of your journey. Another advantage of this approach is that you start seeing a sizable ROI early on; this, in turn, helps get buy-in from your company’s C-Suite and secure further funding.

Here’s how you should approach your pilot project:

Collect stakeholder feedback. Before you start building an AI system, we recommend that you talk to internal and external stakeholders to determine the key process and decision flows that can be augmented or automated with AI’s help.
Identify priority use cases. In this step, you should use a product prioritization framework (e.g., MoSCoW, RICE, or Kano) to select business cases that will drive the most value during the interim period and serve as a basis for further AI implementations.
Select the optimum technology stack. We recommend that you utilize a combination of custom-made, open-source, and off-the-shelf components (e.g., plug-and-play facial recognition engines, API-driven voice assistants, and cloud-based services supporting the creation and training of AI algorithms) to create a vendor-agnostic solution and reduce overall AI development cost. Special attention should be paid to UI design: your future AI system should incorporate a user-friendly interface that enables stakeholders to ask artificial intelligence questions, get instant insights, or automate tasks without seeking assistance from your IT department.
Prepare data subject to AI-driven analysis. To help algorithms make sense of your business data, it is essential to gather information, assess its quantity and quality, and bring it into a unified format. For this, a number of data collection, preparation, and normalization techniques can be applied.
Create an MVP version of your AI system. Starting with a minimum viable product supporting the essential use cases is one of AI development best practices. With an MVP at your hands, you’ll be able to check the feasibility of your concept, pinpoint areas for algorithm improvement, and start scaling the system across different use cases and departments.
Treat AI implementation as a work in progress. Once you put artificial intelligence to work, you may not get perfect results right from the onset; as your AI system consumes new information under the supervision of human specialists, it will deliver more accurate predictions and become more autonomous. It is therefore important to continue gathering feedback from your company’s stakeholders, making the necessary changes to the system, and repeating the steps enumerated above when introducing new features and use cases.

In the end, how much does artificial intelligence cost?

Although it’s hard to estimate the cost of creating and implementing an artificial intelligence application without diving into your project’s details, you may easily spend $50 thousand on a very basic version of the system you’re looking to build. Is the game worth the candle? By 2030, artificial intelligence could contribute up to $15.7 trillion to the global economy, with increased productivity and automation driving the lion’s share of this sum. Currently, the AI revolution is still in its early stages. While some countries, industries, and companies might be better prepared for the disruption (meaning they do have the necessary data and IT infrastructure in place to create and deploy custom AI solutions at scale), the competitive advantage is elusive, since there is an opportunity for every business to transform the way they work and lead the AI race. And your company is no exception.

Wondering how much it would cost you to build a custom AI solution? Feel free to discuss your project with ITRex experts!

Retail technology trends: 13 applications promising survival for brick-and-mortar businesses

Anastasiia Bernatska — Thu, 29 Apr 2021 13:01:43 +0000

Summary: The impact the pandemic has had on brick-and-mortar retail is detrimental. In the US, 12,000 stores had to shut their doors. And those left wonder: What do we do to sustain our businesses? Below, we look at retail technology trends that can help brick-and-mortar stores survive the pandemic, reopen safely, and lure customers back.

2020 wasn’t easy on retailers. Although brick-and-mortar stores saw foot traffic go down even before the pandemic kicked in, lockdown measures have pushed more users online. For retailers with no omnichannel presence, this turned out sour. The hopes for more offline buyers coming back during the COVID-19 plunge in summer and early fall have crumbled, too. The hesitancy to enter shops potentially crowded with people has kept shoppers off-premises. And as much as 37% of them say they’d prefer to shop online even after the lockdown measures are lifted.

Considerable drops in demand (excluding for the essentials like groceries) have added fuel to the fire, making it extremely hard for brick-and-mortar chains, and especially smaller ones, to pull through. So, the legacy 2020’s left for retail looks pretty sad: a record of 12,000 stores had to shut their doors — and some for good. And those still standing face a challenge. How do they sustain their business, workforce, and revenue? And as we live off the expectations for a nationwide vaccination and a sense of normalcy, how do they reopen safely and lure customers back?

To provide the answers, we’ve summarized what retail technology trends can bring to the table. In short: implementing retail software solutions, businesses can make it safer, easier, and more enjoyable to shop. Now, let’s take a more detailed look at new retail technology that helps retailers stay afloat, engage with, and retain buyers.

Retail technology trends: 13 essential use cases

Techs for augmenting retail operations

Smart point-of-sale systems

More than 40% of retailers have already replaced or plan on replacing dated cash registers with smart POS systems. So, what are these, and how do they help retail? A smart POS is figuratively a “brainy” cash register. But along with cash management, modern POS solutions do much more — from storing customer databases and managing loyalty programs to tracking store inventory and monitoring staff productivity to generating analytics reports and supporting mobile payments. This feature-richness turns POS solutions into innovative retail technology tools that improve store management from every angle, namely:

Improve customer experience with lightning-speed checkouts, flexible payments, personalization of shopping experience, and loyalty reward programs
Optimize store management, giving a clear picture of a store’s inventory levels at all times
Help make right decisions providing useful analytics on which items are in high demand, which marketing campaigns are effective, which employees show high performance and more

Following the improvements above, smart POS solutions can save about 10% of revenue annually. And with relatively low adoption costs — businesses with a single register can expect an initial investment of around $1,250 and an additional $1,000/year of maintenance and credit card processing fees — smart POS solutions drive fast ROI. UK’s retailer M&S leverages the mobility of smart POS to the fullest. A handheld component of the POS allows customers with a small number of items to check out while queuing. The M&S staff uses the device for contactless payments, which makes shopping safer and way more convenient.

2. Retail data analytics

One of the hottest retail technology trends, AI-powered data analytics can help retailers make better marketing and procurement decisions. Take sales forecasting, for example. Analyzing past sales data for each SKU against seasonality, special events, marketing promotion campaigns, and other demand-influencing factors can help retailers align supply with demand, avoid stockouts and overstocking, anticipate staffing needs, and plan advertising campaigns. With customer sentiment analysis, you can dig in your customers’ feedback to capture what they feel about your products, service, and brand.

AI algorithms analyze an array of messages across social media and review sites to get insights about why shoppers feel negative about your customer service, which product features they love the most, and more. Innovative retail traffic analysis solutions can help you dive into customer behavior patterns and preferences to reveal peak times, draw-in rates, hot zones, average dwell time, and customer demographics. With this data, you can optimize store operations and create new sales opportunities.

The benefits of using AI solutions in retail are many. Deloitte reports that AI-powered retail analytics results in a minimum of:

10% higher customer satisfaction
16% higher success rate of strategic initiatives
49% more accurate decisions
40% increase in revenue 36 months post-adoption.

3. Inventory management

Inventory overstocks and stockouts are a real problem for retailers. In the US alone, retail businesses lose $224 billion each year because they have too much inventory and another $45 billion — because they don’t have enough. Using innovative retail technology for inventory management, retailers can overcome this issue. Here’s how. Marked with barcodes or RFID tags, inventory items can be tracked in real time as they move along the supply chain. So, retailers gain unprecedented visibility into the entirety of their stock across stores and warehouses and can replenish when needed. With inventory analytics, retailers can predict how much of each SKU they need on hand and distribute inventory items across multiple stores and storage facilities to better suit the demand. And when integrated with ERP, inventory management solutions can even be configured to automatically send replenishment requests to suppliers whenever the volume of a particular product is reaching a set threshold. The adopters of retail technology for inventory management software say they benefit from:

Increased customer satisfaction and loyalty
Increased profits
Better relationships with vendors and suppliers
Higher inventory accuracy

And as far as the cost of inventory management solutions goes, businesses spend an average of $100 per user for an entry-level system and up to $300 per user for more complex solutions with advanced features like AI-based analysis.

