13 Q UA RT E R _ 0 2 _ 2 0 2 6 - - - - - - - - - 8. Make data easy to consume— and enrich it for advantage. As David Baker, winner of the 2024 Nobel Prize in Chemistry, observed when reflecting on recent break throughs: “AI needs masses of high-quality data to be useful.” Without good data, AI breakthroughs are impossible. Yet in most organizations, data often still acts as the constraining factor. Scaling AI therefore starts by productizing data—making it easy to dis cover, access, and consume across many AI-powered applications. That requires investments in building data products. Over time, the game shifts to data enrichment, deepening its quality, context, and uniqueness for sustained performance gains with AI. In Rewired organizations, data is a business-owned performance asset. Can your teams easily consume your data, or are they still wrangling it? 9. Design for adoption and build for scale. AI systems create value only when they are adopted and scaled. That may sound obvious, yet it remains one of the hardest challenges. Adoption often fails because adjacent upstream and downstream pro cesses are left unchanged. An AI solution may predict equipment failures days in advance, but if maintenance still follows calendar-based schedul ing, nothing happens. Scaling is a different, but equally difficult, challenge. Expanding AI solutions quickly and economically across markets, factories, customer segments, or product lines requires modular solu tion architectures and a well-choreographed dance between central teams and the receiving units. These considerations—including required invest ments and run costs—must be addressed up front, not retrofitted later. Can your organization repeatedly adopt and scale AI, or is it still relying on isolated heroics? 10. No trust, no right to deploy AI. When AI systems fail, they challenge trust with customers, regulators, employees, partners, and society at large. Digital trust grows when stake holders have confidence that your organization protects consumer data, enacts effective cyber- security, offers trustworthy AI-powered products and services, and provides transparency around AI and data usage. The challenges are only increas ing with the expansion of agentic technologies, requiring much more time for testing agen tic systems and automating risk controls. It’s a fast-moving space, and the excitement for agentic AI may be getting ahead of companies’ ability to manage the more complex risks associated with the technology. Would your AI deployments withstand public, regulatory, and customer scrutiny today? Scaling AI can make it easy to discover, access, and consume many AI-powered applications.