Case Study
RBI AI Transformation: four brands, more than two thousand employees
How Aventurasoft's founder led AI transformation at Restaurant Brands International across four brands and more than two thousand employees, without a single agentic pilot in the first wave. The sequence mattered.
The scope
Four global brands under a single parent, more than two thousand corporate employees, a heterogeneous technology estate, and a workforce that was already using AI without permission. The brief was to build an AI transformation program that was safe for the business, usable for the workforce, and capable of showing up in the P&L.
The sequence
The program was sequenced on purpose. Most AI transformation stories lead with an agentic pilot. This one did not. The order was tool selection, governance, training, and rollout first, data strategy second, and agentic operating model design last. Every step in front of agentic AI was there to reduce risk, reduce fear, and make the eventual agentic work safer and more valuable.
- Shadow AI assessment: what was already in use, by whom, for what, and with what data exposure.
- Productivity tool selection across Claude for Enterprise, Microsoft Copilot, ChatGPT Enterprise, and Gemini, with a vendor-neutral evaluation framework.
- Governance and policy: acceptable use, data handling, approval paths, and an Action Governance model for agentic work before a single agent shipped.
- Training: a two-reason curriculum that reduced fear and taught people that AI was a skill, not a threat.
- Data strategy: the foundations the analytical and agentic waves would need, addressed before either wave started.
- Agentic operating model design: only after the first five steps were in place.
Why agentic AI came last
Agentic AI is the loudest part of the AI story right now and it is genuinely transformational. It is also the part most likely to fail in production if the data foundations, governance, and workforce readiness are not already in place. Leading with agents in an organization of more than two thousand employees would have exposed the business to decisions nobody could audit and automations nobody could roll back. Sequencing the program the way we did meant that when agentic work did begin, the guardrails were already written and the workforce already had the skills to collaborate with agents rather than be surprised by them.
What Aventurasoft carried forward
The RBI program is the reason Aventurasoft codifies every engagement into a named framework. The 7 Layer AI Transformation Framework came out of this work, as did the Action Governance model and Phase Appropriate Metrics. These are the artifacts clients now get on day one instead of having to be rebuilt from scratch for every program.