4. Smart kiosks

With smart self-service kiosks and self-checkout units, you can let your customers order and pay for goods without any help from the staff. So, retailers can prevent crowding while buyers enjoy safer and more convenient shopping without having to wait in lines. Also, modern self-service kiosks and units can be enriched with AI features, so you can, for instance, serve customers with tailored advertisements and special offers.

Along with improving buying experience, self-service kiosks drive such benefits as:

Creating new revenue opportunities by letting retailers reach more customers without having to invest in additional brick-and-mortar space
Increasing shop efficiency by automating operations like processing credit applications, managing gift registries, and selling gift cards
Improving job satisfaction for the employees as they wouldn’t have to handle as many customer requirements as before and could concentrate on providing personalized service and increasing sales.

The cost of implementing a self-checkout solution varies depending on the number of factors, including a solution’s type, featured integrations, and the availability of peripherals like credit card readers, barcode, or RFID scanners. For example, for a touchscreen kiosk with a bill validator and card reader, an average setup price would start at $11,000. And such retail technology trends as AI-powered apps and autonomous shopping carts take self-checkout a step further. Once a customer links their phone to a shopping cart via a mobile app, computer vision algorithms powering the cart recognize the products in it, so a buyer can pay via the app and leave the store without scanning.

5. Delivery systems

Fostered by the pandemic, the availability of delivery options has become a significant factor in the customer’s decision-making process. To attract customers with fast and reliable same-day or scheduled deliveries, retailers opt for new retail technology and implement direct store delivery systems. Competitive delivery solutions feature:

Live shipment tracking. You can track all of the logistic movements on a single dashboard, including the details about deliveries, drivers, vehicles, customers, and routes, and let your customers monitor the status of delivery online.
Route planning. Delivery software can help you optimize routes, so you can deliver bulk orders on time while engaging fewer drivers and reducing fuel spend.
Vehicle capacity optimization. You can make sure your vehicles are neither under- nor overutilized by optimizing the volume capacity for each car.

Driverless delivery is another innovative use case of retail technology. The industry moved from experimenting to adopting drone delivery a few years ago, with Amazon’s Prime Air as a pioneering solution. Today, drones are routed to deliver goods to areas with poor traffic infrastructure. The cost of implementing delivery software depends on whether you opt for a custom or a SaaS solution, the solution’s feature set, the size of your business, and the size of your fleet. The prices vary a lot, with the popular delivery software providers charging between $2 and $35 per driver.

Techs boosting in-store engagement

6. Mobile apps with loyalty and referral program features

Increasing customer retention rates by 5% can increase profits by 25% to 95% as loyal customers are 50% more eager to try new products and pay 31% more. So, it is worth investing in developing a loyalty program and delivering your repeat buyers special treatment. The simplest way to do it is to power your customer service with a customer loyalty app. Here’re a few examples of excellent customer loyalty systems to get inspired by.

Nike puts considerable effort into making their customers feel special, rewarding repeat buyers with exclusive products and free merchandise in exchange for loyalty points managed in an app. The brand also embraces personalization, from offering discounts for a customer’s birthday to handing gifts after completing a purchase to granting a free subscription to ClassPass.
H&M’s fully digital program enables their app users to collect loyalty points in exchange for rewards. For every dollar, customers receive a point. Once they have 200 points, they are entitled to an additional $5 off their next purchase. Repeat customers also get personalized offers and discounts, can use free online returns, participate in exclusive events, and have the option of deferred payment.
Sephora’s repeat customers, too, earn points that can be exchanged for rewards. Loyal customers can access exclusive previews and have an option of free shipping. Leveraging retail technology trends to the fullest, the brand also serves their repeat buyers with tailored product recommendations that match their beauty type and let them use a virtual makeover tool.

The outcomes of having a mobile loyalty program are promising. 77% of consumers say they are likely to stay with a brand with a loyalty program, and 63% of consumers say they’re ready to increase spending to maximize the benefits of a loyalty program.

7. Real-time marketing

For retailers who want to impress their customers with a frictionless shopping experience and reach them with timely, targeted messages, beacons might be worth attention.

One of the retail technology trends that is quickly gaining in popularity, beacon technology uses Bluetooth to transmit information to the customers’ mobile phones and enable real-time marketing. This way, placing beacons around brick-and-mortar stores, retailers can gather detailed information about customer movements to optimize their in-store experience and create highly personalized marketing campaigns.

Macy’s was one of the first chains to have adopted beacon technology in retail. When a Macy’s customer opens the brand’s mobile app, it registers their exact location. Say, the customer is in the beauty products section. The app can remind the customer of a particular product they have previously added to their wish list and help navigate to where the product is located, or prompt a purchase with a tailored offer. The retailers who use beacon technology say the outcomes they see are pretty compelling. The early adopters of this innovative retail technology could increase profits by nearly 9% with an ROI of 175%.

8. Digital signage solutions

Eight out of ten buyers say they have entered the store due to a digital sign catching their attention. But besides fostering the drop-in rate, placing digital signs at the shop windows and throughout the premises can influence customers’ buying decisions, ease navigation, and even improve staff communication. For example, placing a screen with the details about a clothing garment, the material it’s made of, and styling tips makes a difference to whether a shopper buys it or not.

Digital signage solutions provide entertainment, too. You can share everything from social media updates to news to videos, feeding in the retailtainment trend and extending your brand into the phygital space.

Burberry was one of the first brands to have embraced this retail technology trend. Their London flagship store features mirrors doubling as digital signage displays. At the same time, the staff use iPads and RFID-enabled screens that trigger related catwalk footage when products are taken into a fitting room.

Another example is Kroger’s digital price technology they’ve rolled out partnering with Microsoft. This retail tech allows digitally displaying products’ prices and nutrition information, and the staff can update the data instantly. The tech claims to impact retailers’ sustainability efforts, too, as it runs on renewable energy.

9. Augmented reality

Having become one of the crucial retail technology trends helping build consumer relationships and boost sales, augmented reality is now contributing to improved hygiene and safety as stores are reopening. More retailers are starting to offer AR-powered virtual try-on experiences that range from previewing furniture to trying on luxury fashion.

This retail technology provides a safe and convenient way of shopping for customers as they can dive into AR experiences in any location and via any medium, be it a customer’s PC, a mobile device, or an in-store kiosk. AR also helps drive conversion.

In fact, Shopify has recently stated that interactions with products having AR content show a 94% higher conversion rate than products without AR capabilities.

Zara introduced AR technology back in 2018. Stores visitors were prompted to use their phones as a lens. Holding the phone against a sensor made virtual models appear, showing pieces from the retailer’s latest collection and interacting with the space around them.

The AR experience gave shoppers a clearer idea of how the clothes fit and how they could be styled without venturing into a changing room. Another retailer to have taken on retail technology trends and implemented AR is Ulta.

