DEV Community: Dmytro Spilka

How to Turn a Process Document Into a Working App With AI

Dmytro Spilka — Mon, 02 Nov 2026 12:12:54 +0000

Organizations, no matter how big or small, have processes and procedures. Some are smaller and more specific; others are more encompassing, but all of them are vital to how a business runs.

Despite their importance, many management teams still rely on basic documents, both paper-based and in basic spreadsheets and document files, and while this works, it keeps processes static and can be tough to adapt to changing business needs.

But turning these documents into practical tools has traditionally required technical development, as well as coding knowledge and software teams. Recently, however, AI is allowing businesses to use platforms like Pave to move from written processes to working applications.

Before beginning to turn all documents into apps, it’s important to understand the benefits of doing so.

Why Do It?

Process documents tell your staff how work needs to be done, a key part of their training and ongoing performance within a company. Employees must read, interpret, and manually follow the steps within the documents.

With this information existing in written format, processes can be passive and don’t encourage active participation. Also, completion visibility requires manual checking and logging from management, and consistency largely depends on the person who created the document.

Turning them into apps tries to avoid these by creating an interactive, self-tracking, and consistent workflow. Processes become more visible and accessible throughout the company.

Rather than just recreating documents digitally, the aim is to create something employees can actively use based on the expertise and processes the company already possesses.

However, the process isn’t as simple as just typing out a document into the platform. Here are some key steps to help you translate your process documents into functional apps.

Every App Starts with a Process

Before starting to develop an app, it’s important to first understand the process you want to create. Although process documents describe how work should happen, AI provides a way to turn this into the basis for an application.

This requires an organization to understand and be able to declare success states for the app they want to create. Without taking this step, companies run the risk of creating software bloat that doesn’t serve a clear purpose.

Once the process is understood, AI can begin turning that information into an app.

Turn Requirements Into Draft

Once the process that is being used is fully understood, the creation can begin. AI can translate the mapped process into an initial structure.

From here, the AI can help build the core workflow for each process and allow the designer to test the app using realistic scenarios. At this stage, it’s important to compare what happens in the app against what was expected during the real process.

At this stage, avoid trying to generate the entire finished application in a single prompt; this can be both frustrating for the user and difficult to deliver. It is also important to avoid assuming that because something works technically, it will work operationally; it’s important to test fully.

Once the basic framework is in place and the core process can be replicated, then it can be built upon.

Onboard and Integrate

After the creation and testing, the app will be ready for integration. AI can help with connecting and establishing points that the new app can fit in with the existing technology environment.

This should be done in smaller batches within the AI framework to allow more stress-testing and bug finding before complete launch. This will also allow natural-language changes, making it easier to adapt. It will also allow feedback from early users to be quickly translated into refinements.

For this step, don’t forget that AI-generated changes will need validation themselves. It is also important to monitor how the new app is connecting to everything to avoid creating new points of friction.

Steady and purposeful integration helps establish the new app firmly and securely.

Maintain and Improve

Once the app is up and running, the work is not finished, and leaving it might cause it to become outdated and unusable quickly, costing the business money. AI platforms help keep the app evolving as the process does.

Whilst the app is active, keep gathering feedback and usage information to help keep modifications useful and relevant. Test modifications before deployment, and make sure to regularly compare the new app against the current real-world process to ensure progress is being made.

It’s easy to forget that repeated AI-generated changes can have a cumulative effect, so make sure to keep track of what changes are being made. At this stage, it’s also important to be aware of how many users are able to make modifications.

Losing track is one of the biggest dangers now facing the new app.

The aim of making an app is to take a process document and turn it into something that your employees can use. The ability to do this takes understanding and flexibility. Although AI reduces the technical barrier required to make the change, it still requires active management and participation.

If done correctly, a process document doesn’t just describe how work gets done; it gives the staff a starting point.

How Intelligent Tools Have Changed the Way We Handle Inbound Logistics

Dmytro Spilka — Wed, 09 Sep 2026 16:13:57 +0000

Advanced technologies like artificial intelligence, robotics, and the Internet of Things (IoT) have fundamentally transformed the way businesses manage their warehouses, replacing manual labor with smart, self-operating systems that cut costs while boosting efficiency at scale.

According to a recent survey of 500 supply chain leaders, 73% of respondents claimed to expect automation adoption within five years. At present, 25% of warehouses are using automation technology in some form, while just 10% have implemented advanced automation technologies.

To support this significant rise in adoption, Grand View Research has estimated that the warehouse automation market will more than double from a value of $27.4 billion in 2026 to $59.5 billion by 2030.

It’s clear that there are plenty of advantages for businesses to benefit from when it comes to embracing warehouse automation, with tools capable of blending digital software with physical hardware, allowing more firms to lower operational costs while stamping out human error and scaling up order throughput to meet respective industry demands.

With this in mind, let’s take a deeper look at emerging use cases when it comes to analyzing the ways that automation is driving warehouse improvements in inbound logistics:

Predictive Replenishment

One of the biggest advantages that artificial intelligence brings to supply chains is its ability to shift from reactive processes to more proactive strategies based on algorithmic insights.

Critically, AI algorithms can utilize machine learning (ML) to assess historical sales data to forecast upcoming demand spikes. This can pave the way for autonomous reordering in a way that prevents stockouts or the negative cost implications of overstocking.

Artificial intelligence tools also specialize in tasks that predict factors that can influence outcomes, allowing for warehouse adjustments to be made in real-time to better accommodate swift processes and order fulfilment.

For instance, a higher level of throughput and faster order processing means that automation tools can speed up picking, sorting, and shipping to support warehouses in meeting growing customer delivery expectations at scale.

Greater Visibility

With the help of integrated warehouse management systems (WMS) and RFID tracking, items can be logged instantly as they arrive, removing the need for paper documentation while updating stock tallies in real-time for far greater levels of visibility.

As a result, automation tools can fully integrate with enterprise resource planning (ERP) systems for greater security, stronger data management efficiency, reduced operational and legal risks, and improved safety.

Digital automation measures can also support a positive experience for warehouse workers, who will be supported by IoT tools for enhanced efficiency when it comes to data entry.

As soon as a new arrival or departure occurs within inbound logistics, RFID tracking means that systems are instantly updated for a more holistic overview that can be seamlessly shared throughout departments. This can be critical for saving worker time and effort while maintaining a higher degree of accuracy.

