Henkel embraces gen AI as enabler and strategic disruptor

Four years ago, German multinational Henkel found itself at a crossroads. Like many incumbents in the consumer packaged goods (CPG) industry, Henkel was slow to embrace digital technologies, resulting in a widening disconnect between the 147-year-old company and the changing needs of its customers.

As Henkel CDIO Michael Nilles puts it, by 2019, Marc Andreessen’s pronouncement that “software is eating the world” had come true for the CPG sector, and Henkel was at risk of falling behind.

“We took it seriously and said we need to have software, data, and AI capabilities,” says Nilles, who signed on to the CDIO role at the time. “We needed to double down on the massive upskilling of the internal organization and also acquire — we call it injecting — talent from the outside.”

To achieve its vision, Henkel laid down a five-year strategic roadmap that involved reshuffling the IT organization, creating a new digital unit, consolidating CIO and CDO venture activities under one roof, and building global innovation centers in hubs like Berlin, Shanghai, Bangalore, and the US.

Today, those efforts are coming to fruition, positioning Henkel among the leading wave of companies adopting generative AI to not only optimize its businesses, but use it as a core building block of its strategic vision for the future.

Gearing up for change

Headquartered in Dusseldorf with 50,000-plus employees, Henkel is more than a CPG enterprise. Its industrial B2B arm focuses on adhesives technologies, like Loctite, while its B2C consumer goods arm owns brands such as Dial and Purex.

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.

“In trade promotion management, marginal optimization has a huge impact on the business, both top and bottom line,” says Nilles.

However, at the same time, SAP was working on a new feature for the SAP Analytics Cloud: Just Ask, which applies gen AI to search-driven analytics. The team experimented with using Just Ask with the TPO tool, and quickly saw it as the key to fulfilling the tool’s promise.

“The whole use of trade promotion, which is still a complicated animal, has become much more intuitive for the key account managers,” Nilles says. “The key account manager or the salesperson is looking at the trade promotion data and it’s giving really great hints. This just wasn’t possible with traditional machine learning. With gen AI, the AI capabilities have become much more widely usable by people who aren’t PhDs in data science.”

In the past, Nilles notes that creating a campaign with retailers around a product could take months to hammer out details, like the proper discounts to offer. Now account managers can walk into a meeting, use the tool with natural language to explore options in more or less real-time, and have a finished plan ready to share the next day. By the next week, the complete campaign can be ready to go in stores nationwide.

A catalyst for growth

The tool has already been a big success, but Nilles believes gen AI can play an even bigger strategic role at Henkel. The company has been hard at work translating all the data from its CPG R&D into a large language model (LLM), which Nilles says will be a huge accelerator for Henkel to develop new products and help position Henkel to take a leadership role in its space as gen AI disrupts the industry.

“We believe there’ll be new vertical LLMs emerging, and maybe even micro-vertical ones that are domain-specific,” he says. “We believe having LLMs for the right things will be a huge competitive advantage, and that if we don’t do it, we’ll be seriously threatened and jeopardized by others.”

Imagine, for instance, the tennis equipment marketplace. Today, someone interested in buying tennis equipment might go to a website that specializes in the sport. But with the growth of gen AI, consumers might go to the likes of Meta or Tencent, and put a query in a prompt. Whoever built the LLM that WeChat, for example, uses to answer that query is going to have an advantage in the marketplace.

“If we’re one of the first, we set the market and we have the right to play at the table when there’s a rewiring of the whole value chain,” Nilles says.