The beauty brand uses this innovative retail technology to let customers test beauty products via their try-on beauty tool GLAMlab. During the pandemic, the tool has seen a surge in usage, with the engagement increasing seven-fold.

10. Conversational commerce

Another attention-worthy innovative retail technology, conversational commerce stands for interacting with customers via messaging apps or voice technology. With the help of chatbots or personal shopping assistants, customers can get support, ask questions, receive personalized recommendations, read product reviews, and do much more.

So, product research, price comparison, and even purchasing become just a few clicks or even words away for many customers. Walmart, has embraced this innovative retail technology and developed a voice ordering service. The customers can use Siri or any other voice assistant to add products to the cart. The products then get delivered by the chosen delivery method and paid for online.

Techs improving store and warehouse management

11. The Internet of Things

IoT continues to make its way into the retail sector with different sensors used for managing inventory, equipment, and utilities. Along with monitoring the location of inventory items along the supply chain with RFID tags, IoT sensors can fetch data on products’ temperature, humidity, and tilt, facilitating quality control. IoT solutions for retail help determine whether the products are transported in proper conditions, reducing product damage and loss.

According to Deloitte, 74% of companies that have implemented IoT for inventory monitoring saw their revenue increase by 4 to 10%. Intelligent lighting technology, smart thermostats, and connected water meters can be used to optimize and even automate utilities management.

Different types of sensors can be installed throughout stores and warehouses to automatically turn off lighting, lower room temperature when no one is around, and help save up resources. IoT also helps speed up order picking by quickly locating warehouse equipment like trucks and forklifts and automating operations with warehouse robots, including automated guided vehicles, automated storage and retrieval systems, and collaborative robots.

12. Security solutions

AI-powered CCTV cameras and other next-gen anti-theft solutions contribute to store safety. Placing CCTV cameras across the premises and analyzing the footage in real time with AI algorithms helps retailers detect suspicious and unsafe activities, like signs of flames and smoke, people lingering around employee entrances, or lying on the ground. Leveraging new retail technology, retailers can prevent theft, too.

By placing RFID tags or sensors on products or shopping carts, businesses can track their exact location and get notified if any damage takes place. In turn, biometric authentication and alarm systems prevent unauthorized access to high-security areas.

13. Ensuring social distancing

Technologies help keep customers and staff safe and ensure they follow COVID-19 guidelines, and provide for extended measures to prevent the spread of the virus.

Shop owners can turn to crowd monitoring with real-time video analytics to quickly detect bottlenecks and queues and communicate video and audio messages in and out of stores to inform buyers of the existing regulations. Indoor cameras with built-in video analytics also help detect customers with alleviated temperature.

Such thermal cameras measure human temperature at a distance of 2 to 4 meters, analyze the fetched data in real time and notify the store staff. Placing people counting sensors around the store, too, enables retailers to enforce physical distancing measures and tap into additional benefits, like more effective staff scheduling and reduced energy costs.

Self-checkout and checkout-free shopping solutions are of great help when it comes to ensuring epidemiologic safety. Self-checkout stations, mobile POS, and Amazon Go-like solutions that let customers pay via a mobile app as they walk out of the store help eliminate queueing.

The benefits retail technology trends hold for brick-and-mortar stores

The use cases above prove it once again — the future of retail is digital. A safe and convenient shopping journey now lies at the intersection of physical experiences and digital technology. The benefits of embracing innovative technology in retail are many; those deserving a mention include:

Operating with leaner staff. As of June 2020, there were 1.9 million store-based retail workers unemployed, according to the US Bureau of Labor Statistics. Analysts believe that the workforce could shrink further after the pandemic as retailers discover they can’t make payroll with the same headcount. Retail tech innovations can alleviate the labor shortage problem through task optimization and automation and contribute to higher employee productivity, helping retailers operate as efficiently with leaner staff.
Embracing omnichannel. In advanced economies, the market share of ecommerce and the expenses spent online have increased by 10% to 30% due to the pandemic, making omnichannel the key to the sector’s survival. Leveraging retail technology trends, businesses could combine in-store and online sales to cater to a larger pool of customers.
Optimizing expenses. By adopting a data-driven approach to store and warehouse management, improving purchasing, optimizing inventory spend, and automating day-to-day operations that previously required staff, through innovative retail technology, retailers can cut the cost of running their businesses.
Boosting sales. Shoppers are willing to pay more for stellar customer service. With sales analytics and in-store engagement technologies, you can attract and retain more customers while ensuring their safety.

So, what does the future of retail technology look like?

The retail sector is undergoing massive disruption. Retailers will be expected to turn to digital means and leverage retail technology trends to stay in line with new customer habits and provide engaging and experiential brick-and-mortar offerings.

As retail futurist Doug Stephens puts it, “Consumers become increasingly technologically entrenched, so they’ll crave for more and better physical retail experiences. And so brick-and-mortar spaces will be required to offer the opportunity to draw the consumer into the brand story, deliver a remarkable and immersive brand and product experience, and ultimately galvanize their relationship with consumers.”

If you’re willing to take on the path of transformation to weather the disruption, ITRex is eager to bring its experience in developing retail software to the table.

Types of patient engagement solutions and how much they cost

Anastasiia Bernatska — Mon, 26 Apr 2021 13:49:36 +0000

The healthcare sector has been slow in adopting technology. However, times are changing, and an increasing number of patients want to benefit from digitalization. According to a recent survey by Accenture, 60% of respondents prefer to use technology for patient-provider communication. Consequently, digital healthcare solutions are gaining popularity. Particularly, this change is reflected in the growth of the patient engagement market. The global patient engagement solutions market size was valued at $15.1 billion in 2020, and it is estimated to expand at a CAGR of 21.4% by 2028. The US patient engagement technology market, meanwhile, is expected to reach $9.4 billion by 2023.

Source

So, how software for patient engagement works? Which options do you have if you decide to deploy such a tool at your healthcare facility? And what is the cost of digital patient engagement solutions? Our latest article will shed light on these (and many more!) questions.

Introduction to patient engagement solutions

What is patient engagement, and why digitalize it?

Patient engagement is a partnership between a patient and their healthcare providers team where both sides share information and search for the best treatment options.

It is opposed to the conventional rigid system where the doctor prescribes one treatment course, and the patient feels free to either accept or ignore it instead of attempting to discuss other options. Nowadays, the healthcare sector is turning to technology to increase patient engagement in general and handle COVID-19 in particular. Using technology, doctors can reach their patients anytime and anywhere, foresee and mitigate deterioration in patients’ conditions, and provide helpful information at the right time. These goals can be achieved with the help of patient engagement software — an application or a set of interconnected systems designed to facilitate communication between patients and healthcare providers, offer educational resources, and otherwise manage the patient-doctor relationship.

Standard features supported in patient engagement tools

If you want to purchase/build a patient engagement solution for your hospital, here is a list of basic features your system can offer:

Registering new patients and managing their medical history online
Scheduling and rescheduling appointments
Sending automatic appointment reminders either via text or voice messages
Supporting patient education using customized content
Facilitating online billing and payments

Benefits of having a patient engagement system:

Minimizing no-shows with appointment reminders and effortless rescheduling
Establishing loyalty as patients fill in post-appointment surveys and doctors get the chance to adapt to individual patients’ needs
Attracting new patients as some of these solutions support marketing campaigns
Improving patient compliance as they feel involved in the decision-making process
Enhancing communication between patients and their healthcare providers

The hidden cost of not having digital patient engagement solutions in place

Poor patient engagement strategies have a drastic impact on hospitals and the healthcare sector in general — and that’s not to mention patients’ outcomes.