In many cases, manual data entry errors can lead to costly impacts that could be prevented by automation. The arrival of RFID tracking and IoT technology means that workers can benefit from far more effective levels of digital oversight.

Working with Digital Twins

We’re also seeing an increasing number of warehouses adopt digital twin technology, which creates a live digital model of the space itself.

Powered by advanced tools like AutoScheduler, digital twins can map out workflows, anticipate bottlenecks, and adjust workforce schedules in a predictive and proactive way for the best efficiency outcomes.

Critically, digital twins excel at scenario planning, which allows businesses to run countless ‘what if’ hypotheticals to check whether they’re ready to deal with unforeseen challenges and industry bottlenecks before they happen. This can help with advanced planning and better resilience in the face of new hurdles that may present themselves in inbound logistics.

Supercharging Order Fulfilment

We’re also seeing automation transform the capabilities of robotics in warehouses, with physical bots capable of not only handling heavy lifting but also acting on data to accelerate order processing speeds while keeping human staff safe.

Unlike the guided vehicles that require tracks to operate, modern autonomous mobile robots (AMRs) can be deployed with built-in sensors that can navigate changing warehouse floor layouts and safely carry goods directly to packing stations.

We’re also seeing the rise of the ‘cobot’ which can work alongside human warehouse specialists as a form of mobile cart that guides users along the most efficient routes to find items on a faster basis.

These processes are helping more businesses to transition from inbound logistics efficiency to high-quality last-mile fulfilment that caters to more effective same-day delivery, for instance.

Transforming the Modern Warehouse

For businesses of all shapes and sizes, the demands of modern logistics have opened the door to a range of fresh challenges that automation tools are working to provide advanced solutions for.

In terms of holistic overviews, the modern warehouse has never been more advanced, with RFID technology capable of providing real-time tracking and cloud-based classification of items that stamp out human error.

Automation tools are also excelling at order fulfilment, while leaning on predictive analytics to proactively replenish inventory as and when it’s needed. This is helping to not only help to support more intelligent cash flow forecasting, but also equip businesses with the tools needed to navigate an ever-changing supply chain outlook with greater resilience.

Vibe Coding Project Management: Can AI Build Intelligent Code-Free Solutions for Construction Firms?

Dmytro Spilka — Wed, 15 Jul 2026 16:00:20 +0000

The construction industry is experiencing digital transformation at an unprecedented pace, and firms will need to embrace artificial intelligence (AI) if they want to build intelligent, code-free project management solutions that better fit their workflows.

Custom project management solutions are particularly useful for construction firms, as traditional software may not fully support the evolving workflows and processes of every project.

However, working with professional software developers to build custom project management solutions can be expensive, time-consuming, and offer less control. This is where AI-powered, no-code application builders become increasingly helpful for construction firms.

Vibe coding is one of the latest AI-powered approaches to software development, trending across all industries. It allows users to describe what custom software solution they want in natural language, rather than having extensive coding knowledge or working with professional software developers.

This article further explores vibe coding and AI-powered, code-free project management solutions.

What Is Vibe Coding?

Vibe coding refers to using generative AI (GenAI) and natural language prompts to create custom software and applications, rather than manually writing complex code. Instead of waiting months for custom software to be developed and tested by professionals, almost anyone can use vibe coding to create working prototypes within days.

In a project management context, vibe coding can be used to quickly build custom dashboards tailored to project needs. Here’s how it works, in three simple steps:

Describe your needs for a custom solution: Use plain English and natural language to describe the project management solution you need. For example, the prompt could be "create a project management dashboard that tracks subcontractor progress, flags budget overruns, and sends reminders when inspections are coming up."

AI builds the software: GenAI technology generates the software's interface, workflows, dashboards, and functions based on the given prompt.

Refine and maintain: Project managers can then refine the software using additional natural language prompts or manual editing. Finally, they can publish and continue to maintain the dashboard without extensive coding knowledge.

Benefits of Code-Free Solutions for Construction Firms

AI-powered, no-code software builders, such as Quickbase's Pave, enable construction firms to accelerate the development of custom project management solutions that improve collaboration and streamline workflows.

Here are some of the benefits of construction firms using AI-powered, no-code builders:

Faster software development: With the right no-code builders and prompts, GenAI can rapidly produce customizable software in minutes. This means construction firms save time that would otherwise be spent manually inputting lines of code or going back and forth with professional software developers.

More control: Project managers have more control over the software's interface and functionality when using natural language prompts. This is better than leaving the software to a developer who doesn’t fully understand the construction industry.

Lower software development costs: Using AI-powered, no-code application builders is often less costly than hiring a professional software developer to do the work.

Despite its potential, AI-generated software is not without its challenges, especially without appropriate human oversight. AI generates code quickly, which may introduce security vulnerabilities or fail to follow best practices. Before publishing AI-generated software, construction firms should check for automation errors and ensure proper governance around data quality and user permissions.

Can AI Replace Software Developers?

While vibe coding is advancing at a rapid pace, the AI-powered method is unlikely to completely replace professional software developers. Instead, construction firms should use AI to support fast-paced software development rather than treat it as a fully autonomous developer.

Whilst custom project management software can be simple, building certain applications and softwares will require technical expertise, critical thinking, and human judgment - skills that AI doesn’t have. This is particularly true when construction firms integrate multiple systems, such as customer relationship management (CRM) systems, payroll, accounting, and project management software.

Like in many industries, the greatest value often comes when combining AI with human oversight and input.

The Future Of Construction

As construction projects become increasingly complex, firms need access to custom project management solutions that improve workflow efficiency and collaboration between teams.

AI allows construction firms to build intelligent code-free project management solutions in minutes, giving them a competitive advantage in a growing industry.

5 Insights Into How Generative AI is Reshaping the Future of Open Banking

Dmytro Spilka — Thu, 20 Feb 2025 15:59:32 +0000

We’re in the midst of a generative AI revolution. With the technology set to disrupt a number of industries throughout the months and years ahead, the GenAI boom is set to transform the capabilities of open banking as we know it.

The relationship between generative AI and fintech is growing at an exponential rate. The market size is expected to grow significantly over the coming years to a value of $16.4 billion by 2032, representing a CAGR of 31% from its $1.1 billion market size in 2023.

While open banking is still in its infancy, it’s set to form a core component of the future of fintech. Representing a transformational shift from traditional financial services, open banking is a key industry trend that enables the secure sharing of financial data through APIs with third-party services, helping to leverage an unprecedented volume of actionable financial insights and accessibility to products and services.

