Most data warehouse projects fail. Not because the technology is wrong. Because the design is. Three weeks for a number that should take three minutes. AI agents generating plausible reports nobody can trace. Two ERPs naming the same metric differently. The spreadsheet swamp. The fire drill before every audit. These problems live in the warehouse layer, in how data is modeled, governed, and made available to the people and AI agents that read from it.

There is no shortage of ambition when it comes to AI. It shows up in every boardroom conversation, every strategy document, every budget cycle where AI is no longer a novelty project but a line item with real expectations attached to it. Yet, very few organizations actually execute AI in a consistent, repeatable way that’s tied to reliable business outcomes. The problem with readiness is that we tend to treat it like a milestone: something you reach and then move on from.

You turned on an AI feature in your analytics tool. It surfaced an insight about your pipeline. You looked at it, paused, and closed the tab because you weren’t sure the number was right. AI-ready data would have made you forward it instead. It’s data that is clean, structured, and governed consistently enough that an AI model can reason about your metrics without a human translating or reconciling them first.

Every analytics vendor claims AI. Few can prove their AI is doing real analytical work. Here is what executives need to verify before committing budget to an AI-powered analytics tool.

As regulatory frameworks such as the General Data Protection Regulation (GDPR), Digital Operational Resilience Act (DORA), and Network and Information Security Directive 2 (NIS2) converge with the US Clarifying Lawful Overseas Use of Data Act (CLOUD Act), contractual assurances are no longer a sufficient defense. For senior leadership, digital sovereignty has evolved from a compliance checkbox into a core architectural requirement.

Every customer interaction generates signals that matter—a failed checkout, repeated form errors, a frustrated support call, a confusing AI agent exchange, or an unresolved email thread. Individually, these are isolated events. Connected, they reveal customer intent, friction points, operational risk, and opportunities for action.

Engineering is in the middle of an almighty shift. Thanks to AI code-generation solutions, Engineers are being asked to take on a different and wider set of responsibilities in order to be more productive. It’s what’s increasingly being coined as Agentic Engineering - using AI agents to accelerate engineering & operations work while maintaining human oversight, quality and rigour.

“Garbage in, garbage out.” We are not the ones who said this, George Fuechsel did. But when we are talking about AI today, it is hard not to repeat it. We spend a lot of time discussing what AI can do, the outputs, the predictions, the impact it can create. Much less attention goes to what is actually going into these systems.