Turning AI Ambition into Enterprise Execution

THE CHALLENGE

Everyone wanted AI. Nobody agreed on where to start.

A global Fortune 500 life sciences and agriculture company had made AI a priority. Across HR, Finance, IT, Procurement, and Facilities, teams were already running experiments. The energy was real.
But three questions had no answers:

• Where should AI create value for employees?
• Who decides and how?
• What does a coherent employee experience look like across functions?

An AI platform was already being built. But teams were designing their own pieces of it independently. Each function had its own ideas, its own priorities, and its own version of what good looked like.

The risk was not that AI would fail to launch. It was that AI would make things worse. Faster, but more fragmented. What the organization needed was not more technology. It needed a clear strategy and someone who could turn that strategy into something real – a future oriented enterprise self-service user experience.

OUR SOLUTION

Start with clarity on intent. Build everything else from there.

We joined as Enterprise AI Strategist & Service Experience Lead to take the organization from scattered experiments to a coherent, executable AI program. There was no rulebook for this and no clear starting point. So we started with a simple but powerful shift in perspective.

Stop designing for the org chart. Design for the intent.

Most companies organize their services around how they are structured internally. HR owns HR things. IT owns IT things. Finance owns Finance things. However, employees do not think that way. They just want to get something done:

• I need to update my bank details
• I need a new laptop
• My payslip looks wrong

They do not know or care which department handles it, they just want it sorted. We reframed the entire AI program around this insight. Instead of building more function-specific tools, we designed a shared framework that organizes all services around what employees are actually trying to do.

A clear plan for what to build, and in what order

With a shared framework in place, we built the strategy for execution.

We mapped 1000+ services across all functions and assessed each one. Some were ready to be automated. Others needed simplifying first. A few were too complex to touch early on. The result was a clear, prioritized roadmap. Not a wish list. An actual sequence, wave by wave, based on what would deliver the most value to employees and the business fastest, without creating new problems.

Further, we designed the rules of the road: six principles that every function agreed to follow when making decisions about AI. Simple, practical, and clear enough that anyone in the organization could apply them.

And we put quality checks in place. Before any service could move to the AI platform, it had to meet a readiness standard. Because automating a broken process does not fix it. It just makes the problem harder to see.

THE RESULTS

From five teams pulling in different directions to one program moving forward together

Within months, the organization had something it had never had before: a single, shared plan for AI adoption that every function understood and had signed up to.

• 1000+ services assessed, prioritized, and sequenced into a clear delivery plan
• The highest-value opportunities identified,  the services touching the most employees, ready to automate first
• All functions aligned around the same priorities for the first time
• Six shared principles guiding every AI decision across the organization
• A clear roadmap: what to build, in what order, and why

AI stopped being a competition between teams. It became a shared journey with a direction everyone trusted and a plan everyone could follow.