How GenAI is Enhancing Open Banking

The rise of generative AI has brought significant enhancements in the fields of mathematics and statistics, and the utilization of machine learning (ML) is helping to open the door to unprecedented opportunities within the fintech landscape.

The contribution of mathematics and statistics and their applications in linear algebra, calculus, probability theory, and optimization algorithms are invaluable in building a strong open banking framework. These can combine to build more comprehensive models while ML algorithms can analyze patterns to make accurate predictions surrounding metrics like spending habits and investment opportunities.

Machine learning can also help to interpret these metrics to deliver actionable advice built on accurate forecasting. With the help of image and natural language processing, data can be analyzed across a range of structured and unstructured sources, contextualized, and delivered to users in a way that can improve their financial management.

Generative AI can have a range of applications across different industries because of its strength, and we’re already seeing an impact on the quality of open banking services for different users.

With this in mind, let’s explore four ways the GenAI revolution is reshaping open banking:

1. Next-Generation Payments Insights

Generative AI insights can leverage historical payment data to offer fresh perspectives and actionable advice across different areas of financial management.

Through AI-powered open banking dashboards, users and businesses alike can discover more effective ways to make payments for goods and services, better understand their cash flow, and make budgeting decisions.

With ML logistic regression classification algorithms, these dashboards can provide payment source and method insights, as well as non-straight-through processing rates and payment processing duration data, helping users gain recommendations on effective payment methods with lower associated fees.

2. ML Fraud Detection

With the help of machine learning, AI can be trained using significant volumes of historical payment information, helping it to learn how cards are typically used while providing analysis with an overview of current fraud trends.

These models can help to automatically filter low-likelihood fraud alerts while assigning a specific level of risk to each case, allowing analysts to minimize instances of manual intervention.

Fraud managers can also use AI to summarize all suspicious payment behavior, allowing open banking providers to develop new fraud rules and apply their ML models by asking them how the rules and models would perform in a series of scenarios to build confidence in their capabilities.

3. Omnichannel Payments

Omnichannel payment solutions can also help to enhance the open banking ecosystem by allowing customers a series of transactional choices as well as driving greater operational efficiency and cost-effectiveness in the payment process.

Generative AI’s automation capabilities mean that we can access greater levels of personalization that can pave the way for more cost savings whenever a transaction is made.

We’re already seeing use cases in the form of Amazon’s palm-scanning technology within Amazon Go stores. This form of biometric payment technology uses a customer’s physical attributes to authorize payments without the need for any further action.

4. Intuitive Support

Generative AI support bots can also improve open banking by providing far more focused contextual information surrounding user queries and requested insights.

While chatbot technology is nothing new, traditional bots can be limited or incapable of interpreting user queries. However, large language models (LLMs) can interpret user queries at scale and provide instant responses that are easy to interpret.

This paves the way for open banking users to have human-level conversations with chatbots to access far more detailed information in response to various queries.

Fintech’s Next Frontier

Generative AI in open banking will be more than a trend, these united technologies will fundamentally change fintech and the way we manage our wealth in the future.

By understanding the opportunities that the technology offers, users and businesses alike can prepare in advance for the age of flexible payments and shifting consumer spending patterns.

The age of GenAI in fintech is already upon us, and it’s paving the way for a new frontier in financial management.

Meet the Seven Fintech Innovations Driving eCommerce Growth for Startups

Dmytro Spilka — Fri, 18 Oct 2024 17:03:09 +0000

The evolution of eCommerce is being supplemented by fintech innovations that are making online purchases more streamlined and adaptable than ever before. This is helping to put more industry startups in touch with a larger target audience.

According to McKinsey research, revenues in the fintech industry are set to grow nearly three times faster than the traditional banking sector between 2023 and 2028, and this will pave the way for more innovations for eCommerce startups to achieve their conversion targets.

Cutting-edge fintech solutions are increasingly shaping the eCommerce landscape. The arrival of artificial intelligence and blockchain is set to accelerate this aspect of digital transformation exponentially. With this in mind, let’s take a deeper look at seven fintech innovations that are driving eCommerce growth for startups:

Payment Flexibility

Digital transformation has empowered eCommerce startups to take their payment flexibility options to new levels, and this has been characterized by the availability of 0% interest online loans that can be directly introduced at checkout.

This has empowered consumers to make larger purchases and increased their options for large, one-off purchases.

Fintech has also helped to expand the ways to pay for online shoppers, opening the door to mobile wallets and even cryptocurrency purchases, paving the way for less friction throughout the checkout process.

Next-Generation Consumer Insights

Fintech tools can also aid businesses in analyzing significant volumes of data produced by customers, empowering startups to deliver more personalized experiences.

By combining customer analytics, eCommerce stores can work alongside fintech solutions to tailor marketing strategies and product offers at the point-of-sale (POS) to help encourage upselling and multiple purchases online.

Cross-Border Purchases

The world is becoming smaller thanks to the universality of social media and natural language processing (NLP), and this is helping eCommerce stores sell their products to international customers at scale.

Cross-border transactions have been streamlined with the help of fintech, with tools making complex processes like currency conversions, translations, and extensive regulations more manageable for consumers and retailers alike.

By supporting multiple currencies, real-time exchange rates, and localized payment options, fintech is helping businesses to expand their reach on a significant scale to sell virtually anywhere in the world.

Intelligent Invoicing

Fintech transformation in eCommerce isn’t just customer-facing. The technology is helping to drive innovation throughout every step of the supply chain, including accounts payable.

AP automation processes can help to speed up supply chain processes and ensure that there’s no danger of stockouts facing eCommerce startups. The best AP automation software also comes equipped with tools like intelligent invoice capture, which extracts essential data from a range of invoice formats to prevent instances of human error when paying vendors - many of which accept crypto payouts as well.

Enhanced Security

Fintech is also rapidly improving the security landscape surrounding eCommerce thanks to advances in its payment systems. The growth of biometric payments, which can encompass fingerprint, voice, facial, and retinal authentication, has helped to ensure that payment fraud is mitigated to the best of the technology’s capabilities.

For eCommerce retailers, these biometric authentication tools can be utilized alongside other email or mobile verification tools to offer two-factor authentication (2FA) or multivariate authentication for more expensive purchases.

Crucially, 48% of consumers state that they feel more secure when using 2FA to make purchases, and this means that eCommerce startups can build higher degrees of trust among their customers by embracing fintech-based security measures.

