Each day, managers of large companies are told they need to make an immediate and substantial investment in AI. “Innovate or die”, they are told. In the media they are shown the latest and greatest accomplishments in AI. At home they browse Amazon and in one-click they have a new gadget delivered to their door in a day (or sometimes less). With all of the hype, noise, and uncertainty, managers may be tempted to invest in AI moonshots and try to skip several elementary- but important- steps in their AI transformation.
There are several examples of this happening. One of the most prominent comes from IBM. In March of 2012, IBM entered an agreement with a well-funded US hospital to begin developing an AI application. This application would help physicians diagnose and treat cancer. The announcement came shortly after IBMs Watson had defeated the greatest jeopardy player of all time and the hospital received a $50million donation to pay for the project.
Four years later, an auditor’s report revealed that the project had cost $62million to date and had not been used to treat a single patient. Even more worrisome, the project was not integrated with any of the hospitals systems and could not be used. After the emergence of this report, the CEO of the hospital submitted his resignation and the project was cancelled. Although the project had a noble cause, the hospital did not have the project management capabilities, nor the operational infrastructure to pull off such a difficult feat. There is another side to this story, though.
During the time that the project was underway, another department in the hospital was also developing products using AI. This team was pursuing projects such as a ‘care concierge’ that makes recommendations to patient families, a supervised learning algorithm that would predict patients who may require financial assistance, and an automated IT support system. None of these received the hype or attention of the larger budget project, but all of them successfully added value to the patient experience and saved the hospital money.
With the success of these smaller AI projects, the hospital is now prepared to re-engage in larger, more aspirational goals. The hospital has a strong model development infrastructure, technical and non-technical staff with AI project experience, and all of the learnings from their initial failures. This has led them to take on a new initiative that looks at patient data alongside their genomic profiles, and provides suggested treatments.
The takeaway is not that AI moonshots or aspirational goals should be ignored and left unfunded. It instead illustrates that while large budget, aspirational projects may get the most hype and coverage, organizations early in their AI transformation may not be ready to deliver. Companies who are successful in transforming their businesses have many less ‘sexy’ projects going on under-the-hood and these are what slowly, but surely lead to the achievement of revolutionary transformations made possible with AI.
Managers need to tune out the noise. They need to reduce their anxiety about moving too slowly and see value in moving at all. When it comes to successful AI transformations, it's much better to be the tortoise than the hare.
