How to manage AI projects: From Proof Of Value to Production | Deeper Insights™

According to a recent report from McKinsey 50% of enterprise claim to have “embedded some form of AI”, however, “only 21% of respondents report embedding AI into multiple business units”. From our experience in building multiple Applied AI solutions for enterprise, we usually start with a Proof of Value. However in some cases, the POV doesn’t make it through to production, so we wanted to address the reasons why:

1. Misunderstood requirements. Make sure you know what the requirements are and what you are trying to solve/achieve from this implementation. The POV isn’t the place to refine the problem, but where you prove the value, so there has to be a clear problem you’re trying to solve or value you’re looking to create in the first place.

2. Lack of ownership. If you’re working with external parties, they will need clear communication as per point 1. However so will in-house teams where you’ll need buy-in from multiple department stakeholders to ensure your AI solution will be adopted more widely. Clear ownership by each department is crucial, but so is clear direction from one project lead. Get buy-in but be clear on who has the final say.

3. Poor change management. Understand which teams will be affected by the implementation of the new AI system and try to communicate the long term vision and objective. Engage people early on who will be using the product. They’re ultimately the ones who are affected, and also the ones who can make or break a POV.

4. Data modeling. The difference between generating the input data needed by a Machine Learning model for a POV and doing it continuously and at scale is important. The time and energy required to get the data needed are often underestimated.

In a POV, all forms of data modeling have to simplify the reality, therefore, some fidelity is always lost in the process. Ensure you cater for the drop in accuracy when moving to production, and have the right people on hand to retrain, and maintain the system while it deals with its new environment.

5. No definitive endpoint. A POV should end as soon as the desired objectives are archived, avoiding gold-plating the results to make it work if they aren’t successful. Be clear when starting the project on what the Acceptance Criteria will be so that your data scientists and engineers have a clear goal in mind.