One-Time Use Virtual Cards

Another strategy to mitigate security risks for eCommerce businesses focuses on one-time use virtual cards, which are created on-demand for approved purchases within the business.

Because having a company credit card can open businesses up to the risk of fraudulent purchases, one-time use virtual cards can streamline B2B payments for retailers without the risk of essential company data falling into the wrong hands.

These virtual cards operate in a similar way to one-time passwords, and they actively reduce the risk of damaging data leaks that could see businesses end up paying for purchases they haven’t made.

For eCommerce startups, generating on-demand virtual cards can increase checkout efficiency, with less lag between procurement departments approving purchases and employees being able to complete transactions on the company’s behalf.

The Arrival of Blockchain

We’re also seeing blockchain technology herald a new era of decentralization within the world of eCommerce.

This innovation is set to transform supply chain finance and improve the transparency of transactions and product tracking through every step of the chain. This can provide a more holistic overview regarding the authenticity of materials and their ethical sourcing.

For many customers, ethical supply chain practices can be a significant draw for eCommerce firms, and having the ability to record and track every step of your supply chain using a blockchain means that you can offer greater authenticity to customers.

Driving the Future of eCommerce

The unity between the worlds of fintech and eCommerce is helping to drive an innovative future that’s more secure and flexible than ever before for customers online. This helps to improve the online shopping experience while upholding a stronger degree of transparency and trust between businesses and their consumers.

Startups can benefit significantly from these fintech innovations within the industry and maximize their growth potential in an increasingly financially interconnected landscape. This can help to break down age-old barriers in lead generation and form the foundation for sustainable online retail.

5 Big Data Use Cases that Retailers Fail to Use for Actionable Insights

Dmytro Spilka — Wed, 16 Oct 2024 10:31:22 +0000

Big Data in retail refers to large, complex data sets generated from new sources, such as customer transactions, website visits, and in-store interactions.

The global Big Data market is expected to grow to $103 billion (£79 billion) by 2027. This shows how rapidly retail businesses are recognizing the potential of Big Data analytics.

Businesses can use the insights from Big Data to transform their retail operations and gain a competitive advantage in the industry. This customer and operational data can help retailers optimize pricing, streamline operations, and enhance the customer experience.

However, there are many Big Data use cases that retailers fail to use, causing them to miss out on actionable insights that could help them make better-informed business decisions. This article will explore five Big Data use cases that retailers fail to use.

Targeted Marketing

Today’s consumers demand personalized experiences wherever they interact with retailers. In order to create targeted marketing campaigns, retailers must determine which customers are likely to want a particular product or service and how best to present it to them.

By taking advantage of Big Data, retailers can better understand their customers and easily segment them according to their preferences, purchasing and browsing behaviors, demographic information, and more.

Retailers can then create customer profiles which help personalize marketing campaigns for the needs and preferences of specific segments. Targeted marketing campaigns improve the effectiveness of advertising efforts and enhance overall customer satisfaction.

Inventory Management

Failing to have stock that meets the demands of customers might put retailers at risk of missing out on sales and even losing customers in the long run.

In fact, almost 31% of online shoppers are likely to switch to a competitor if the product they’re looking for is unavailable on their preferred site. This is why retailers must leverage a point-of-sale (POS) system to manage their inventory effectively.

A good retail POS can gather Big Data to help retailers understand customer purchasing patterns, peek into past sales, and predict future purchases. This enables them to optimize their inventory, leading to cost reductions.

In addition, accurate demand forecasting is critical in retail. Big Data analytics helps retailers analyze seasonal and market trends to predict future demand accurately.

Fraud Detection And Prevention

Due to the high volume of transactions and the diversity of products and services offered, the retail industry is particularly vulnerable to fraudulent activities. A study predicted that global merchant losses to online payment fraud will exceed $343 billion between 2023 and 2027.

Fraud can negatively impact retailers by damaging customer relationships and eating into revenue. Luckily, Big Data can be game-changing for fraud detection and prevention measures, such as transaction monitoring, identity verification, and fraud-related data analysis.

Big Data employs modern analytical tools and machine learning algorithms to help detect fraudulent patterns and anomalies that might be hard to spot otherwise.

Pricing optimization

Setting the right price for products is essential, as overpriced products can lead to lost sales and reduced profits.

Big Data enables retailers to analyze market conditions and trends, competitor pricing, and customer purchasing behavior. By leveraging this data, retailers can optimize pricing for various products based on customer demand, seasonal variations, and competitor pricing, allowing them to stay competitive and achieve maximum profitability.

Supply Chain Optimization

A retail supply chain is the logistics of a product, from raw materials to customer delivery. Therefore, everything from customer satisfaction to profit margins depends on how well optimized your supply chain is.

Big Data such as shipping times, inventory levels, and supplier availability enables retailers to optimize their supply chain to reduce costs, deliver products faster, and gain a competitive advantage in the industry.

Ready To Use Big Data For Actionable Insights?

Big Data is a game-changer in the retail industry. It allows businesses of any size to gain insights into customer behavior, make better-informed decisions, and gain a competitive advantage.

By leveraging the power of Big Data and employing the use cases explored in this article, retailers can unlock many benefits, including increased revenue, enhanced customer satisfaction, and more efficient operations.

As the retail industry evolves, embracing Big Data analytics will be crucial for growth and success.

Artificial Intelligence: Uses And Threats For Small Businesses

Dmytro Spilka — Wed, 06 Sep 2023 13:25:18 +0000

Artificial intelligence (AI) is the ability of a computer or machine to carry out tasks that usually require human intelligence. The technology allows machines to learn and adjust from experience and data inputs to perform human-like tasks such as conversing.

AI can power customer service chatbots, implement marketing strategies, predict advertisement performance and follow up with leads. This allows small business owners to find extra time to plan what’s next for their business, improve their products or services, or interact with customers.

Justin Fortier, CEO of FYC Labs, said: “AI has become a major trend as of late, but we are seeing it become an even bigger part of how small businesses run and operate.

“Small businesses are increasingly realizing they can use AI to reduce the time it takes to do a lot of menial, day-to-day tasks.”

59% of small businesses in the UK are already using AI in some form, whilst 23% plan to do so in the future.

So how can small business owners use AI to impact their business positively, and what are the threats?

How Is AI Helping Small Businesses?

