Imagine if AI had access to hundreds of expert analyses about your business — not isolated reports, but continuously updated, PhD‑level insights available only within your organisation. What questions would you ask? Which decisions could be made faster, and with greater confidence?
Not realising the full value of AI
Extracting insights from AI‑driven solutions and acting on them is becoming business as usual. Yet many organisations still fail to unlock their full value. Why? In most cases, the limitation is not the technology itself, but the absence of holistic, cross‑functional governance of their own data.
In a previous article, I explored the vital role of data products in future generative analytics and business intelligence. Data management capabilities embedded within business domains create trust and provide the necessary context for enterprise data. From this perspective, well‑governed data — structured as certified data products — can be seen as an internal knowledge library tailored to the organisation. Modern AI tools are now able to analyse and reason over such “knowledge” at a scale and speed far beyond human capability.
A critical foundation for AI
With some industry‑specific and regulatory exceptions, organisations do not need to launch extensive research programmes to extract value from their data. What is essential is investing in processes to govern, assure quality and share data across domains and organisational boundaries. This provides AI systems with a trusted foundation to generate insights that can drive outcomes such as new services, more efficient operations, improved risk management and future‑ready organisational models. In this sense, well‑established data products act as trusted knowledge assets for AI — with clear ownership and a well-defined context.
Key to unlocking AI value
Crucially, the focus should not be on technology alone — it is already available. The real challenge lies in establishing and embedding cross‑functional processes that ensure continuous data quality, clear ownership and a shared understanding of data across the enterprise. Without this foundation, the benefits of AI initiatives will remain limited, regardless of model sophistication.
This is not an easy task and requires broad organisational engagement. However, it is essential in an increasingly data‑driven world. The ability to establish effective data governance across all domains that produce, manage and use data may well become one of the most decisive strategic differentiators in the years ahead.