Implementation of enterprise artificial intelligence is becoming increasingly popular. However, many companies face the fact that it is much simpler to implement a working pilot project than make it beneficial and operational.
Success in demonstrating a capability does not mean success in implementing it in the company's business process and making sure that there is ROI and scalability.
1. The Problem Is Not Defined
One of the most widespread pitfalls is that companies tend to choose technology instead of the problem they have to solve.
An enterprise can implement any new AI-based technology (like generative AI, predictive analytics, or automation). However, without having a specific operational problem and goal to solve with the help of the technology, it is hard to say whether the pilot has been successful.
What one needs is a particular problem: decreasing downtime of equipment, improving forecasting, automating document processing, increasing the level of operational visibility.
2. Poor Data Quality
Data quality is vital for any AI system to perform well. The information of a business is scattered in different places like ERP, spreadsheets, databases, machines, sensors, legacy applications.
The pilot might be successful on a carefully curated set of data but fail to cope with the poor quality of data in actual practice.
Thus, data integration and governance must be kept in mind right from the start.
3. AI Is Not Integrated With Workflows
The other challenge here is designing an AI application that works independently of the tools used by employees.
A correctly predicted value becomes worthless if the next step cannot be determined.
Good AI solutions help people act on the insight immediately.
4. ROI Not Determined
Executives require more than a good demo. They require proof that the technology will make their business better.
Some useful KPIs may include less downtime, less costs, greater speed, improved asset utilization, increased productivity, or greater customer satisfaction.
Without KPIs, the decision to move beyond the pilot project into production may be tough.
- Scalability Comes As An Afterthought Pilot projects succeed with a small team working in a controlled environment but fail at scale when applied to multiple locations.
Issues such as scalability, security, integration, monitoring, and model performance need to be evaluated prior to the pilot becoming a production requirement.
From AI Pilots to Business Results
In our view at Aperture Venture Studio (https://apertureventurestudio.com/), the first step towards AI success is finding actual business problems. Using AI together with IoT may give you practical implementations like predictive maintenance, assets monitoring, operations analytics, and workflow optimization.
The point here is not just to show that your AI is working. You need to create an application that can be used and measured by people and businesses.
Enterprise AI needs the right combination of technology, data, people, and processes.
To learn more about AI and IoT ventures, see https://apertureventurestudio.com/.
And the key takeaway from our experience is this one: AI pilot implementation is just the start of your journey towards measurable business value.
Top comments (0)