91% of businesses using AI say the technology has made their business more successful. Here are some areas of a small business that AI can positively impact:

*Marketing *

AI can collect and analyse customer data such as behaviours and preferences so that small businesses can create personalised marketing campaigns and messages that resonate with their customers’ needs.

AI can produce a broad range of short-form and long-form marketing materials - such as social media captions, blog posts, newsletters, or infographics - in natural language, so it seems like a human wrote it.

Additionally, AI can augment human-created content by automatically editing the work or suggesting the most effective tone.

(Image Source: Fit Small Business)

Frank Vella, CEO of Constant Contact, said: “Small businesses and nonprofit organizations are using tools that leverage AI for better outcomes — to enhance their creativity, get closer to their target audiences and drive more sales volume.

“As their marketing sophistication grows, we also see them look to incorporate AI and automations into more areas of their marketing.”

*Advertising *

AI tools can learn which adverts are most effective for which audiences. Then, these tools recommend images and words that will perform best and how to structure the advert for maximum results.

Customer Experience

AI is a powerful tool which can be used in multiple ways to enhance the customer experience that a small business offers.

Firstly, many small and large businesses use AI-powered chatbots to tackle customer service issues quickly and efficiently, which reduces wait times and makes it easier to offer immediate customer service even if you only have a small team.

(Image Source: Infobip)

Secondly, small businesses can automate routine tasks such as sending order confirmations and following up on support tickets. This saves time and resources while ensuring consistent and timely communication.

Finally, with 4.6 billion smartphone users worldwide in 2023 and nine new smartphones launching in August 2023 alone, a mobile consumer experience is crucial for small businesses to reach their target audience in today’s digital age.

AI can help shape a mobile consumer experience by offering voice-activated searches, personalised recommendations, and enabling customers to make purchases effortlessly using their smartphone.

What Threats Does AI Have On Small Businesses?

High costs

Adopting AI can be costly for small businesses. The initial expenses include acquiring the necessary hardware, software, and infrastructure.

Furthermore, small businesses also need to consider ongoing expenses such as maintenance, updates, and training to ensure an effective and efficient use of AI.

Small businesses often need more financial flexibility, so allocating a portion of their budget to AI may not be feasible, putting them at a competitive disadvantage to large firms with more financial resources to invest in the technology.

Data gaps

AI technology relies on data to learn and adapt. So, if the data used for training is limited or of poor quality, it can compromise the accuracy and effectiveness of the AI system.

Small businesses may struggle to gather enough data for their AI systems to handle all scenarios or behaviours, unlike larger firms with vast data access.

Skill gaps

The development and deployment of AI involves complex processes such as data processing, algorithm selection, model training, and performance evaluation. So, adopting AI requires technical expertise and knowledge.

However, small businesses may have limited access to experienced professionals with the expertise needed, so they may need outside help with selecting the right AI tools or frameworks, integrating AI systems into their existing infrastructure, or effectively managing and maintaining AI solutions.

Unpredictability

AI systems can sometimes behave unpredictably, producing results or content that humans did not intend. This could result in unexpected costs, damages to the reputation of a small business, and a potential loss of customers.

Andréa Morris of Forbes said: “Billions of different weights influence any single decision of a large, state-of-the-art AI system. This currently makes it impossible for technologists to unpack and understand exactly why it did what it did. This also makes it impossible to track and predict, with any degree of certainty, what it's going to do next.”

Cybersecurity risks

AI systems are subject to cybersecurity risks due to their complexity and interconnectedness. This can be challenging for small businesses lacking the financial and technical resources to invest in robust cybersecurity defences.

Here are some cybersecurity risks associated with AI systems:

Data breaches. AI relies on large datasets for training and operation. These datasets can include sensitive information, so if unauthorised individuals gain access, it can significantly harm the business.
Adversarial attacks. Adversarial attacks involve manipulating data to trick AI algorithms, leading to incorrect outputs or compromised system behaviour. For example, attackers can alter image text, causing AI algorithms to misclassify objects and provide misleading information.

Kimberly West, founder and CEO of Magnum Opus Consulting, said: “The mishandling of customer data, either intentionally or inadvertently, can lead to a breach of trust, hefty fines, and irreparable damage to a company's reputation.’

Conclusion

AI is a potent tool that can assist small businesses in enhancing their operations, expanding their business, and staying ahead of the competition.

AI will have a significant impact on the future of business, and small business owners should take note of this and implement AI for work tasks that are taking up most of their time, such as dealing with customer queries online or creating social media posts.

However, it’s crucial to thoroughly assess your business needs and choose the appropriate AI solutions to meet those requirements. With meticulous planning and execution, AI can be a valuable resource for small businesses.

How Augmented Reality will Transform how we use our Smartphones

Dmytro Spilka — Tue, 07 Mar 2023 13:03:51 +0000

Augmented is set to be the innovation that will take center stage in both our personal lives and working lives in the future. But where do smartphones fit in this brand new virtual landscape? As the industry continues to grow into the world of entertainment and consumerism, we’re gaining greater insights into just how AR will shape our lives, and how the innovations it will bring will be powered by smartphones.

Reality technology and smartphones have developed an uneasy relationship over the years. We perceive AR as an experience that’s optimized through smart headwear to generate a more immersive and engaging experience for the wearer.

This has led to questions as to whether AR will ultimately retire the smartphone. After all, if you can browse the web, make calls, and communicate with friends and colleagues all within your field of vision, what use is a smartphone?

However, today the smartphone appears to be thriving in the age of augmented reality, and has utilized the technology to bring users cutting-edge experiences on their handsets. The future looks pretty promising for both smartphones and AR too, with more innovative experiences on the way for users to enjoy.

Augmented Reality will be a Major Disruptive Force

The immersive qualities of augmented reality will greatly improve the experience in the field of gaming and far beyond over the coming years, with a wide range of possibilities for the development of new applications utilizing AR technology.

Fundamentally, augmented reality has the power to make existing processes considerably more efficient while mitigating instances of human error.

For instance, in the manufacturing industry, augmented reality programs can digitally overlay instructions to assemble complex equipment, or display a list of potential troubleshooting issues associated with the machinery that you’re looking at. When operating machinery, augmented reality can share your field of view with senior personnel who can visually guide you by adding indicators onto your augmented display.

The utility of holograms as a means of conveying complex information in bitesize chunks means that technicians can always have the most important information available to them to follow as and when required–all with AR working alongside artificial intelligence to understand when to display the relevant information and how.

