The big mistake business leaders make with AI (and how to avoid It)
How businesses can ensure the investment in AI is successful
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After a couple of years of high excitement around the potential ofartificial intelligence(AI) to drive results for business, many leaders are now highly impatient to deploy the technology and have great expectations for whatAIcan deliver. Tech leaders are hopeful that AI can deliver everything from streamlined operations to game-changing improvements in the way the whole organization does business, and planned AI spending is rising 61% this year, according to new research. Business leaders need to maintain a firm grip on reality, and temper their AI enthusiasm with a grounded view of what the business really needs AI to deliver. In the past two years, many companies have invested in AI only to find that their proof-of-concepts have not delivered results. Getting the right results from AI investment requires careful thought beforehand, combined with precise attention to detail during the project itself.
The past two years have seen an unprecedented amount of technology hype around the potential of generative AI, so it’s all too easy to understand how a business leader could be tempted to ask their IT teams why they are not using generative AI right this second. The problem is that in those businesses, neither the leaders who are swept away in a wave of AI enthusiasm, nor their IT teams, really know how AI can deliver a business advantage. Before rolling out AI, leaders need to be certain that they are doing so for the right reasons (and not just using it because their competitors are).
The gap between exciting technology built in the laboratory and the day-to-day reality of businessapplicationsis very large, and it’s crucial not to fall into the trap of becoming over-excited about technology that has yet to cross that gap. Taking a short-sighted view and moving forward too early is how AI investments end up wasted.
WW AI Business Lead and Head of Lenovo AI Innovators Program at Lenovo.
Building the foundations
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-lineemployees. Does the business lack staff to talk tocustomers? 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.
What you need for 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 abusinessproblem, and then scale it accordingly. For example, a generative AIchatbotthat 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 organization, as AI projects often fail to be exciting to leadership until they grow to a certain size.
Is generative AI right for you?
Even experts who have worked in the field for many years were caught by surprise at how the launch ofChatGPTmade 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 aschatbots, knowledge discovery and content generation. It’s also highly useful in segmentation and intelligent automation and anomaly detection. For example, one leading UK Industrial AI & IoT technology company used 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.
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The companies getting the most from AI
Artificial intelligence is already helping organizations 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.
Computer vision also has an important role in allowing retailers to draw important insights from cameras in retail stores, far beyond simply dealing with theft or similar incidents. One current 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, computer vision helps make factories and laboratories more efficient and also safer for employees. For example, computer vision is already helping to conduct quality control checks on products, ensuring they are not missing any components, andmonitorsthe number of products coming off a production line in any time period, also scanning for defects. But even more importantly, new computer vision systems are helping to make factories safer, scanning for smoke and fire, while also detecting accident-prone machinery.
A sensible approach
With excitement swirling around AI and generative AI in particular, business leaders need to ensure their feet are firmly planted on the ground, and take a sensible approach to the technology. This means focusing on real, tangible problems within the business, and working out how AI can deal with those problems. It’s also key to ensure that AI projects are ‘woven into’ the business effectively: not only should AI integration be closely linked to real-life problems, but the AI project should also be something that as many employees as possible can be ‘hands on’ with. This sort of holistic, integrated approach is the way to ensure AI projects do not fail in their early stages, and a foundation stone to using AI to gain a true competitive advantage.
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Nicholas Borsotto is WW AI Business Lead and Head of Lenovo AI Innovators Program at Lenovo.
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