AI’s challenge starts with definition. We hear all the time about how AI raises productivity, and many have experienced that themselves. But what, exactly, does “productivity” mean? To the average person, it means they can do things with less effort, which they like, so it generates a lot of favorable AI stories. To a business, though, it means more revenue-generating output per unit of input, such as labor, capital, and materials. It means a business case, which any tech project requires working with the CFO to prove.
AI projects have to prove that they can transform personal productivity feelings into business value. Enterprises tell me that isn’t being done very often with AI because there’s no IT project, so no testing of a business case. Right now, most of AI is driven by the same force that drives “citizens development” things like low- or no-code. Line departments just expense access to a cloud-hosted AI service or tool, and the whole process of justifying costs and proving benefits is skipped.
With AI, you have to use the data that’s available. Citizen AI is almost always divorced from the core business data that defines how a business operates. Without that, how valuable can AI be in making business decisions? Data governance policies rarely allow this data to be hosted in the cloud, much less used to populate a giant AI model, so the cloud AI tools are left with the task of helping with emails and reports, and that’s not likely to transform business operations or make a business case. Enterprises are almost unanimous in saying that creating a real impact on the bottom line means gaining new insights from the core business data.