Augmented Consumerism

Although we may best know smartphone AR as the filters that we can add to images or apps like Pokemon Go, the technology is already transforming eCommerce shopping on handsets.

Thanks to artificial intelligence, our smartphone cameras are the perfect tool for AR programs. Our cameras are capable of understanding what’s in front of them, and are capable of delivering essential information surrounding any given object.

According to Jennifer Liu, director of product management at Google, this is significantly changing how search engine queries are processed. “Search isn’t just about the same old text input, website-suggestions output anymore,” Liu suggests.

Google has understood this changing sentiment towards searches. After all, many of our queries arise from something we’ve seen or another form of visual trigger. For instance, if we’re interested in a coat worn by a friend, it can be tricky to describe in a way that a search engine would understand–but with programs like Google Lens, it’s possible to identify the item visually. Google Lens would then overlay relevant information about the item and even offer shopping or styling recommendations to boot.

This, accompanied by virtual try-ons, is the future of shopping. As the world becomes increasingly remote, our traditional brick and mortar experiences will be replicated by augmented reality.

(Image: Sifted)

As Sifted data shows, there’s already a market for users willing to shop via augmented reality, and the volume of users willing to virtually try on goods is only likely to increase as acceptance grows around the world.

Furthermore, the emergence of 5G connectivity, and the rise of more competitively priced 5G smartphones is likely to make augmented reality usage more effective, with faster data speeds paving the way for a faster, more seamless experience.

Roots in the World of Gaming

As with many emerging technologies, we’ll see the latest evolutionary steps for augmented reality take place in the field of gaming.

Games like Pokemon Go and Harry Potter: Wizards Unite may be early iterations of AR gaming, the future appears to be exciting for the augmented video games market as the technology continues to grow.

One iteration of smartphone AR gaming, Knightfall, offers an immersive experience based around a 3D tower defense strategy game that can take place on your living room table, or any other real world location in-view of a smartphone camera.

As the technology grows, these augmented reality games will become more impactful, and the experiences they offer will be richer and more immersive.

For the coming years at least, smartphones will be the gateway to augmented reality acceptance and adoption among millions of users. Whether they’ll pave the way for a future built around mixed reality headsets remains to be seen, but for now, AR and smartphones have formed a symbiotic relationship that’s aiding both technologies to evolve and improve.

How Big Data is Forming the Cornerstone for the Happy and Productive Remote Businesses of Tomorrow

Dmytro Spilka — Fri, 24 Jun 2022 14:53:52 +0000

Workplaces have fundamentally changed for countless industries - possibly forever. The fallout of the Covid-19 pandemic has left many businesses operating on a purely remote basis, whilst others have settled in a more hybrid working setup. Whilst the transition from managing office-based employees to collaborating with remote teams has been tricky to manage for some employers, mounting evidence suggests that big data is evolving to smooth out the transition process and deliver a more productive working environment.

The byproduct of the pandemic has been a significant push towards digital transformation across a range of sectors. This has caused an acceleration in the development of digital tools that can help to better support work from home (WFH) environments.

The high volume of quantitative and qualitative insights provided by big data will be imperative for businesses to maintain the same level of understanding they have with their employees despite a distinct lack of face-to-face communication, and a potential loss of opportunities to have the kind of personal meetings that can help employers to take on board levels of employee happiness and comfort in their roles.

(Image: PR Newswire)

As we can see, the rate of growth for the field of big data is growing at a rapid rate in the wake of the Covid-19 pandemic. With an expected CAGR of 34.52% and incremental growth forecast to weigh in at almost $100 billion both set to take place between now and 2025, businesses are only seeing the technology in its fledgling stages.

Significantly, the big data market is currently fragmented, which opens the door to more innovation through competition among key players.

This is all invariably good news for the WFH landscape. It’s also worth noting that remote work is also maturing at a strong rate as the technology to support WFH continues to grow and businesses become more accustomed to the practice.

(Image: Vox)

Although the data above indicates that employees always believed they were more productive working from home, the figure referring to just how productive they are has climbed significantly from the early stages of the pandemic to today. Whilst respondents believed they were on average 5.6% more productive in a WFH environment in August 2020, this figure has grown to 8.9% by April 2022.

Whilst much of this is down to employees becoming more accustomed to their new working environments, it’s likely that technology has played a key role in supporting the transition over the past year and beyond.

It’s certain that big data will continue to evolve to play a significant role in supporting the mass shift towards remote work. With this in mind, let’s take a look at how the technology can support a happy and productive workplace:

Analytics to Form a Happy Working Environment

With the help of analytics, it’s possible for businesses to build a greater understanding of how their employees feel about their work environment and its impact on their productivity.

Through the use of big data, businesses can even assess the health needs of their employees - which helps to build better wellness programs to help their workers to cope better with the isolation of working from home, or by spotting personal issues that may be harder to identify on a WFH basis.

This data can help to identify more comprehensive insights into underlying employee medical conditions for a more bespoke attitude to each worker, it can also help to build wellness programs based on factors amounting to employee satisfaction, productivity, and turnover.

Big data software can also help employers to better understand employee needs and goals, as well as identifying the key factors that can help them to perform and remain motivated over time.

Platforms like Qualtrics EmployeeXM, Lattice, and Workday can all utilize data to provide more comprehensive insights that can improve employee retention, engagement, and productivity.

This data can also be applied actively to improve the quality of remote processes that companies are building. For instance, it’s through the use of these big data analytics systems that businesses can fine tune how their employees engage with information during presentations.

With the help of this insight, it’s possible for businesses to modify their collaborative meetings and approaches to presentations to optimize how they can convey data to workers.

Through the support of purpose-built platforms like Powered Template, employers can access professionally designed slide and data presentation templates to modify in a way that engages workers. Then, with the help of big data, these businesses can alter their slides and adopt new templates that correspond to employee feedback.

To help to see how Powered Template can actively support businesses through offering an array of slides that can correspond to their big data insights, let’s take a deeper look into the process of building an infographic via the professionally designed slides available.

Powered Template features 93,685 resources for individuals and businesses alike to access. Of these options, there are over 40,000 graphic templates which can be fully customized to suit both business and employee needs.

Once you’ve selected a slide that complements the analytical feedback from your remote meetings, you’ll be presented with a page that shows key information revolving around file compatibility, size, file type, and user rating. In the example above, we can see that the above infographic pack has gained a 4.5 star rating from 12 reviews - which is a solid indication that the content is high quality.

