Early enterprise AI investment decisions were often evaluated heavily on first-year pilot results-a reasonable starting point, but one that can miss important dynamics that only become visible over a longer time horizon. A more disciplined approach that's become more common among experienced technology investment committees is explicitly modeling how an AI investment's cost and value are likely to evolve into its second and third years, not just its pilot phase.
Why the first year can be misleading
First-year AI deployments often benefit from vendor-subsidized pricing intended to secure a reference customer, from concentrated implementation support that won't be sustained at the same level once the deployment is considered complete, and from a small enough scale that data quality and edge-case issues haven't fully surfaced yet. Each of these factors can make first-year economics look more favorable than the steady-state economics a company will actually experience once the deployment scales and vendor support normalizes.
What a second-year model accounts for
A more complete investment evaluation projects vendor pricing forward past any initial promotional period, accounts for the ongoing cost of the human oversight a mature deployment will require at full scale, and builds in the likely cost of addressing data quality and edge-case issues that tend to become more visible as usage scales beyond an initial pilot group.
Why this discipline is spreading
Enough companies have now had the experience of a first-year AI pilot that looked strongly positive followed by a second and third year where costs rose and reported value growth slowed, that more experienced investment committees have started building this pattern explicitly into how they evaluate new AI investment proposals, rather than being caught by surprise by it again.
What this means for how AI investments get pitched
Investment proposals that address second- and third-year economics directly, rather than presenting only favorable first-year pilot projections, are increasingly viewed as more credible by investment committees that have seen the alternative pattern play out before. This is a meaningful shift in what a well-constructed AI investment case now needs to include.