GaneevEcoWhat a good report actually contains
A lot of what gets called an "AI opportunity report" in the market is really just an extensive list of things AI could theoretically be applied to somewhere in a business, with little differentiation between a genuinely high-value opportunity and a technically-possible-but-low-value one. A good fit-gap report does the harder, more useful work of actually narrowing that list down to what's worth doing first.
- A short, ranked list of specific use cases — not a long list of everything AI could theoretically touch across every department and function in the business.
- An honest "not yet" section — the ideas that are good but need more groundwork, such as data cleanup or process standardization, before they're realistically achievable without significant additional prerequisite work.
- A clear recommendation for what to do next, tied to a specific engagement or workshop, not a vague "continue exploring AI" that doesn't actually commit to anything concrete.
Signs a report is generic, not grounded
If the recommendations would read identically for almost any company in your industry, it wasn't built from your actual department interviews and your organization's specific manual-task inventory — it was built from a template with your logo swapped in, and it's worth being skeptical of any report that reads that generically regardless of how polished the presentation looks.
A genuinely grounded report should reference specific details from your own organization throughout — a particular team's workflow, a specific pain point someone described in an interview, a real number from your own manual task inventory. If those specific references are missing or feel generic and interchangeable, that's a fair signal to question how much of the report was actually built around your organization rather than repurposed from elsewhere.
It's worth asking directly, before commissioning any such report, exactly how the department interviews and task inventory will be conducted, since that methodology is what actually determines whether the eventual output is grounded in your organization's reality or assembled largely from a reusable template with minor surface customization applied to make it look bespoke.
It's also reasonable to ask for a sample of anonymized findings from a previous engagement before commissioning a new one, specifically to judge how specific and grounded that prior work actually was in practice. A provider confident in the genuine specificity of their process should be willing to share at least a redacted example, and hesitation on that particular request is itself a meaningful signal worth paying attention to.