The Key Steps Business Leaders Must Take To Avoid AI Projects Failing

It’s entirely understandable that business leaders have high expectations for artificial intelligence (AI) technology, and also that they should be impatient to get this technology to work. We have seen two years with a near-unprecedented level of hype around the technology, in particular around generative AI. AI spending is rising, with 61% of tech leaders planning to increase their spend this year, according to the latest  research. But it’s best to tread very carefully, and to ensure that the organisation approaches AI not with blind enthusiasm, but with a grounded view of what AI is expected to deliver. Over the past couple of years, too many companies have invested in AI, then found that their proof-of-concepts have failed to deliver. It is very possible to get the right results from AI, but it requires not only careful thought beforehand, but also attention to detail throughout the project. 

In the wake of the global frenzy around ChatGPT, some business leaders have become carried away with the hype around AI and challenged their IT teams to find ways to use generative AI right away, without waiting. But there’s a big problem with this approach. In those businesses, neither the leaders nor their IT teams have thought about how AI can really deliver a business advantage. Business leaders need to be certain they are using AI for the right reasons, rather than simply doing it out of the fear their competitors might get ahead. 

Many technologies look exciting in the laboratory, but the gulf between such technology and the day-to-day reality of business applications is vast. Above all, business leaders need to avoid getting over-excited about technology that is at the ‘exciting’ stage, but has yet to become really useful. This sort of short-sighted view leads to AI investments being wasted. 

Beyond the laboratory

Even the very best technology is just a science experiment if it cannot be adopted and used in the real world. The single biggest reason AI ‘doesn’t work’ for businesses is that people try to ‘do AI’ rather than identifying where problems or inefficiencies exist. To find such problems, business leaders should first talk to partners, and listen to consumers and front-line employees. Does the business lack staff to talk to customers? Does the business need to find a way to cut fuel emissions? Beyond the hype, the real excitement of this technology comes not from thinking about AI as a standalone solution, but by adding AI into the solution to a real business problem. 

Communicating success

All too often, the approach to AI is to have a specific ‘AI team’, rather than applying the technology across the whole business. This siloed approach is a key mistake. AI must be integrated with a holistic approach, and a view to scaling it across every part of the business. Business leaders must connect multiple teams together to initially implement the technology, and avoid cutting corners to ensure seamless integration. 

Business leaders need to design an effective proof-of-concept solution that includes AI appropriately in order to mitigate a business problem, and then scale it accordingly. For example, a generative AI chatbot that can answer niche questions could be made available to a small subset of customers initially, but rolled out to larger groups thereafter. Internal communication is also key as the business benefits of the proof-of-concept must be effectively communicated within the organisation, as AI projects often fail to be exciting to leadership until they grow to a certain size. 

The right kind of AI

Even experts who have worked in the field for many years were caught by surprise at how the launch of ChatGPT made the pinnacle of AI technology so easy to adopt. This, in turn, made it easy for business leaders to imagine that generative AI should be adopted universally. But they should pause to think about whether such technology is the right choice, or if other forms of AI might do the job better. 

The enthusiasm around generative AI has meant that it’s sometimes used in areas which don’t play to its natural strengths. Generative AI is great for conversational user interfaces such as chatbots, knowledge discovery and content generation. It’s also highly useful in segmentation and intelligent automation and anomaly detection. For example, Smartia, a leading UK Industrial AI & IoT technology company, worked with Lenovo to harness machine learning and computer vision AI technologies to enable its composite manufacturing process to be smoother and greatly reduce anomalies. This demonstrates how AI is already improving manufacturing quality control through various systems that accurately detect defects.

Reaping rewards

Artificial intelligence is already helping organisations to solve real problems in sectors such as retail and manufacturing. AI helps to streamline and speed up processes, eliminating the amount of time spent by employees on mundane tasks. In both retail and manufacturing, computer vision is emerging as an interesting and successful use of AI, linking the physical and digital worlds, and helping to spot defects on production lines and offering valuable insight in retail settings.  

Signatrix's AI solution uses computer vision to draw important insights from cameras in retail stores, far beyond simply dealing with theft or similar incidents. The system is able to offer insights into important trends around what customers are looking at and buying, and to validate the success of promotions. The system can identify everything from misplaced products to how retail media (advertising) within the store is performing in terms of views.

In manufacturing, Graymatics’ LabVista software uses computer vision to help make factories and laboratories more efficient and also safer for employees. LabVista conducts quality control checks on products, ensuring they are not missing any components, and monitors the number of products coming off a production line in any time period, also scanning for defects. But even more importantly, the LabVista system helps to make factories safer: the system scans for smoke and fire, while also detecting accident-prone machinery. 

Preparing for success

The rewards of a successful AI project are very real, but business leaders need to ensure that they take the right approach to the technology. This entails remaining focused on the real, tangible problems that AI can solve, and how to deliver solutions that work for the business. It’s also key to ensure that as many employees and parts of the business are ‘hands on’ with AI during the project. Taking this sort of balanced, holistic approach will help to ensure that AI projects survive from the drawing board through the tricky early stages, to become solutions which can deliver real and lasting value for the organisation as a whole.