AI Enablement Specialist

About BEES

Join us to build the future of B2B commerce!  

BEES is AB InBev’s B2B platform. Through our ecosystem, merchants and retailers across 29 countries can stock their businesses quickly, easily, and securely. 
At BEES, we dream big, lead with purpose, and develop technology that transforms the way retailers and sellers grow.  

Every line of code and every partnership is built in service of a single mission: to make commerce better for retailers and sellers around the world. Here, your work is not just important. It makes a difference!  

About this opportunity

We are improving how our teams operate. We are scaling AI as an enabler for better quality and speed: less effort spent on low-impact deliverables, more time building and executing the BEES strategy. The bar is high-quality analysis and better decisions — not simply faster output.

As the AI Enablement Specialist, you will own that shift. You will make sure people can access the right tools, trust what those tools produce, and genuinely change how they work — not just try AI once and go back to the old way. This is a hands-on role delivered with others: working alongside a multi-disciplinary team, you will lead the design of the enablement and governance model, and build enough of it yourself to prove it works before asking anyone else to adopt it.

You will own enablement and the quality standards behind it — the documentation, definitions and data context that decide whether an AI answer can be trusted. On technical solutions you set the strategy, priorities and governance while our technical teams build. You will lead a network of AI champions across teams, working through influence.

We are just starting our journey with AI in our workflows. You will have autonomy and the opportunity to leave your mark by building and executing the strategy yourself.

What you will do: 

• Build the management system that grows people's AI skills: define what good looks like at each level of maturity, the path to get there, and how progress is tracked by team.
• Deliver that learning at scale — role-based paths, hands-on workshops and regular office hours, always tied to the work people are actually doing rather than abstract training.
• Lead the AI champions network across teams: the operating model, the cadence, and the recognition that keeps it alive.
• Run short adoption pilots with individual teams, starting where maturity is lowest, and turn what works into a repeatable format.
• Own the tool strategy: which tools people use, how they get them, and how they apply them to their own work.
• Make access seamless. Run the internal AI hub as the front door and clear friction end to end with IT and data owners — provisioning, permissions and licensing are yours to unblock, not to route around.
• Own the intake for new AI requests: triage, prioritise, decide what gets built and in what order, and make sure nothing is built twice.
• Translate business problems into scoped briefs for the technical teams, and govern delivery against them. You set the what and the why; they build.
• Own the documentation standard that AI depends on: KPI and domain definitions, formulas, sources, rules and known pitfalls, produced with the owners of each area.
• Build the evaluation approach for AI output — how reliable answers are, where errors and drift appear, and when documentation or setup needs resetting.
• Maintain a shared library of skills, prompts and agents, with versioning and review, so good work is reused rather than rebuilt.
• Define and report the AI KPI set — a small number of primary indicators plus a secondary layer — reviewed with leadership on a fixed cadence.
• Set the guardrails for responsible use: where AI automates, where it augments, and where a human stays in the loop. Build this into training rather than bolting it on.

What success looks like

• Teams spend measurably more time on strategy and execution, and less on assembling recurring deliverables.
• Core workflows run faster and at a higher standard, because AI-assisted ways of working have become the default — sustained daily use, not licences issued.
• Decisions rest on analysis people trust, because the definitions, documentation and data behind every answer are standardised and owned.
• What gets built follows business value: solutions are reused across teams and duplicate builds stop.
• Leadership can see, on a regular cadence, where quality and speed improved, where they did not, and what the programme is worth.

What we're looking for:

• University degree in courses related to the field.
• Hands-on experience building AI solutions — agents, retrieval or knowledge systems, or workflow automation — and the judgment to know when a build is not worth it.
• Practical fluency across the modern AI toolset — ChatGPT, Claude, Copilot, Cursor, Databricks Genie, Obsidian, Granola and similar — plus comfort working directly with data platforms.
• Experience designing prompts, context and evaluation for LLM-based systems, including how to test output quality rather than assume it.
• A track record of driving technology adoption at scale — moving the long tail, not just running pilots.
• Strong documentation and knowledge-architecture instincts: you can turn scattered institutional knowledge into a structured, governed source of truth.
• Ability to define and operate a KPI framework, and to report honestly on what is and is not working.
• Proven ability to influence without authority across business and technical stakeholders, up to director and VP level. You will deliver by navigating complexity and connecting different teams.
• Excellent facilitation and executive communication; comfort being the visible, approachable point of contact for AI.
• Comfort with ambiguity, in a programme being built while it runs.
• Advanced English — the role operates globally and reports to international stakeholders.
• Experience with champion networks, communities of practice or internal product adoption is a plus.