Here are some of the consequences of not keeping patients engaged:

Diminished patient flows

According to Puneet Maheshwari, the man behind the DocASAP patient access and engagement platforms, “Today’s consumers expect companies to anticipate their needs and tailor their messages accordingly, leading patients to expect the same level of personalization from their healthcare providers.” Maheshwari’s words are echoed by a recent survey, which claims that 75% of US consumers wish for a more customized experience when it comes to receiving healthcare services. If one clinic does not offer it, patients will find the one that does. Consequently, hospitals that ignore patient engagement might lose their patients. Furthermore, improving patient engagement has an economic agenda. Aaron Martin, Chief Digital Officer at Providence, describes the problem as follows: “Engagement in healthcare has two benefits. One is you really start to build a sticky relationship with your patients. There’s also a big economic opportunity in that if you’re entering into risk-based contracts, if you help build a healthier population, your economics are much, much better as a provider.”

Less optimistic patient outcomes

Adults who are disengaged and refuse to play an active role in their health are less likely to adhere to the prescribed treatment plans. According to recent studies, medication non-adherence affects up to 50% of patients with chronic conditions. Skipping medication has severe consequences and can even result in death. About 125,000 patients die annually in the US due to medication non-adherence.

Rising healthcare expenses

The lack of patients’ involvement in their own health and wellbeing leads to an increase in healthcare costs. As people avoid regular medical checkups and ignore their chronic condition management plans, they often fail to catch the early signs of diseases and contact providers when it is already too late, which leads to a prolonged and more expensive treatment. The US spent around $10,966 per person on healthcare in 2019. This is almost double of what other developed countries paid on average.

Source: Peterson-KFF Health System Tracker

Technology increases patient engagement. Here’s proof

Tools for patient engagement leverage innovative technology to improve user experience. Many organizations are already deploying technologies to reach out to patients at least 12 times a year, with AI and IoT being the most promising solutions in their toolbox.

How can IoT improve patient engagement?

Already back in 2018, 60% of healthcare organizations worldwide deployed IoT in their facilities. One of the most prominent applications of IoT in healthcare is remote patient monitoring. Patients use connected devices to capture relevant medical information and share it with their doctors. For example, IoT devices can collect data on vital signs, such as heartbeat, glucose levels, weight, mood swings, and sleeping patterns. All this data is transmitted to the provider, allowing them to catch on early signs of change in patients’ physical condition and overall wellbeing. When patients are directly involved in tracking their medical data, they become mindful of any changes in their current condition, which will make them more compliant and eager to participate in treatment options selection.

How can AI improve patient engagement in healthcare?

Here’s what Kevin Pawl, Senior Director of Patient Access at Boston Children’s Hospital, says about AI:

“You’re not calling a hospital with the joy of going on vacation or going on a trip or staying in a hotel, but it would be awful nice if the service and the experience were similar. That’s what we were trying to achieve as we tap into artificial intelligence.”

AI solutions in healthcare can increase patient engagement in a variety of ways:

Employing risk assessment strategies. AI can go through patient data and identify patients with a high risk of developing a particular disease and predict their healthcare needs. Based on this information, physicians reach out to high-risk patients and invite them for additional screenings or suggest a personalized preventive/curing treatment plan.
Relying on AI-powered chatbots for 24/7 communication. Calling a hospital can be daunting as patients are put on hold for prolonged periods of time. Intelligent chatbots can serve as the first line of response by filtering patient calls, answering simple questions (e.g., where is the parking), pointing patients to valuable resources, or redirecting them to a human provider. Chatbots respond to patients at any time round the clock, offer consistent responses, and can be easily retrained.
Understanding patient behavior. Providers engage with patients through intelligent mediums that can analyze and understand patients’ behavior patterns. This helps medical staff to come up with a personalized treatment plan tailored to an individual patient’s needs. For example, AiCure developed a platform that leverages AI to predict patient behavior throughout their clinical trial journey.

Types of patient engagement software and how much it costs to build them

If you decide on acquiring a patient engagement software solution, you will have three options: off-the-shelf, content management system (CMS)-based, or fully customized solutions. While choosing among the different options, consider the following criteria:

Are you willing to adapt your internal processes?
Is there a need to connect your new system with legacy equipment and applications?
How much support do you expect from your vendor?
How much are you able to pour in as an initial investment?
What is your current practice size, and are you planning to expand?
Do you intend to offer new services?
How much time can you dedicate to research, development, deployment, customization?
Do you need full ownership of this product?

Now let us weigh in on the advantages and downsides of different types of patient engagement solutions and assess their costs.

Off-the-shelf patient engagement software

There are a plethora of patient engagement solutions readily available for the healthcare sector. Hospitals can choose from a wide array of features and pricing models. Some patient engagement solution providers offer predefined types of subscriptions, while other companies give clients the liberty to select the features they want. Every patient engagement platform has its primary focus. Some are specialized in appointment scheduling, some on EHR integration, and some on e-prescriptions. The monthly subscription can vary from $12/month for a basic feature to $350/month and beyond for more comprehensive solutions. And of course, there are additional customization, support, and licensing costs to consider. The table below presents some examples of available patient engagement solutions at different price ranges.

Characteristics of off-the-shelf patient engagement tools:

This is the fastest option from order to deployment
No need to procure any additional technology as these solutions typically run in the cloud
Initial investments are significantly lower than in other options
In case you want to expand your practice and offer extra services, scaling your software will be easier if your current vendor has plugins corresponding to your new services. Otherwise, diversifying your operations will be a challenge
No ownership of your platform
If you require a unique set of features, you will need to do your research to find the right vendor
You might need to adapt your internal workflow to match the patient engagement solution unless your vendor shows flexibility
There is a risk that the vendor will discontinue supporting your version

Best suited for medical practices that:

Are small in size with up to 10 doctors; also, the option works well for individual practitioners
Have limited initial funds
Employ internal processes that can be easily adapted and integrated with the new solution
Will deploy the patient engagement system as stand-alone without integrating it with other applications

CMS-based patient engagement software

These solutions are custom developed using existing content management platforms, such as Wix and WordPress. These platforms allow building most of the basic functionality that patient experience solutions require, such as patient registration, appointment scheduling and rescheduling, patient-provider communications, patient education, and even EMR/EHR integration. With CMS-based solutions, you can either opt for a hosted platform (Wix, WordPress.com) or download the open-source version of WordPress (WordPress.org); the latter gives you more control over your system, but it is more expensive as you need to hire software engineers and designers to help you customize it. Hosted platforms might look like a more lucrative and cost-efficient solution.

However, their monthly plans seldom cover maintenance, and you'll have to pay for third-party plugins and extensions separately. As a result, this option offers minimal functionality. That is why we'll focus only on self-hosted CMSs below.

The costs of developing a CMS-based solution for patient engagement can fluctuate around $30k-$35k for systems with the basic functionality, such as appointment scheduling, online payment, and secure patient-provider communication. Such a solution is typically developed and deployed within two months.

