No. 2 problem: Unrealistic expectations lead to problematic requirements
Early planning and business case validation show that the requirements set for the project can’t be met, which then requires a period of redefinition before real work can start. This situation – reported by 69% of enterprises – leads to an obvious question: Is it the requirements or the project that’s the problem? Enterprises who cite this issue say it’s the former, and that it’s how the requirements are set that’s usually the cause.
In the case of the cloud, the problem is that senior management thinks that the cloud is always cheaper, that you can always cut costs by moving to the cloud. This is despite the recent stories on “repatriation,” or moving cloud applications back into the data center. In the case of cloud projects, most enterprise IT organizations now understand how to assess a cloud project for cost/benefit, so most of the cases where impossible cost savings are promised are caught in the planning phase.
For AI, both senior management and line department management have high expectations with respect to the technology, and in the latter case may also have some experience with AI in the form of as-a-service generative AI models available online. About a quarter of these proposals quickly run afoul of governance policies because of problems with data security, and half of this group dies at this point. For the remaining proposals, there is a whole set of problems that emerge.
Most enterprises admit that they really don’t understand what AI can do, which obviously makes it hard to frame a realistic AI project. The biggest gap identified is between an AI business goal and a specific path leading to it. One CIO calls the projects offered by user organizations as “invitations to AI fishing trips” because the goal is usually set in business terms (“improve sales/competitive position” or “reduce inventory cost”), and these would actually require a project simply to identify how the stated goal could be achieved. From that, it would be possible to frame an actual project to implement a strategy.
Why doesn’t this happen with traditional technology? According to enterprises, the big reason is that line organizations can experiment with AI, and draw conclusions about its benefit to them, without any IT involvement at all. In the past, with non-AI technologies, line departments tended to work with IT just to learn what could be done. “Early partnership with IT makes a big difference,” one IT professional with AI skills noted.
This particular problem, though, happens a lot less often for enterprise IT leaders who have a strategic vendor partner who has practical AI experience. A “strategic vendor” is usually one that has broad enterprise engagement and credibility. Combine that with AI skills, and you have a combination that can meld business and technology, which overcomes the problem of translating business goals to steps that can be implemented.
