Strategies for Turning AI Pilots into Measurable Impact (Closing the AI ROI Gap)
A familiar trend for many executives occurs when an AI pilot provides evidence that it may have potential; a prototype performs effectively in a clean and controlled environment until all progress subsequently freezes in place.
This situation is referred to as the “AI ROI Gap” and exists between the potential of AI and the actual results achieved.
Scale is part of this problem; an AI model may perform at a very high level when tested against the set of Clean, Defined, and Curated Data, but may struggle when introduced into the real-world environment’s inherent data variability. Additionally, when integrating AI into the existing Infrastructure Systems, there can be additional sources of friction that were not apparent in the initial tests.
The much larger challenge, however, is often related to misalignment. AI initiatives are often operated in a silo, and disconnected from the operational goals of the company that fund them. This means that even though an initial pilot may have shown great promise, there will be little or no value after the AI has been deployed.
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A much more value-centric approach is to begin with well-defined and measurable use cases. As an example, using a defined percentage of customers still engaged as a retained customer versus having the metric of improving customer retention.
The other important aspect of establishing value from AI is to define who is responsible for the results achieved after the AI has been deployed. If there is no defined accountability, the AI loses its momentum.
All these issues can be compounded by the question of how much time the initiative should be given to achieve true value versus a time when the initiative is finished, as some AIs take a fair amount of time and iterations to fully integrate into operations.
Despite these challenges, it is evident that as the pressure for accountability increases, and the magnitude of investments in AI such as these becomes more substantial, the successful organizations that are bridging the gap will treat their investments in AI as a core competency of the business, and not necessarily as an experimental program.
Though not a substantial change, this change makes an immense and lasting impact on how Al is used.