If you want to enhance the basic solution with new functionality, you can either purchase and integrate third-party plugins, or develop custom plugins. Building a custom WordPress plugin will cost you $2k-$5k for a simplistic extension that adds one minor feature to your existing solution. A more complex plugin with an extended functionality will cost up to $20k. Meanwhile, a cumbersome plugin that requires in-depth analysis can easily add up to $45k and more.

Characteristics of CMS-based patient engagement tools:

You can quickly add new functionality with existing and custom plugins
The underlying CMS might limit your system’s scalability
Hospitals have ownership of the final products, but if they make changes to the CMS, it will be challenging to update it when the next version is out
There is a need to invest in technology and support personnel

Best suited for medical practices that:

Need a quick solution without overpaying
Have no immediate plans of expansion, as scaling CMS-based solutions can be difficult

Custom-built patient engagement solutions

Such a solution is developed specifically for your organization to cover your unique needs and cater to your internal processes. You will have full ownership of the final product. The vendor or your team will customize this software following any relevant changes in your organization. Unlike CMS-based solutions, custom-built platforms are developed from scratch, which gives you the freedom of choice when it comes to technology. Together with the vendor, you can select the web development framework, the database type (e.g., MySQL, MS SQL, etc.), front-end technology, and data visualization format. This type of patient engagement software is easy to integrate with third-party systems because you are in full control of the code. One example of a custom-built solution is this automated appointment scheduler that integrates doctors' schedules into the EMR system and facilitates appointment management.

Another example is this AI-based solution that manages data of patients diagnosed with cancer and assists doctors in developing personalized treatments. It also contains a social platform that allows patients to share their experience and support each other. The cost of a custom-built patient engagement system starts from around $50k. Under this price tag, you will get the basic patient engagement features, such as appointment scheduling, patient feedback, billing, and payment, as well as a simple chatbot integrated from third-party services and basic analytics. Such a solution will take at least three months to develop.

More complex patient engagement tools will cost at least three times more. For example, if you want a solution that offers all the basic functionality in addition to connected wearable devices that help monitor patients' condition, intelligent chatbots, and an AI-driven analytical system that can perform risk estimates for individual patients, that will cost you at least $120k-$150k.

Characteristics of custom-built patient engagement solutions:

Requires substantial investments in the application itself and the underlying technology
The most time-consuming option
Full ownership of the resulting solution
Extra costs on support personnel
High scalability
There is no need to change your internal processes as this custom solution will fit them well
Flexibility with integrating any new services the hospital is willing to include in its practice
You are not bound by any technology and can choose what suits your needs best

Best suited for medical practices that:

Offer a diversified set of services
Plan to expand in size and services
Can pull off a significant initial investment
Have rigid workflows that can’t be easily adapted
Need to integrate the new software with existing systems

Closing thoughts

Some providers are still skeptical about deploying patient experience software. They fear that elderly patients will not be open to navigating digital spaces. Sometimes doctors don’t want to admit they can use help communicating with patients. If providers used healthcare software in the past and had a bad experience, this can also cause resentment. Despite all the challenges, the organizations who tried patient engagement technology can attest to its considerable benefits:

Offering continuous 24/7 patient support
Assisting doctors with repetitive tasks and keeping them on top of patients' health issues
Keeping the overall healthcare costs down

If you've decided to procure a patient engagement solution, you have three options to choose from. You can either purchase an existing solution, build your software based on an existing content management system, or develop your solution from scratch without being restricted by any technology or workflow. Every option has its benefits and drawbacks. What suits you the most will depend on your current processes, desired features, budget, and future plans of your practice.

How to implement DevOps: mindset, practices, and step-by-step implementation plan

Anastasiia Bernatska — Mon, 19 Apr 2021 12:50:07 +0000

People, processes, and tools are the three foundational pillars of DevOps — the term coined by Patrick Debois in 2009 to describe a new culture of collaboration and shared ownership in software development. With DevOps adoption, software is developed and delivered in short cycles supported by automation and tools.

But DevOps implementation is not all hard stuff. According to Patrick Debois, DevOps is more a human problem. Or, as Viktor Farcic put it in his book The DevOps Paradox, it's a people game at its roots. In the old pre-DevOps days, developers would only tell the operations team after completing a piece they have been working on for months, "Here's version 2.0. Make it run." And Ops would run it to find out that everything has crashed. Finger-pointing was also commonplace.

DevOps processes, however, invite everyone to the party, making teams assume collective responsibility for user experience with new software. In the world of DevOps, Ops engineers are saying, "Fine, we get it, we should ship this piece tomorrow, we'll figure out how to meet the deadline." And Dev people are saying, "We don't want to be messaged in the middle of the night that something has gone terribly wrong, so we will help ship it safely."

In the DevOps environment, everyone is working together toward the same goal — building better quality software faster. With no blame game. Everyone means people from product owners, developers, testers, and IT operations to even marketing and sales that bring customer feedback to the table. An important thing to know is, you don't set up a DevOps department between Dev and Ops to implement DevOps. This way, you can get only a third silo. Instead, you change the culture of Dev and Ops to break down the walls between them.

What is exactly the DevOps mindset?

DevOps implementation above all means a cultural shift. Because DevOps is mostly about soft stuff:

Empathy and collaboration

Empathy, meaning the ability to think about things from another's perspective, is believed by many to be the true DNA of DevOps. It allows Ops people to understand the importance of quick releases, while developers can better appreciate problems caused by bad code. Plus, both parties are putting users at the center of everything they are doing. Empathy powers collaboration. Collaboration powers productivity, encouraging everyone to commit to software development quality and speed.

Mutual responsibility

DevOps implementation involves setting up small cross-functional groups who will work autonomously, taking mutual responsibility for continuous delivery. Loosely coupled product-centered teams, consisting of developers, operations engineers, testers, and project managers, enable microservices style, reducing the number of handoffs along the value stream — from version control to production deployment. However, individually, developers are still responsible for a piece of code they've written. "You build it, you run it," Amazon CTO Werner Vogels once famously said.

Knowledge sharing

Scrum standup meetings and retrospectives allow teams to understand what was done yesterday, what was successful, and what can be improved. In a knowledge-sharing environment, DevOps engineers learn new things and grow continuously, adjusting their competencies. At a bare minimum, everyone on the team should have on-demand access to source code, logs, tools, test servers, production builds, and postmortem reports.

Continual experimentation

DevOps is hypothesis-driven development. Failing fast to learn faster through a tighter feedback loop is key to DevOps implementation. DevOps teams write code in small batches and push it to production as quickly as possible. This way, they have an immediate chance to put the solution right by fixing defects swiftly or removing a certain feature altogether. As The DevOps Handbook co-author Jez Humble said, "Users don't know what they want. Users know what they don't want once you've built it for them." So, the DevOps mindset is about starting with intuition and learning lessons as you move forward to arrive at a data-driven decision.

Customer centricity

The DevOps culture relies on a product-first approach that cares about the needs of real users. DevOps teams should understand what they build and understand the people for whom they build. Continuous feedback is crucial. This often means that (i) Ops take part in developing service-level agreements with users to understand user requirements, (ii) developers heed to end-user requests with fast updates, leveraging a loosely coupled, service-oriented architecture that allows utmost flexibility, and (iii) testers set up user focus groups for a controlled UAT, interacting with users directly.

