Henkel embraces gen AI as enabler and strategic disruptor

But to achieve Henkel’s digital vision, Nilles would need to attract data scientists, data engineers, and AI experts to an industry they might not otherwise have their eye on. As Nilles says, there’s a “nuclear war” for talent, but one factor in Henkel’s favor is that the company has meaningful problems to solve.

“We’ve been lucky, I think, because we have interesting industry problems to crack,” Nilles says. “The foundational AI super guru who’s working for big tech is maybe not interested in joining a company like Henkel, but he’s also probably not the right guy for that. What we’re doing is finding the guys who like to crack big industry problems with technology.”

Another key component of Nilles’ plan for Henkel has been to build strong strategic partnerships. Henkel already had a relationship with SAP, and Nilles opted to deepen that relationship by going all-in on SAP’s Business Technology Platform (BTP) and working closely with the software company on co-innovation. The partnership was dubbed Digital Leapfrog, and one of its first fruits was an AI-powered trade promotion management (TPM) and trade promotion optimization (TPO) tool.

TPM and TPO are key disciplines in the CPG space that involve managing and optimizing all promotional activities conducted with retailers, from discounts to deductions and payments. Big CPG companies like Henkel can spend billions on trade promotion, so there’s a lot at stake in getting it right.

“Most of the industry wasn’t doing that right for many years because there wasn’t really a standard software available in the space,” Nilles says. “It’s a bit of a tough computer science problem. You have a highly complex data model, you need a lot of compute, and you need to have a really smart UI.”

Invention through necessity

Unable to find a solution in the marketplace, Henkel decided to build one. The Henkel and SAP co-innovation team worked closely together to build and scale the tool, which had to be able to handle more than two billion planning nodes. At the time, the team was focusing on traditional AI, using machine learning capabilities to build a recommendation engine that could help end users perform TPO on the fly.