Whilst Powered Template features 10,000s of free options, the platform’s premium options can help businesses to fine-tune their approach to remote presentations with unlimited options from some of the internet’s most capable creators.

The beauty of Powered Template stems from its flexibility, and businesses can either opt for comprehensive annual subscriptions ranging from $99 for full access, or single one-month access to content for $19 per month. Alternatively, Powered Template also offers an On Demand service, whereby customers can buy 10, 20, 50, or 150 downloads in packages ranging from $24.95.

Although the world of remote work is still a mysterious one for many businesses, big data can work in tandem with other technologies to create a seamless experience for employees to remain happy and productive.

In utilizing technology to better understand employee needs and engagement, presentations and the sharing of data can be comprehensively and continually reviewed to ensure that they’re making the strongest impact possible. In relying on data in this manner, businesses can continue to create a positive environment for their employees in the WFH era.

How the Metaverse will Spark a Big Data Revolution

Dmytro Spilka — Thu, 05 May 2022 12:53:58 +0000

Interest in the metaverse is only beginning to hot up in the wake of some big moves by a collection of the world’s largest companies. At the pinnacle of metaverse fever we can see Mark Zuckerberg, who decided to rebrand Facebook into Meta in a bid to demonstrate the company’s commitment towards becoming a go-to entity in the new frontier of the internet. With the vast possibilities for how big data can be used by individuals and businesses alike, it’s little surprise that Meta is dedicating at least $10 billion towards building the company’s own vision of the metaverse.

The arrival of the metaverse has been heralded as a revolutionary change to how we interact with one another online, and will drastically alter what we can do online. We’ve seen clips of Mark Zuckerberg seamlessly jumping through virtual spaces whilst interacting with different people, whilst a Walmart rendering of what the company believed grocery shopping would look like in the age of the metaverse recently resurfaced.

Whilst it’s still unclear just what the metaverse will mean for those who’ll use it most, we can be sure that there will be inordinate amounts of data generated every second in such an immersive digital environment.

Today, businesses already have access to a wide range of data sources that can process and analyse customers and target audiences alike, and the metaverse is certain to increase the volume of information flowing through companies at an inordinate rate.

(Image: Statista)

To think that the future of the internet will generate masses of data every minute is exciting and worrying in equal measure. Above, we can see the amount of data we share online collectively in any given minute - which amounts to 69 million private messages sent via WhatsApp and Messenger, almost 200 million emails, and hundreds of hours of content engaged in and generated.

In a landscape as interconnected as the metaverse, there’s likely to be far greater emphasis on the sheer quantity and quality of data that users produce without even knowing. With this in mind, it’s important to monitor how our data will be used in the metaverse and just what we’ll be giving away.

Digital Fingerprints in the age of Interconnectivity

As more leaders in tech prepare for the metaverse, we’re beginning to learn more about what the future of big data may look like. In this new digital frontier, it’s likely that technology will be capable of far more than simply monitoring your purchasing and browsing history, or the conversations you have with friends and acquaintances online.

Through machine learning, tech firms may be capable of learning where, when, and for how long you’ve engaged in a specific product or service online - even if it’s just a glance. In a world where your every action can be observed, we’re likely to see a brand new era of hyper targeted and personalized advertising.

The metaverse is likely to be experienced predominantly through virtual reality eyewear, and the equipment that’s set to deliver the metaverse to a broad network of users will also be capable of monitoring our biometrically inferred data. This will be capable of understanding our eye movements, changes in how we navigate the metaverse, and alterations in our heart rate when exposed to different marketing materials or content. Essentially, if Web 2.0 is a place for big data, Web 3.0 will take these insights to the next level.

Although the metaverse will be experienced via virtual reality headsets, its vast interconnectivity will mean that we’ll be able to access the space via smart glasses and our own smartphones - which will evolve over the coming years to embrace multiverse biometric readings too. Today, we may already have access to the first devices that can possess the fast connectivity necessary to access the metaverse. As 5G ready handsets continue to become more affordable for consumers, we could already have easy access to a metaverse-integrated future.

These metaverse access points will generate vast amounts of raw data, which can subsequently be analysed to infer farm information surrounding users online whilst giving companies a greater level of understanding surrounding idiosyncrasies, emerging market trends, consumer behaviour and plenty more insight.

Living in the New Age of Data

With such a significant information revolution just around the corner, new regulations regarding how businesses access and use the information of metaverse users need to be imposed swiftly to avoid the emergence of intrusiveness or exploitation.

"In the metaverse, you may not be able to distinguish between authentic content you came upon in the world, augmented or virtual, and paid content that was injected specifically for you, personally targeted by AI algorithm. So restricting this is important," explained Louis Rosenberg in an interview with Insider.

The prospect of businesses creating underhanded marketing tactics to extract information from target audiences is a worrying one. In the metaverse it may one day be possible for entirely AI-generated avatars to operate as a machine learning algorithm to converse with users and adapt their marketing strategies based on their responses.

Whilst GDPR was built to offer some form of protection for European internet users from companies stockpiling their personal data, it’s clear that more protection is necessary in Web 2.0 - let alone in Web 3.0. With this in mind, the coming years are essential for leaders in the development of the multiverse to be clear about how their products will be used. In the next generation of the World Wide Web, transparency will be more important than ever.

The Future of Consulting and Legal Relationships Powered by AI

Dmytro Spilka — Wed, 30 Jun 2021 14:29:00 +0000

With AI becoming more prevalent in day-to-day operations, the word "consultant" may soon take on a new meaning. That's because these intelligent systems will be making decisions and providing insights that would have previously required human consultants.

The implications for the legal field are even more significant. The potential for AI to not only replace many current tasks of lawyers but also perform them automatically is real – and it's only a matter of time before it becomes the law of the land. These technologies can intuitively capture information in ways humans never could, as well as predict who might need their services down the road – all without charging any higher rates than their human counterparts.

(Image Source: Raconteur)

In some instances, the roles of consultants and lawyers will likely overlap more than we may expect. For instance, the average consumer who doesn't know his or her credit score and is unfamiliar with how it affects his or her financial goals may seek out a financial consultant to assist with their debt management. In this case, the consultant would be able to use machine learning to plug in various factors – like credit score and income level – to provide recommendations for how consumers should best manage and pay off their debts. This is far more insightful than what any existing credit scoring system can do for consumers today.