Automation

A very strong emphasis in building DevOps is put on automation that reduces human error and amplifies speed, cutting overhead costs. Automation in DevOps processes is extended virtually to everything, from development and testing to infrastructure provisioning, configurations, network tasks, databases, deployment, and monitoring. Netflix is known as a fantastic example for DevOps implementation, with its fully automated deployment processes allowing engineers to run code thousands of times per day.

Why to implement DevOps

According to the 2020 DevOps Trends Survey by Atlassian, organizations see a number of benefits from DevOps implementation, including:

Speaking of tangible DevOps benefits, an example from our experience would be a 5x increase in the speed of delivery of new features that we helped achieve in gaming app development.

DevOps implementation best practices

It's impossible to oversell the importance of automation for a successful DevOps implementation roadmap. Automation, however, cannot be done overnight. It's a journey with multiple stops and requires careful planning. According to the Boston Consulting Group's (BCG) classic article, Leaner, Faster and Better with DevOps, organizations implement DevOps in four automation steps:

Continuous Integration

An essential part of agile processes, CI allows DevOps teams to always work with the latest code version. In the CI process, developers push new pieces of code into a shared repository at least once a day for validation through an automated build and testing, which typically includes unit, integration, and basic security tests. Builds and tests are triggered by a version control tool for code check-ins which the server is watching. So, CI is a fully automated process run within a few seconds or minutes to allow developers to know immediately if their code is right and has successfully integrated with others' code or has broken the build.

Continuous Delivery

Continuous Delivery automates the build-test-release process, allowing new builds to be released with one button push. While CI is focused on the quality of code to make sure it passes all testing before being merged into the master build, the objective of CD is to ensure through more testing that the software (the master build) behaves as intended in the real world. CD is a short cycle, completed in a few hours, enabling the team to automate everything in the life of a code. In essence, it consists of two pipelines: a CI pipeline and continuous testing.

Continuous Deployment

In a Continuous Deployment process, code changes are deployed automatically after successfully passing the complete testing cycle. There is no need for human intervention to decide what to release and when. Continuous Deployment differs from Continuous Delivery in only one thing: it involves an automated push to production environments. Continuous Deployment, however, requires setting up a rigorous automated testing framework.

Continuous Monitoring

If an organization's environment is automated, ensuring its stable performance will be an imperative. In DevOps infrastructure, there are a lot of things you can monitor, but it is, however, impossible to keep track of all of the services and procedures. So, when implementing DevOps, you need to understand which are your primary monitoring targets. These can include the efficiency of your CI/CD pipeline, deployment milestones, automated handling of vulnerabilities, server health, user activity, application logout output, and application performance. You do the job either through application monitoring tools like Datadog or Newrelic or by going manually through application logs or by configuring the notifications and alarms. Important metrics to watch are failures, usage patterns, performance, availability, scalability, and custom telemetry.

Plus, Configuration Management

This process in DevOps implementation ensures optimal configuration of multiple testing environments, set up for unit testing, integration testing, load testing, acceptance testing, end-user testing, and more. Unlike in the traditional organization where configuration management is done through formal documentation, this process in a DevOps environment is automated to a great degree — configurations are encapsulated in code or scripts and controlled through a version control tool. Configuration management systems have given rise to:

Infrastructure as a code, which codifies all infrastructure resources in a code or a script, with the text files stored in a version control tool like Git.
Configuration as a code, which defines server configurations as a code or script and commits them to version control.

Infrastructure automation enables automated infrastructure provisioning, configuration, and scaling, increasing productivity, saving costs, and freeing engineers for more creative tasks.

Step-by-step DevOps implementation plan

Drawing on our vast hands-on experience, we've compiled a list of practical recommendations on how to implement DevOps activities successfully:

Start a DevOps initiative DevOps adoption should start with a strong business justification. Your DevOps initiative should be aligned with hard-boiled business goals, rather than driven by a fear of missing out. The CIO plays a key role, initiating DevOps transformation as part of the organization's digital activities and planning financial investments and human resources.
Build a DevOps strategy Program managers are responsible for developing the company's DevOps implementation strategy. They need to identify its vision based on challenges like production defects, complicated deployments or team burnout, and figure out how to make everything in a software development cycle continuous. It's also critical to set KPIs for your DevOps team. A common set of metrics, proposed by Nicole Forsgren, Jez Humble and Gene Kim in their book Accelerate, includes: Lead time for changes – the time it takes to go from code committed to code successfully running in production Deployment frequency – software deployment to production or an app store Time to restore service – mean time to recover (MTTR) Change failure rate – how often deployment failures occur in production that require an immediate rollback
Set up a competent team No matter whether your company chooses to go with an in-house or outsourced model, people on your team should be qualified and eager to learn new things. They should be experts in advanced DevOps tools, able to think out of the box when configuring complex scenarios, and… have the patience to troubleshoot and debug as many times as needed to find the root cause of a problem. Consider including a release manager, automation experts, testers, QA lead and SecDevOps engineers in your team. The right people are key. According to the 2020 DevOps Trends Survey, a lack of skills is the number one inhibitor to successful DevOps implementation.
Choose the right tools There are many good DevOps tools available today, from release planning to building, testing, and safely delivering applications. In fact, there is an overwhelming amount of them that keeps growing, so it's easy to get confused. Get expert help if needed and don't be. When choosing DevOps tools, be also sure to understand how they will fit together to avoid ending up with what Gartner calls disconnected islands of automation. What you will need is an integrated toolchain and systematic automation implemented with tools aligned at each stage.
Containerize your applications Сontainerization makes your application easy to execute. Containers, implemented with tools like Docker or Kubernetes, are basically an isolated process for an executable file running on a Linux machine. They include code, dependencies, binaries, libraries, and other things your software needs, presenting an environment that can follow the application from development to testing to production deployment. Containers are considered the best choice for a microservices architecture that splits your application in multiple microservices focusing on smaller tasks and providing scalability, flexibility, easier automated testing, and rapid deployment.
Integrate infrastructure automation with CI/CD You should run a number of application services integrations to make infrastructure automation tools such as Kubernetes, Chef, or Puppet work with CI/CD tools (Jenkins, Bamboo or other). Seamless integrations will arm you with a consistent set of services applied automatically. As a result, you will be able to deploy new infrastructure configurations and release new software way faster.
Automate testing To achieve DevOps-style speeds and agility with DevOps implementation, it is important to automate testing as much as possible but as appropriate. Once you add automation testing to the process, checks are run the moment new code is complete, and your developers no longer have to spend days on trying to remember and fix code written many weeks ago. According to Slava Makovsky, QA Automation Team Lead at ITRex with more than 15 years of experience with a global tech giant, there are few things to remember when implementing test automation: 1. Start with creating simple test cases, rather than designing a complex automation framework up front 2. Smoke tests are an imperative 3. Use remote machines to run tests from the very beginning 4. Make sure you've got a well-thought-out plan for running tests in parallel and as part of your CI/CD pipeline 5. Set up capabilities to analyze test results on a daily basis
Start Small Before configuring any complex scenarios, it is safer and more beneficial to begin with small initiatives in DevOps implementation. Pick carefully your first app and scale only when you are ready. There are basically no restrictions for applying DevOps, but web-enabled agile applications are a popular choice for the first moves. These applications are often architectured from the beginning with a lean or agile mindset that complements DevOps.
Collect feedback "We all need people who will give us feedback. That's how we improve," Microsoft Founder Bill Gates said. There are two types of feedback you might want to seek: feedback on bugs and incorrect usage. Apart from collecting customer-centric data, such as the load time of your application, the maximum number of concurrent users or performance differences between web, mobile app and wearables, you can garner feedback directly from your users. You can either build user feedback mechanisms within your application using emojis, star ratings or binary-type questions, or ask users through your app or in an email to join a survey, or study public discussions. Feedback is an essential part of the DevOps loop, informing the next steps and enhancements.

