The companies taking AI seriously are not the ones making the loudest announcements. They are the ones asking a harder question before they start: not what AI can do, but what the business actually needs it to do. That question leads to a different kind of project. It leads to narrower scope, more specific success criteria, and less tolerance for theatre in the evaluation process.
At Vinove, we have seen this play out across the portfolio. The teams that have gotten real, durable value from AI integrations did not start by exploring the technology. They started with a problem that was costing them time, accuracy, or customer trust, then worked backward to the question of whether AI was the right tool for that problem. In most cases where it worked, the answer was yes to a specific, bounded application, not to a general transformation programme.
The pattern in what fails
Organisations that struggle with AI adoption tend to share a recognisable pattern. The initiative starts broad: a mandate to bring AI into the business, a team assembled to explore possibilities, a timeline built around launch rather than outcomes. The work produces a demonstration that impresses in a controlled setting. Then it meets the actual workflow, the actual data quality, the actual users who were not consulted during the design phase, and the gap between the demonstration and the real operating environment becomes visible.
A successful demo is not evidence that a system will work in production. It is evidence that someone built something that works under controlled conditions. The gap between those two things is where most AI projects fail.
This is not a technology problem. It is a decision problem. The decision to treat AI adoption as a category rather than as a specific answer to a specific question creates the conditions for that gap. The technology is capable enough. The issue is whether the problem was ever defined clearly enough to know what success looks like.
What the 18-month view requires
Vinove applies the same test to AI that it applies to every product decision across the portfolio: does it work where it counts, and is someone still depending on it 18 months after launch? That framing changes what you build. It shifts the question from "can we demonstrate this capability?" to "can we build something that holds up in daily use, with real data, at the hands of people who were not involved in designing it?" Those are harder questions to answer. They take longer. They produce less impressive announcements. They produce more durable software.
