Aug 22 Written By Dan Rose Johansen
I recently surveyed danish CIO’s(Chief information officers) about their relationship with AI and I had some interesting results. One of the results was that one of the biggest barriers to get started on AI projects is that building the business case is difficult. I completely understand the issue and I agree with the CIO’s. Building an AI business case is difficult and if you try to build it as a traditionnel IT business case it’s down right impossible.
Building a business case is all about understanding the cost and revenue drivers well enough to work them into a model that yields a profit with high certainty within an agreed timeline. When building AI solutions or even buying them off-the-shelf that whole process turns out to be way more challenging than what you will experience with traditional IT-projects. In my experience this is for many a lesson hard-learned by many in the IT business that naturally grabs their well-known tools and methods but quickly fails. This often results in AI being disregarded as being a too immature technology. With the right approach, that I’m going to show you here, you can actually build a business case that makes sense. The technology is ready and at a stage where most businesses can successfully utilize it. New technology just requires new approaches.
Before moving on to how you build an AI business case, let’s understand why this is such a difficult task. The reason is simply, that everything in AI is experimental in its natural form and as a result nothing is predictable. How much data you need, what algorithmic approach will work and how good the result will be is very difficult to know beforehand. You can look at a similar project but small differences in the problem, the data or the environment will often to much surprise make a big difference. So knowing the exact costs, results and the road there is just not possible.