Your AI Pilot Worked. Here's Why It Still Won't Scale.

Enterprise AI doesn't have a model problem. It has an infrastructure problem.

Organisations have spent the last three years validating use cases, running controlled experiments, and demonstrating what AI can do in the right conditions. The results are compelling. And yet Gartner estimates that 60% of AI projects lacking AI-ready data will be abandoned by end of 2026. The bottleneck isn't the intelligence. It's everything the intelligence depends on: fragmented data, unstable pipelines, and environments that were never designed to sustain AI at operational pace.

The cost of that gap is rarely visible in failure. It shows up in the initiatives that worked, and quietly never reached the decisions they were built to inform.

Closing the gap between experimentation and impact

At Ekimetrics, this is the problem we've been built around. Not more AI initiatives, but the conditions under which AI can actually deliver on its promise in production: consistently, across business leaders, across use cases, and under the pressure of real commercial decisions. That means unified data foundations that don't require rebuilding for each new initiative, deployment practices that industrialise what works, and environments stable enough that clients can trust the output when it matters.

This change in orientation drives a huge difference in outcomes. An AI system that performs well in a controlled environment and one that performs well at the pace of a live business are not the same thing. Bridging that gap requires as much rigour in the infrastructure as in the science.

A foundation built for production

Databricks sits at the core of that architecture. We use it to support scalability, streamline deployment, and maintain the kind of reliable production environments that clients can depend on for day-to-day commercial decisions, not just periodic reporting cycles.

Being part of the Databricks Built-On program takes our solutions further. It is not an integration badge. It means working directly with Databricks engineering to deepen how we use the platform, extend its capabilities in production environments, and stay at the edge of what it can do for our clients — a recognition shared by only a handful of companies globally, and as the only French company selected this year.

For our clients, that is what unlocking the full value of data and AI looks like in practice: faster decisions, stronger competitive positioning, and systems that hold under pressure.

What this means in practice

The organisations we work with are not short of AI ambition. What they are short of is AI impact that lasts — that moves from a promising result in a controlled environment to something that holds up under real operating conditions, integrates with how decisions actually get made, and compounds in value over time rather than plateauing after the first deployment.

It is also the foundation on which we build our marketing and commercial expertise, where the quality of the decision has a direct and measurable impact on business performance.

Scaling AI in production is not a technology challenge. It is an organisational and architectural one. That is where we have chosen to focus, and where we believe the most durable business value gets built together with a technology partner like Databricks.