Denodo Executive: AI’s Real Bottleneck Is Turning Insight Into Action

According to the article, organizations have spent years investing in generative AI, machine learning platforms and large language models, with new capabilities appearing almost weekly. Yet leaders keep asking where the business value is. The technology itself is no longer the main obstacle, the article said, because powerful AI tools are now within reach of most organizations. The difficulty is turning insights into action.

TechRadar said many AI projects lose momentum in this last mile. 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, organizations can invest heavily while struggling to move beyond pilots and proofs of concept. The article said the missing piece is often the ability to connect intelligence to how the business really operates.

Executives discussing successful AI programs rarely focus on model sophistication, according to the article; they focus on outcomes such as lower fraud losses, faster customer onboarding or reduced downtime. Those outcomes depend on more than AI itself. AI 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, the article said.

The article identified the real bottleneck as sitting between the model and the business process. Data is fragmented across systems, essential context is missing, information often arrives too late to be useful, and different teams define the same business concepts in different ways. These are not new problems, but AI has exposed them more clearly, TechRadar said.

In financial services, the article cited one institution that had invested heavily in AI-driven fraud detection. Investigators still struggled to act quickly because customer records, transaction histories and external fraud signals were spread across multiple systems. Once those sources were brought together in a trusted, governed view, fraud losses fell, false positives declined and onboarding processes became more efficient, according to the article.

TechRadar said AI depends on a broader view of the business than traditional analytics. Most companies have spent years investing in ERP platforms, CRM applications and operational databases, which remain authoritative records and provide much of the information AI needs. But AI increasingly needs information from outside those core systems, such as supplier data to understand a supply-chain disruption, partner, SaaS or external intelligence sources for context, and live operational signals such as customer interactions, fraud alerts, connected devices or event streams.

The article described three layers of information: authoritative data held within core enterprise systems; contextual information from partners, suppliers and external sources; and real-time operational awareness. Most organizations have made significant progress managing the first layer, but far fewer have found an effective way to combine all three, it said.

A global manufacturer cited in the article could predict equipment failures with AI models, but critical information about production schedules, supplier delays and maintenance activities sat across different systems. Connecting those sources gave teams the context needed to identify risks earlier, reduce downtime and make better operational decisions, according to TechRadar.

The article said AI often operates with an incomplete picture of reality. It may understand what happened yesterday but not what is happening now, and it may have access to internal records but lack the external context needed to make a confident recommendation. That is where the last-mile challenge starts to emerge, TechRadar said. It added that the rise of AI is changing how organizations think about data sharing.