As I wrap up the week at the World Economic Forum, it’s clear that a handful of themes are setting the tone for the year ahead. Here, I share the five Must Reads that capture the ideas leaders are taking into 2026.
1. AI’s next chapter
Looking back at the week in Davos, one of the clearest signals was how quickly the conversation around AI is shifting. Leaders are moving past isolated use cases and beginning to confront what it really takes to industrialize AI across the enterprise. Security remained a defining concern, and leaders spent meaningful time reflecting on human-centered design and what the future of work will demand. Much of the discussion centered on how people and intelligent agents will operate together in trusted, and practical ways. What stood out most was the growing recognition that the path forward hinges on intention. The next chapter of AI will be shaped by disciplined scaling, robust safety, and a more deliberate approach to how technology supports people and enhances the work they do.
5 emerging technology themes that will shape 2026 and beyond
2. 6 for 2026
EY Global Chair & CEO Janet Truncale‘s “6 for 2026” has been a consistent reference point in my conversations across Davos. Many gravitated to her focus on collaboration and her reminder that “confidence and trust are strategic assets,” a message that felt especially resonant in this moment. She was clear that AI is not a magic wand, it requires constant innovation, and equally firm that responsible AI is now a defining priority. Organizations that ignore it could face a steep bill, while those already investing in responsible AI are seeing the opposite: higher productivity, faster growth and lower costs. Her emphasis on investing in people and preparing for geopolitical volatility also echoed across the week’s discussions. It’s clear to me that these are the mindsets shaping the organizations that intend to lead the next chapter.
3. The soverignty shift
Sovereign AI also rose to the top of the agenda, and EY Global Vice Chair – Alliances & Ecosystems Julie Linn Teigland’s perspective helps explain why. Her observation that “risk and strategy are now inextricably intertwined” captured a major shift in thinking, as organizations reconsider not only control of their data, but the full architecture behind their AI capabilities. The move from national to enterprise sovereignty is accelerating, and decisions about where AI runs and how specialized talent is deployed are becoming central to long-term resilience. In an uncertain world, sovereignty is quickly becoming a foundational capability.
Why sovereign AI drives resilience and strategic advantage in an uncertain world
4. Beyond GenAI
Neurosymbolic AI (NSAI), is a term that frequently came up frequently at Davos, and US EY Growth Platforms Leader, EY-Parthenon Jeff Schumacher highlights why it is resonating so widely with executives. Organizations are looking for intelligence they can trust as decisions grow more complex. As Jeff notes, NSAI delivers “predictions and decisions that are practical, actionable and grounded in real‑world outcomes,” a capability that is becoming increasingly important. Its ability to pair pattern recognition with logic and causality is drawing attention from leaders focused on growth, risk and competitiveness. For companies navigating heightened complexity, NSAI is emerging as a powerful tool for clarity and stronger decision-making.
5. Shape 2026 with confidence
The CEO Outlook 2026 echoes many of the themes that shaped conversations at Davos. Leaders are balancing geopolitical pressure and rising costs, while continuing to see AI and operating model transformation as essential to creating forward momentum. The shift from pilots to disciplined scaling is evident, and M&A is becoming an important catalyst for new capabilities. The CEOs gaining traction are not waiting for stability; they are making deliberate choices, reshaping their organizations and building adaptability into every part of the enterprise.
How do CEOs reimagine enterprises for a future that keeps rewriting itself?
If you do one thing:
Think beyond experimenting with AI. How are you turning pilots into implementation that drives measurable results?