A pilot graveyard with a copilot logo on the headstone.
Six AI pilots, two production deployments, zero P&L impact. The board wants outcomes, not demos.
Six AI pilots, two production deployments, zero P&L impact. Fix it with a value/risk shortlist on Azure AI Foundry + MLOps + Responsible AI guardrails. First use case in production in eight weeks.
If half of these sound familiar, this is your fight.
No formal assessment needed, just an honest look at the daily friction.
Most AI use cases never get past the pilot stage.
You can't name three AI initiatives that produced measurable savings.
Models drift in production and nobody owns retraining.
GenAI tools live in IT pilots that the business never adopted.
You don't have a single shared scorecard for AI value.
Every new AI initiative starts with a fresh data integration.
Why it happens
No data foundation
AI runs on data. Without a Fabric backbone, every pilot rebuilds the same plumbing.
No MLOps, no retraining
Models that work in week one drift by month three. Without monitoring, performance silently degrades.
Use cases picked by enthusiasm
Without a value/risk shortlist, the loudest team wins, not the highest-ROI use case.
What it costs you
Sunk pilots
Hundreds of thousands burned in licences, consultants and POCs that never reached production.
AI fatigue
Business leaders stop believing AI can deliver, which is exactly when competitors break ahead.
AI Act exposure
High-risk models in production without documentation, evaluation or human oversight: regulator nightmare.
The shortest path from problem to results.
Microsoft-first stack. Belgian and Estonian engineering. Senior team kickoff through delivery to support.
AI use-case shortlist & risk tiering
Two weeks to a prioritised, AI Act-mapped backlog, with a pilot plan that ships in eight.
See solutionAI-ready Fabric foundation
A governed semantic model, feature store and OneLake backbone, so the next pilot starts at week three, not week one.
See solutionResponsible AI guardrails
Evaluation harnesses, content filters, monitoring, so pilots can ship to production without nasty surprises.
See solutionWhen five sleeping pilots became three live use cases.
Pilot to production in eight weeks · MLOps from day one
A Belgian public-sector organisation with five GenAI pilots stuck at proof-of-concept. We ran a value/risk shortlist, killed two, productionised three on Azure AI Foundry with full evaluation and human-in-the-loop. First measurable saving: month three.
AI that actually delivers. Pick one use case.
Free 60-minute use-case shortlist: we'll score, tier and prioritise three candidates.
Common questions, direct answers.
Why do most AI pilots fail to reach production?
Three reasons: no data foundation (every pilot rebuilds plumbing), no MLOps (models drift unmonitored), and use cases picked by enthusiasm not value. We fix all three in parallel.
How do we pick which AI use cases to build?
A two-week shortlist scoring every candidate on three axes: business value, technical feasibility, and EU AI Act risk tier. We typically kill 30% of the wishlist on day one, and the customer thanks us for it later.
Do we need a separate AI platform or use what we have?
Azure AI Foundry on top of your existing Microsoft Fabric stack covers 90% of what most teams need. Specialist tools (Hugging Face, OpenAI direct) only earn their keep on specific bets.
How is "Agentic AI" different from regular AI use cases?
Agentic AI plans, calls tools, takes actions, with human-in-the-loop on critical decisions. It's where the puck is going for 2026. We pilot agents on low-risk workflows first (internal copilots) before customer-facing ones.
What about EU AI Act compliance for AI in production?
Every use case gets risk-tiered (minimal, limited, high, unacceptable) up front. High-tier needs Article 9-17 documentation; we build it in from day one rather than retro-fitting.