Contrasting Enterprise AI Strategies: ExxonMobil and Levi Strauss

At SAP Sapphire 2026, the company emphasized its vision of an ‘Autonomous Enterprise’ where AI agents handle business processes independently. However, customer case studies revealed more nuanced approaches to AI adoption.

ExxonMobil, with its century-long history, is taking a deliberate approach—prioritizing foundational work before widespread AI implementation. In contrast, Levi Strauss has already deployed over 1000 AI agents, demonstrating the spectrum of enterprise readiness and strategic priorities.

ExxonMobil: Building First Principles

As part of a broader transformation to a ‘clean core’ architecture, ExxonMobil is removing decades of custom SAP modifications. According to Bill Keeler, VP of Business Transformation:

“We think it’s better to watch the AI phenomenon unfold rather than rush in. Getting the fundamentals right is our priority.”

Keeler emphasized that data serves as this foundation—a ‘strategic asset’ that needs to be unlocked. He warned against superficial implementations, stating:

“If you don’t build on a solid base, you’ll continue to pay the price for it later.”

He advised CIOs leading transformations to set clear strategic intent, establish robust governance, and partner selectively—viewing AI as mutually beneficial rather than transactional.

Levi Strauss: Embracing Agent-Based Automation

While ExxonMobil focuses on foundations, Levi Strauss exemplifies the potential of agent-based AI. The 170-year-old apparel company has already deployed over 1000 agents and provided AI training to 4000 employees.

Jason Gowitz, Chief Digital & Technology Officer, explained:

“Standardization enables agility—they’re not mutually exclusive concepts. In fact, standardization creates the foundation for more responsive capabilities.”

For example, order processing that once took days now completes in minutes thanks to AI agents integrated with SAP S/4HANA and Microsoft Azure.

Other Notable Cases

• Lockheed Martin emphasizes ‘readiness’ over mere transformation—ensuring systems function reliably even in critical situations.
• Aeropuertos Argentina implemented a complete AI solution (‘S.N.O.W.’) across 35 airports in just 12 weeks, reducing costs and improving operational efficiency.

The contrasting approaches highlight that enterprise AI adoption isn’t one-size-fits-all—it requires aligning technology with specific business needs, risk profiles, and strategic priorities.