Data & AI skills for industry, energy and logistics

In industry, energy and logistics the data is operational before it is analytical. Sensor readings, maintenance logs, load profiles, route and delivery data, safety records. It exists in volume, it is often locked in systems that were never designed to talk to each other, and the people closest to it are rarely the people with the dashboards.

That produces a specific gap. Head office builds models and reports; the planners, engineers and site managers in the operating companies have the context that would make those models right, and no shared language to hand it over. A program that only trains the central analytics team widens that gap instead of closing it.

Add the spread: multiple operating companies, multiple countries and languages, and a calendar that is set by the operation rather than by a training plan. Plus a safety culture that quite rightly asks what happens when a model is wrong, which is why AI work here has to arrive with governance already attached.

The behavior we are after sits in the operating companies, not only at head office: a planner or engineer who understands what a model is flagging and says out loud when it does not match what they know from the operation. That single habit is what turns an analytics investment into something that works, and it is exactly the habit a head-office pilot never builds.

It needs knowledge of how the data got there and what it leaves out, skill in the tools people already have, practiced in the browser rather than watched, and a shared language between the operating companies and the analysts so context actually gets handed over. Where a client has its own AI policy that policy goes into the program, so what people learn and what they are allowed to do are the same thing.

Adoption across sites and countries is the hard part. Champions per location, live sessions planned around the operational calendar, and a handover to your own L&D so the community outlives the program. Growth is measured from a baseline before the start and benchmarked against other organizations.

The SHV Energy Data & AI School is our largest partnership to date. It kicked off in the summer of 2026 and reaches up to 6,000 employees across countries including the Netherlands, Italy and Brazil, with SHV Energy's own AI policy built into the program rather than sitting beside it.

At PostNL we have launched Effective Prompting for Everyone, rolling out to 4,000 colleagues who get hands-on experience with AI and learn to use it in their own day-to-day roles.

It also helps that we have sat on your side of the table. Our co-founder Nanne Veldman spent years inside a global energy group before starting Data Booster, which is why this program is built around operating companies, sites and countries rather than a single head-office pilot.