After a few years of great excitement around the potential of artificial intelligence (AI) to drive business results, many executives are now very eager to deploy the technology and have high expectations for what AI can deliver . Technology leaders hope AI can deliver everything from streamlining operations to revolutionary improvements in how the entire organization does business, and planned spending on AI is growing by 61 % this year, according to a new study. Business leaders must keep a firm grip on reality and temper their enthusiasm for AI with a grounded view of what the business actually needs from AI. Over the past couple of years, many companies have invested in AI only to find that their proofs of concept have not delivered results. Getting the right results from an investment in AI requires careful thought upfront, combined with careful attention to detail during the project itself.
The last two years have seen unprecedented technological hype around the potential of generative AI. So it’s all too easy to understand how a business leader might be tempted to ask their IT teams why they’re not using generative AI right now. The problem is that at these companies, neither the leaders swept up in a wave of enthusiasm for AI, nor their IT teams really know how AI can deliver business advantage. Before deploying AI, leaders need to be sure they are doing it for the right reasons (and not just to use it because their competitors are doing so).
The gap between exciting technology built in the lab and the day-to-day reality of commercial applications is very large, and it is crucial not to fall into the trap of over-excitement about a technology that has not yet closed this gap. Taking a short-sighted view and moving forward too soon is how investments in AI end up being wasted.
Nicolas Borsotto
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WW AI Business Lead and Head of the Lenovo AI Innovators Program at Lenovo.
Build the foundations
Even the best technology is just a science experiment if it cannot be adopted and used in the real world. The biggest reason AI “doesn’t work” for businesses is that people try to “do AI” rather than identify problems or inefficiencies. To detect such problems, business leaders must first talk to their stakeholders and listen to consumers and front-line employees. Is the company understaffed to speak to customers? Should the company find a way to reduce fuel emissions? Beyond the hype, the real excitement about this technology comes not from viewing AI as a standalone solution, but rather from the fact that it adds AI to the solution to a real-world business problem.
What you need to succeed
Too often the approach to AI is to have a specific “AI team,” rather than applying the technology across the entire enterprise. This siled approach is a major mistake. AI must be integrated into a holistic approach and with a view to its extension to all levels of the enterprise. Business leaders should connect multiple teams to initially implement technology and avoid cutting corners to ensure seamless integration. Business leaders must design an effective proof-of-concept solution that appropriately includes AI to alleviate a business problem, and then scale it accordingly. For example, a generative AI chatbot capable of answering specific questions could be made available to a small subset of customers initially, but then rolled out to larger groups. Internal communication is also essential, as the business benefits of the proof of concept must be communicated effectively within the organization, as AI projects often fail to excite executives until they reach a certain size.
Is generative AI right for you?
Even experts who have been working in the field for many years have been surprised at how the launch of ChatGPT has made the pinnacle of AI technology so easy to adopt. This made it easy for business leaders to imagine that generative AI needed to be adopted universally. But they should take the time to consider whether such technology is the right choice or whether other forms of AI could do the job better.
The enthusiasm for generative AI means that it is sometimes used in areas that do not take advantage of its natural strengths. Generative AI is ideal for conversational user interfaces such as chatbots, knowledge discovery, and content generation. It is also very useful in segmentation, intelligent automation and anomaly detection. For example, one of the UK’s leading industrial AI and IoT technology companies used machine learning and computer vision AI technologies to enable its composites manufacturing process to be smoother and significantly reduce anomalies. This demonstrates how AI is already improving manufacturing quality control through various systems that accurately detect defects.
Companies making the most of AI
Artificial intelligence is already helping organizations solve real problems in industries such as retail and manufacturing. AI helps streamline and speed up processes, eliminating the time employees spend on mundane tasks. In retail and manufacturing, computer vision is emerging as an exciting and successful use of AI, bridging the physical and digital worlds, helping detect defects on production lines and providing valuable insights in retail environments.
Computer vision also plays an important role in allowing retailers to derive important insights from cameras in retail stores, far beyond just managing theft or similar incidents. A current system is able to provide insight into important trends in what customers are viewing and buying, and validate the success of promotions. The system can identify everything from misplaced products to how in-store retail media (advertising) is performing in terms of views.
In manufacturing, computer vision helps make factories and laboratories more efficient and safer for employees. For example, computer vision already makes it possible to perform quality checks on products, ensuring that no components are missing, and to monitor the number of products coming off a production line at any time, by also looking for faults. But more importantly, new computer vision systems are helping to make factories safer, scanning for smoke and fire, while detecting accident-prone machinery.
A sensible approach
With the excitement surrounding AI and generative AI in particular, business leaders need to ensure their feet are firmly planted on the ground and take a judicious approach to technology. This means focusing on real, tangible problems within the business and determining how AI can solve those problems. It is also essential to ensure that AI projects are effectively “integrated” into the business: not only should AI integration be closely linked to real-world problems, but the AI project should also be something on which as many employees as possible can be “involved”. ‘ with. This type of holistic, integrated approach is the way to ensure that AI projects do not fail in their early stages and is the cornerstone of using AI to achieve true competitive advantage.
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