A final note

While it may be cliche to say that every business should implement DevOps today if they want to keep up with the pace of innovation and stay competitive, almost half of organizations are only starting with DevOps, practicing it for less than three years, according to the 2020 DevOps Trends Survey. DevOps transformation indeed can be complicated. It is not an initiative that can be undertaken once and for all. It is not even done after you implement continuous practices. These will require continuous improvement as well.

Contact ITRex if you are interested in learning more about DevOps or need help from our experienced consultants with starting or accelerating your DevOps journey.

AIOps for your business — how it works, implementation tips, and use cases

Anastasiia Bernatska — Fri, 16 Apr 2021 11:30:09 +0000

As more companies are digitizing their processes, the complexity of enterprise IT deployments is increasing too. This will inevitably lead to Ops inefficiencies — and inefficient back-office operations reportedly cost companies 20-30% of their annual revenue. AIOps could be a silver bullet solution to this problem as it helps teams to detect issues earlier and resolve them efficiently before business operations and customers are impacted.

What is AIOps, and how does it work?

Interest in implementing artificial intelligence solutions in information technology operations has increased dramatically in recent years. As more companies jump on the digital transformation bandwagon, the number of IT systems (and the amount of data they produce) has skyrocketed. As a result, IT operation teams struggle to identify and fix system failures, unauthorized access to corporate data, and other issues that may disrupt business operations. And here's where artificial intelligence might come into play.

Let's start with the AIOps definition. AIOps, short for Artificial Intelligence for IT Operations, refers to a multi-layered environment where Ops data and processes are monitored using AI. Typically, the term describes multi-layered technology platforms that automate the collection, analysis, and visualization of large volumes of data gathered from different tools, logs, metrics, and other sources.

AIOps platforms leverage big data, collecting miscellaneous information from different IT operations tools and devices to automatically spot and react to issues in real-time while still providing traditional historical analytics.

In general, an AIOps platform should perform these functions:

Automate routine practices
Perform real-time and historical analysis of data
Recognize and predict issues faster and more accurately than humans
Streamline the interactions between data center groups and teams

Image source: StackState.com

Check these four steps on how AIOps works:

Step 1 - Getting relevant data

An AIOps platform gathers historical data and events, system logs and metrics, network data, and real-time operations events. AIOps is able to remove noise and duplication and distinguish only the truly relevant data.

Step 2 - Data analysis

Then, AIOps groups and analyzes the information, discovers patterns and root causes of failures/issues in operational data.

Step 3 - Automated responses

Following the data analysis, AIOps automation tools come to action. An AIOps platform can automatically alert IT teams about the problems, define what caused the issues, and suggest solutions. Also, it can implement automatic system responses to solve issues in real-time before users are even aware some problems occurred.

Step 4 - Visualisation

AIOps visualization tools provide real-time insights, reports, and graphics that allow IT operations to resolve issues quickly and take the most efficient actions.

Image source: TechTarget

AIOps use cases: how can businesses apply artificial intelligence in IT operations?

IT operations management is becoming more challenging and complex due to ever-growing volumes of data generated by different applications and systems. According to the Dynatrace 2020 Global CIO Report, organizations now use an average of 10 and more different monitoring tools in day-to-day IT operations. Artificial intelligence can be a welcome addition to traditional IT infrastructure monitoring solutions, as it automates time-consuming routine tasks and allows Ops specialists to focus on mission-critical issues.

Some of the real-world examples of AIOps usage include:

Big data management and performance analysis

The IDG Data & Analytics Survey showed that the average enterprise has managed 347.56TB of data, as for 2016. The top three sources of data are sales and financial transactions (56%), leads and sales contacts from customer databases (51%), and email and productivity applications (tied at 39%). Thanks to machine learning algorithms and neural networks, AIOps solutions can gather and structure vast amounts of data, which is extremely difficult to do manually.

Anomaly detection

AIOps helps detect anomalies in response time, CPU usage, and memory usage faster and more efficiently and alert users in emergency cases. For this, AI algorithms analyze data from application and infrastructure logs and compare insights to historical data. AIOps can also reduce false alarms by up to 90% and minimize the impact of irrelevant notifications.

Threat detection and analysis

One of the most essential AIOps use cases is applying AI to security management. Advanced machine learning algorithms can analyze data from various internal sources (netflow logs, application event logs, and DNS logs) to identify malicious activity within the infrastructure. In such a way, IT teams can detect a variety of breaches and violations and react accordingly.

Storage management

AI can handle both basic and complex storage management tasks. Storage resources are constantly tracked for optimal efficiency and usage when handling output workloads. When the performance is reduced due to lower IOPs or when the disk is nearly complete, the administrators receive an alert. AIOps predictive analytics can adjust storage capacity by adding new volumes proactively automatically.

Automation

AIOps allows IT teams to set automatic alerts for incidents and remediate issues by running automatic system responses. The approach helps resolve application performance issues in real-time, thus improving productivity and overall user experience.

AIOPs tools can be beneficial for the healthcare, retail, logistics, banking sectors. Here are just some of the most common AIOps uses cases across industries:

AIOps use cases in healthcare

Healthcare IT departments can apply AIOPs to secure patients' data and prevent breaches, keeping electronic personal healthcare information (ePHI) safe in compliance with the Health Insurance Portability and Accountability Act (HIPAA). Also, there are cases such as applying AIOPs tools to reduce interruptions during emergency calls or analyze significant data volumes.

AIOps use cases in retail

Big retailers and smaller stores also benefit from applying AI and machine learning to their day-to-day IT operations. Thus, AIOps tools can help syncing data across all retail channels and platforms, including POS, self-checkout systems, mobile-based customer loyalty/referral programs, etc. With the help of AOIps, retailers can better secure customer data and create a personalized customer experience or implement new intelligent devices and checkout-free tools.

AIOps use cases in logistics

Using AIOPs tools, logistics companies can reduce networking errors and fix application issues. Therefore, companies can improve delivery speed and accuracy, which will lead to better customer experience and brand loyalty.

How your business can benefit from AIOps solutions

The pandemic has changed the way most companies work today. For Ops teams working remotely, it is critical to optimize the problem-solving workflow to be completed quicker, more accurately, and with fewer manual procedures. IT departments have faced many challenges, such as ensuring a stable connection between the end-users and the network, data, and/or apps. Companies had to adopt new tools to set up efficient communication within the organization and their customers and partners. Moreover, enterprises' IT environments are getting more complex to manage, as operational data spread across on-premises and multiple cloud systems. As a result, there's very little time and limited resources to hunt out, fix issues, respond to every user, and bounce between management and operations tools.

