The last mile of AI: Closing the gap between insight and action
AI delivers value when trusted data turns insight into action
by https://www.techradar.com/author/errol-rodericks · TechRadarOpinion By Errol Rodericks Published 16 September 2026
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For the past few years, the spotlight has been firmly on AI models.
Organizations have poured investment into generative AI, machine learning platforms and large language models. New capabilities appear almost weekly. Yet despite all this progress, a familiar question keeps surfacing in boardrooms and leadership meetings – where is the business value?
Part of the answer is that the technology itself is no longer the main obstacle. Powerful AI tools are now within reach of most organizations. What remains difficult is turning AI-generated insights into decisions and measurable business outcomes.
Latest Videos FromTechRadarWatch full video here: Errol Rodericks
Global Product Marketing Leader at Denodo.
This is the last mile of AI, where many projects lose momentum. A model may produce a recommendation in seconds, but acting on it is often far more complicated. Data may be incomplete, critical context may sit elsewhere, and governance teams may not be confident in the output.
As a result, many organizations find themselves investing heavily in AI while struggling to move beyond pilots and proofs of concept. The missing piece is often the ability to connect intelligence to the reality of how the business really operates.
The real AI bottleneck
When executives talk about successful AI programs, they rarely focus on the sophistication of the model. What matters is the outcome. For example, whether fraud losses fall, if customers can be onboarded faster, or if downtime is reduced. Those outcomes depend on much more than AI itself.
For AI to create value, it needs a clear view of what is happening across the business, enough context to understand what those events mean, and the ability to operate within governance guardrails. When any of those elements are missing, recommendations become harder to trust, automation stalls, and adoption suffers.
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