Eric Siegel, author of Predictive Analytics and the Chair of Machine Learning Week, had a great article on Harvard Business Review recently – The AI Hype Cycle Is Distracting Companies. You should read it, as he makes a lot of great points about AI hype and its dangers. One comment, in particular, stood out for me though:
Most practical use cases of ML — designed to improve the efficiencies of existing business operations — innovate in fairly straightforward ways. Don’t let the glare emanating from this glitzy technology obscure the simplicity of its fundamental duty: the purpose of ML is to issue actionable predictions
This focus on improving existing business operations in a straightforward way is critical. We see a lot of companies spending a lot of money on ML and AI. Much of it is wasted because the ML/AI team, keen to show how smart they are and to justify the investment, insists on putting all their effort into “transformational” projects or “new businesses”. The potential for ML to improve their current business in meaningful but boring ways is ignored. These ML/AI teams are often more focused on using the coolest technology, so they will be hired by bigger companies and be given bigger budgets, than they are on delivering business value NOW.
In contrast, successful ML/AI teams are ruthlessly focused on incremental improvements – taking well understood problems in the business and using machine learning to improve results in each area in a very focused way. Often the improvement is small at a per-transaction level but the team focuses on high-volume problems, multiplying that small improvement by very large numbers of customers, products or transactions.
Not only does AI hype tend to distract from these very practical problems, it tends to result in a model-first or technology-first mindset. It becomes more important that the project uses AI than that it generates results. As Eric goes on to say:
This exacerbates a significant problem with ML projects: They often lack a keen focus on their value — exactly how ML will render business processes more effective. As a result, most ML projects fail to deliver value.
Our experience is that you really need:
- A clear understanding of what decision needs to be made differently to generate a result
- A detailed awareness of how exactly your ML model will influence that decision
- A sense of what organizational change will need to happen to get from the current decision-making approach to the new one.
We ensure this on our projects using decision modeling and our DecisionsFirstTM Approach to projects. This means we always know how the decision is being made, and can automate most of it, before we start applying ML to improve it.
If this is a topic that interests you, why not come to Machine Learning Week? I’m speaking on the Tuesday to kick off the business track (Step 1: Setting Machine Learning and AI Projects Up for Success) and giving a workshop (Machine Learning Operationalized for Business: Ensuring ML Deployment Delivers Value) on the Monday. Or drop us a line at Decision Management Solutions and learn how we can help you directly.