AIOps gives Ops teams the flexibility and time they need to track, recognize, and fix problems efficiently before they might impact business-critical digital systems and customers.

IT departments should use AIOps to:

Control infrastructure output in a multi-cloud environment
Achieve more accuracy in capacity planning
Optimize storage resources by automatically adjusting capacity
Improve resource usage by analyzing historical data and making predictions
Detect, predict, and avoid IT operation issues
Organize and monitor linked devices through a network

AIOps may offer significant business advantages to enterprises by optimizing IT and reducing operational costs. For instance, you can increase the number of satisfied customers by avoiding delays and bring your brand reputation to a higher level.

Moreover, AIOps optimizes revenue generation because when apps malfunction, sales are lost. In the survey conducted by Enterprise Management Associates, companies ranked AIOps as the most successful IT analytics investment, with 81% indicating that the value they get from AIOps exceeds its cost.

Other benefits of implementing AI to IT Ops include:

Root-cause diagnosis and remediation can be completed faster, saving time, money, and energy for the company
Service delivery is enhanced by increasing response time and accuracy
IT executives have more time to collaborate with business colleagues, which improves the quality of the strategic planning and organization of the IT department

Things to consider when implementing AIOps to IT operations

AIOps solutions are getting more popular among businesses, and some may think that if you merge traditional IT operations tools with AI algorithms, you will get an AIOps platform. It's not true, as a real AIOps platform is not just a set of tools. This is important to understand when you start implementing AI to IT operations as it will determine the result.

When implementing artificial intelligence for IT operations, make sure to follow these steps:

Identify your main AIOps goals

It is best to implement AIOps step-by-step. Users typically begin to apply machine learning to monitoring, operations, and data to automate IT and helpdesk services. You should specify the key issues your company is trying to solve before you start applying AI tools. AIOps requires strategic planning and thoughtful implementation.

Provide access to the required data

An AIOps platform is data-driven, requiring access to all relevant operations data, including unstructured machine data such as logs, events, metrics, streaming data, API outputs, and device data. Such various data types allow you to build a comprehensive view of all silos and take the best decisions that are relevant to the situation and data type.

Gather and analyze as much data as you can

Start with accessing and analyzing past states of your systems to identify trends and patterns. Provide access to a vast range of historical and streaming data types. The data types that you select depend on the problems you're solving. Then you can begin to analyze streaming data to see how it fits those patterns.

Many AIOps platforms are used to focus solely on a single data source, which limits your understanding of application behavior. It is better to use AIOPs platforms for IT that can consume and analyze data from a variety of sources.

Focus on priority problems

Concentrate on finding the root cause of your key problem and track the data. Then, start with implementing the AIOps platform, which gives you both an effective foundation for organizing large amounts of data and monitoring capabilities to reveal similarities. Use machine learning root-cause analysis to move into a predictive state. You will be able to identify an incident and evaluate its impact before it even affects key business services and customer experience.

Test & deploy

Run the system in testing mode for at least a couple of weeks to determine that the outputs are accurate and that users are happy with the recommendations. Once you are comfortable with your testing results, it is time to turn the system on in production.

Custom AIOPs vs. vendor solutions

AIOps is a rapidly growing area, which offers many ready-made tools such as Splunk's IT Service Intelligence (ITSI) tool, BMC's TrueSight platform, Cisco's Crosswork Situation Manager, Moogsoft AIOps, and DRYiCE AIOps from HCL Technologies Ltd.

Alternatively, you can choose to build your own custom AIOps platform using open-source frameworks. Among the benefits of in-house built AIOPs platforms for IT departments are data security and multifunctional performance.

Vendor AIOps tools require access to your data, which can be sensitive and impact business in case of breaches. When deciding to buy an AIOPs tool, you must rely on a trusted vendor. Compliance issues can also arise, especially if the tool moves user data into the vendor's own infrastructure for processing or storage.

Also, you should think about the range of real-time and potential needs you want to resolve with an AIOPs platform. Not every vendor provides products in each category. So it's best to look at the vendor's full range of AIOps options and consider future needs as you start deploying your own AIOps platform.

However, this way can be risky, as it takes considerable expertise to not only build an AIOps platform but also to integrate and maintain it.

Here are the pitfalls you may face when developing your own AIOps platform:

Data management

IT teams that build their own AIOps platforms need to make sure they collect all relevant logs, metrics, and traces along with data collected from IT service and incident management platforms. A poorly constructed AIOps platform will show insights incorrectly and will not accurately reflect what's actually happening in the IT environment.

Deployment, service, and support

The deployment is complex. After developing several algorithms for AIOps to produce meaningful results, the next challenge is figuring out how to deploy it in architecture.

The internal IT teams will need to create a product that requires ongoing service and support. The total cost of a custom platform will continue to rise as the IT team may end up spending most of their time managing the AIOps platform instead of improving it. Even if the IT team has the experience required to build an AIOps platform, there is no guarantee they will always be available to maintain and update it.

Keeping up with the trends

Finally, AIOps as a field is still in its infancy, and most internal teams are not keeping up with the rapid development of artificial intelligence in IT operations alone.

Take-home message

Today, as the competition level is high and new reality requires a search for optimization of IT operations, AIOps platforms are becoming not just a trend but a must-have solution. Being able to automate processes, analyze big data, recognize problems, and aid in smoothing communications between teams, artificial intelligence helps many large companies with complex IT environments. Forward-thinking enterprises will use AIOps to draw valuable insights from their IT data that will help drive strategic business decisions.

There are many opinions and theories on how AI will transform DevOps and IT operations. But today, we already have an opportunity to explore successful AIOps use cases across the different industries, included but not limited to retail, logistics, healthcare, banking, and many others. According to the OpsRamp survey, 87% of AIOps implementation cases ended successfully. Here are three leading insights from The State of AIOps report, which included reviews from 200 IT leaders that had applied AIOps in their organizations:

AIOps adds value

87% of technology pros agree that AIOps tools are delivering value through proactive IT operations and improved hybrid infrastructure resilience.

AIOps improves operations

The three biggest benefits of AIOps tools include the productivity gains from the elimination of low-value, repetitive tasks across the incident lifecycle (85%), rapid issue remediation with faster root cause analysis (80%), and better infrastructure performance through noise reduction (77%).

AIOps requires a solid expertise

IT leaders identified the main concerns and pitfalls while implementing Aiops tools, which are: that data accuracy/trust in the reliability of AIOps tool recommendations (67%), lack of skilled employees with data science and machine learning skills to support AIOps deployments (64%), and loss of control (52%).

Top 4 online education trends, or will AI become the future of eLearning?

Anastasiia Bernatska — Tue, 13 Apr 2021 11:56:23 +0000

Pushed by COVID-19, education has switched to the online mode. But it looks like online education is likely to stay with us even after the pandemic is over. More colleges and universities are switching to fully online degrees and iMBA’s as they are cheaper and more accessible.

And techs, and specifically AI, can take online learning a step further. For example, artificial intelligence solutions can be used for curriculum personalization or automated test grading. AI can make distance learning less stressful and more engaging.

Learn more about benefits of artificial intelligence in online learning in this article https://itrexgroup.com/blog/online-education-trends-will-ai-become-future-of-elearning/