The clamour of anticipation around new applications for artificial intelligence is as fevered as ever. The problem for me is that expectations are not informed by a robust appreciation of the practical requirements for innovating with AI. As an adviser to businesses on bringing such innovation to market, my advice is simple: to scale rapidly, large-scale AI-enabled projects must be built on firm foundations to allow multidisciplinary development teams to thrive. Chief among the reasons is that, in engineering terms, developing AI is a complex, non-linear process. Frankly, you can expend a great deal of time and effort with very little progress to show for it.

Given all the relentless hype about its artificial intelligence and its transformative potential for healthcare, it would be understandable if some health systems might be casting about in search of AI or machine learning projects they could try. But that sort of rushed, ad hoc approach is precisely the wrong one to take, says Tushar Mehrotra, senior vice president of analytics at Optum. "The only way you are going to get value out of AI is to link the clinical or business problem to the organization's overall strategy and make sure you have a rich enough data set to train the model so it generates actionable insights," said Mehrotra. "Making sure you are building and designing your AI effort the right way means putting in the work up front to create a clear understanding of what you are trying to solve so it can be embedded in the decision-making workflow," he said. "Too often, AI projects start with a quest for academic insight."

One of the biggest mistakes IT champions make when pitching trendy transformative technologies like AI, blockchain or quantum computing to executives is not doing their homework. They often fail to identify the problem they are trying to solve, determine whether it's worth solving and understand whether their company is positioned to solve it. This article proposes a simple four-part framework your champions can use to assess AI readiness so stakeholders can get a good early read on the likelihood of success before you invest too much time and effort in AI initiatives. To animate this framework, I will apply it to a real-world scenario -- assessing organizational readiness for AIOps, or the application of AI technology to transform IT operations. The framework comprises four elements.