Sales up 38%, worth €166M in EBITA.
Churn down 4%.

The board talked margin. The sales floor was paid on volume.

Sales was sceptical of AI. Years of dashboards and failed transformations had left the organisation exhausted, and trust in previous AI initiatives was low. Growth, churn and margin were discussed at length in the boardroom, but none of it translated into a clear definition of what to do differently on a sales call.

Ordering patterns across Brenntag’s markets – from Food & Nutrition and Pharma to water treatment, energy and automotive – were highly volatile and depended on individual salesperson intuition rather than any scalable insight. Sales incentives made it worse: they rewarded volume, not timing, margin quality or customer value, so even where good insight existed, nothing in the pay structure pushed behaviour to change.

Three constraints were non-negotiable:

• Low trust in AI after failed dashboard initiatives
• No shared language linking board KPIs to sales actions
• Incentives rewarded volume over margin and retention

A finance-owned business case, then a model sales could trust.

The starting point wasn’t a model – it was a P&L value case, owned by finance, that defined what counted as value (EBITDA, gross profit, churn prevention), how it would be measured, and what was realised versus theoretical. Board KPIs were translated into sales-level actions and metrics, giving finance, leadership and sales a shared language for the first time, tracked through an executive dashboard showing value against plan by team.

Delivery was restructured around cross-functional squads of sales, finance and data & AI, replacing a traditional build-and-deploy approach with co-creation. Our Fractional Head of AI led the team to an MVP in under 90 days, focused on order timing, product selection and margin insight, accepting imperfect data and iterating rather than waiting for a perfect model. That MVP grew into a suite of ML models: predictive order and churn models flagging optimal order windows and at-risk accounts, recommendation models surfacing next-best products and cross-sell opportunities, and algorithmic price and margin intelligence highlighting margin at risk and optimal price ranges.

To make the change stick, each salesperson got an individual impact analysis showing exactly how AI-guided actions moved their basket size, margin and retention. Commissions were realigned to reward growth, price realisation and churn prevention, and managers were equipped to coach from the same data – closing the loop from insight to action to reward.

38% more sales, 4% less churn, 90% of the footprint in a year.

Closing the board-sales gap turned AI into a commercial performance engine rather than another dashboard nobody trusted. With incentives, coaching and insight all pointing the same way, adoption moved fast and stayed – giving Brenntag a repeatable foundation for the next wave of AI initiatives.