Bridging the hidden gap between data and decisions in the age of AI

Everywhere you turn, the conversation about AI includes the same message: success depends on good data. It’s become the mantra of every boardroom and conference stage.

Companies invest millions in cleaning, tagging, and organizing data with the belief that once it’s right, AI transformation will follow.

But that belief is incomplete. Cleaning and collecting data is step zero. Without the engineering, architecture, and operational readiness to use it, even the cleanest data set won’t move the business forward.

Most companies are trying to cross the finish line without actually building the car.

A Gartner survey found that 63% of organizations either don’t have or are unsure if they have the right data management practices for AI.

But even if companies don’t know where to start to get from data to AI transformation, there’s a straightforward strategy that any organization can use to produce business outcomes.

Why progress stalls at step zero

Progress stalls when there’s a gap between any of the layers between data and activation — strategy, engineering, modernization, visualization, and readiness. Some organizations write an ambitious data strategy that never links to measurable business outcomes.

Others collect and store vast amounts of information without a plan for how it will flow between systems. Most often, legacy IT infrastructure makes modernization nearly impossible, while data teams remain siloed from the decision-makers.

Gaps in skillset or experience are another frequent hurdle. Companies may have data analysts who can interpret dashboards, but lack data engineers and architects who can build the pipelines and governance structures that make insights reliable and scalable. When there’s a lack of talent available, organizations remain stuck on one piece of the process.

This blocks more than just a deeper understanding of the numbers; it’s preventing innovation inside these companies. Nearly half of the executives in a survey from IBM said data concerns remain a barrier to agentic AI adoption for their organizations.

When teams can’t trust their data, they can’t use it as the foundation for an AI strategy, even when there’s pressure from the top. AI may be the flashy ...