It's also worth noting that these AI-powered systems may be able to do this without charging a fee. This is because the credit scoring system is also owned by the same company that owns the financial consultant service, and therefore profits off of both sides of the equation. So in this example, it's unlikely consumers will need to pay a fee for such credit guidance.

The important question to ask at this point is: What do firms stand to gain from implementing such technological advancements? On the one hand, they can save money on human consultants while continuing to provide customised services for their customers. But there's a more significant opportunity when it comes to managing client relationships in ways that leave no room for error or discrepancy. And that's where the real value comes in.

Managing client relationships requires that firms have the ability to interact with clients on a personal level, answer their calls and emails, and provide appropriate advice on any questions they may have. But when it comes to clients who initiate contact with a firm, AI can have a major effect on how firms communicate with them – bringing human-like empathy and compassion to the conversation. This opens up whole new channels of communication with customers: those who just want to pay their bill and move along without any questions or concerns about more profound issues; those who want input on how other companies are performing; and even those who want help in figuring out how they should manage their taxes.

Imagine a world where every interaction with customers is real-time and personalised, where the two parties can speak in a way that's not filtered through a computer. This means a greater level of engagement, better communication, and more effective decision making, all while saving money on human resources.

How AI is used for consulting

AI can also be used for consulting, training, providing supervisors and managers with live information about employee performance. They can use this information to assess each employee's strengths and determine how they should act when faced with a particular issue or problem. This allows firms to give employees practical instructions or advice that leads to improved performance – something that's crucial in any service business.

Several companies have adopted this kind of system already, including Airbnb and Uber. But it's worth noting that much more could be done if firms could effectively integrate such AI into their overall business model. This is where precision marketing comes into play – using AI in marketing campaigns to help generate more sales leads and improve marketing conversion rates for existing customers.

(Image Source: Adverity)

In fact, many marketing firms already use some type of AI in their campaigns. Benchmarking is an excellent example of how this is done, as it's used to optimise an existing marketing campaign and ensure that it's more effective than before. This helps companies improve their conversion rates while maintaining the same level of satisfaction with clients. The ultimate goal? To help companies increase the value, they deliver to their customers over time.

How AI is used for legal relationships

For legal relationships, AI can be used to predict outcomes in various scenarios. Traditionally, this type of AI is reserved for financial cases where it's used to check whether a contract is legally valid or not quickly. This has been an issue because humans are sometimes found to have made mistakes. In fact, the legal profession has already experienced a shortage of workers in recent years due to the growing number of qualification exams and the increasing complexity that comes along with the job.

AI can also perform other tasks related to law-related issues, such as identifying missing files and improving predictive analytics regarding anticipated court dates or similar scenarios when it comes to court cases.

By using artificial intelligence, firms can offer a variety of legal services without increasing the size of their workforce. This will allow the legal industry to remain competitive and profitable in the long run, despite having peaked in recent years. It's also an essential step toward keeping up with the growing demand for these services as technology begins to change how consumers live and work, especially regarding how they manage their money and interact with one another.

Artificial Intelligence: Help or Hindrance and Should it Become Regulated?

Dmytro Spilka — Wed, 09 Jun 2021 14:32:38 +0000

Artificial intelligence has grown over the years and has been adopted in various smart devices and industries. Many have seen AI technology as a means of enhancing work processes. However, although it may help businesses and industries excel far beyond their organic reach, many concerns are circulating its security and regulation regarding personal data.

Many organisations are thought to be using AI as a tool to generate value. The value comes in the form of revenues. Many companies are planning to invest even more in AI responses following on as a form of their response to Covid-19 to help to accelerate digital transformation. The adoption could create a more comprehensive division between AI leaders and smaller companies struggling to capitalise on technology.

(Image Source: VentureBeat)

So the question remains, does AI have the means to create better online security, or will it have the opposite effect and increase security breaches, creating issues for businesses and large organisations in protecting customers data.

Help or hindrance?

According to AV-TEST institute, an astonishing 1139.24 million malware viruses were created in 2020. The graph below shows an apparent increase, year on year, in the total amount of malware that has been found. Growth in their numbers could be down to the rise in sophisticated technologies such as AI used in our daily lives but are not yet regulated.

(Image Source: AV-TEST)

AV-TEST registers 350,000 new malicious programs and potentially unwanted applications (PUA). The total distribution of threats over the last 12 months demonstrates a high percentage, 86.25% for malware and 13.75% for PUA. One factor to consider is how the pandemic has pushed for a global increase of online users, creating a spike in online shoppers and businesses moving to online platforms with increased traffic and threats like malware.

Unfortunately, there is no amount of human resources that can tackle the number of daily breaches and help prevent an attack that hackers create. Primarily because these new attacks and breaches are conducted with highly sophisticated technologies. Therefore, implementing the suitable types of solutions is imperative for businesses to avoid data leaks and breaches.

AI is one technology that can help by automating threat prevention and detecting threats, and responding to them to stop them. AI has a significant role in creating a robust cybersecurity system. However, we need also to understand that AI business tunes to AI for its qualities in helping to prevent attacks. Hackers also use AI to test their own malware, which allows them to bypass even the most advanced systems.

Fraudsters use AI in an advantageous and sophisticated way. The Internet of Things (IoT) devices are increasingly becoming a part of malicious botnets connected to the internet. Mobile bots are software applications that run automated tasks over the internet. They can perform simple tasks and at a much higher rate compared to humans.

Some bots are used by businesses to help to boost brands. However, the etch also allows criminals to automate attacks and increase invasion, increasing their capabilities against the detection system. Therefore, to help protect and avoid businesses from getting infected with malware, they need to fight back with the level of tech, if not higher.

AI and sustainability

AI-based systems can predict intelligent grid systems, which can help manage the demands and supplies of renewable energy. Doing so will create a better environment for all to live in whilst increasing sustainability. It will also optimise efficiency, reducing costs and cutting unnecessary carbon pollution generation, which is currently a big issue.

AI also has the ability to help conserve species across the world through acoustic monitoring. It can also help countries improve their recycling efforts with AI-powered robots with optical sensors to identify rubbish, sporting it for rapid recycling quickly.

(Image Source: KTH)

AI can make things better and create problems when used in a less sinister way. However, AI technology has been rapidly adopted over a range of platforms, creating cross-pollination and cross-contamination. Like all types of technology, there are beneficial ways and damaging ways to use them. As technology continues to progress, increasing our limitations, it will be interesting to see how criminals adapt as technologies such as blockchain technologies can make systems transparent without being centralised.