FG Fabian Grupe
Exemplary delivery case

A representative example of how I run an enterprise program, end to end.

A governed AI marketing platform for a national-leading retail group.

Best-of-breed AI plugged in per task. Governance built in from day one. One platform lifting brand-safe marketing across 10+ brands and 14+ channels.

AI marketing platform  ·  Retail  ·  Agent-agnostic, governed by design
~€3.8M
Year-1 budget
~25
specialists
6
delivery pods
4
organizations
10+
brands
14+
channels
At a glance

What it was, and what I owned.

The goal

One platform, not scattered point solutions.

Stand up one agent-agnostic AI marketing platform across every brand, region and channel. Centrally governed, locally used.

One platform
My role

Overall-accountable Program Manager.

Single point of accountability across four organizations. One person carries the decision and the delivery.

Central unitCentral ITDelivery partnerCloud & AI partnerProgramManagersingle point ofaccountability
The detail

The challenge, and how I approached it.

The challenge

Many owners, no natural seam.

A deeply federated retailer: a central business unit plus autonomous regional stores. Pilot regions, a cloud & AI vendor, and two implementation partners. No single owner of the whole.

Central unitRegion ARegion BRegion CVendorPartners
My approach

An MVP path that ships the pilot to production.

A Minimum Lovable Product first: controlled, brand-safe, governed from day one. Then scaled step by step, not all at once.

DiscoveryPilotMLPin productionExpansion 1Scale▲ governed from day one
How it works

The platform, layer by layer.

Business, platform and model logic kept separate, so the platform holds when models, vendors or systems change.

Experience layerWeb UI, asset selection, variants, review, export
Orchestration layerWorkflow engine, agent routing, guardrails
AI modelsBest-of-breed, swapped per taskimagetextvideovoice
Brand & data intelligenceCI rules, templates, product data (ePIM)
Cloud / infrastructureIAM, security, monitoring, FinOps
Governance & QA
What makes it hold

Two mechanics that keep it brand-safe.

Model-agnostic by design

No lock-in. Swap the model, keep running.

The orchestrator picks the best model per task. Swap one out and the platform keeps running, across text, image, video and voice.

ClaudeOpenAIGemini
Orchestrator · routes per task
text·image·video·voice
Brand-safe gate

Nothing publishes unchecked.

Every asset runs the gate before it goes live. Human-in-the-loop on the release that matters.

Draft
Brand check
Compliance
Human sign-off
Published
Outcome

From AI pilot to production. By design.

Currently in delivery. The proof is set-up quality, governance, and a program that holds. Not a vanity metric.