I recently had the opportunity (again!) to work with Ann Culver from Amazon Web Services (AWS) and Todd Pruzan at Harvard Business Review 🙌 on a special webinar and playbook. And they’re now available! (email gate). Links below! ⬇️

Why this is worth your time…

We dug into a tension we’re seeing in C-Suitesand boardrooms: Most organizations are still stuck in AI “pilot purgatory” and the box of “business as usual.” Companies have PoCs, scattered experiments, and a lot of hype, but not a scalable, enterprise-wide AI operating model.

Ann and I explored the shift from tools to transformation. AI-driven workflows are not about sprinkling models on top of yesterday’s processes. They are about orchestrating work differently end to end, powered by connected, trusted data, robust governance, and a culture that’s ready to operate at the speed of, well, not business as usual.

We also explored the shift from simple assistants to agentic AI: systems that can reason, collaborate, and make autonomous decisions across workflows.

Gartner projects that within 3 years, 15% of day-to-day work decisions will be made autonomously. This represents a new management reality, where leaders must design for humans and agents to work together across cloud, data, and workflow/cloud platforms.

🚨SPOILER ALERT: Companies are already repeating the worst mistakes of the digital transformation era. They are funding siloed AI initiatives, over-indexing on short-term potential, and under-investing in vision, culture, and scale. That’s why a recent ServiceNow study shows organizations’ AI maturity scores actually declined in the last year, dropping from 44 out of 100 to 35. Then there’s that viral MIT study…

The message we wanted to land is this…

You don’t fix this with another pilot; you fix it by reimagining how your business works with AI at the center. Data + Workflows + AI.

When you unify distributed data, connect systems across end-to-end workflows (rewiring the enterprise), and run on cloud built to scale, you can lower costs by orders of magnitude, achieve 3x faster operational speeds, and save millions by optimizing processes with AI…all while improving customer response times, NPS, and lifetime value.

From there, then imagine the enterprise value you can unlock when you dream bigger! 💭

Key Takeaways

• AI at scale starts at the top, not in the lab. Moving from pilots to production requires top-down leadership, a cohesive AI strategy, and a clear business case that links AI directly to revenue growth, cost reduction, and better risk outcomes. Experiments without executive ownership and an enterprise roadmap rarely scale.

• We are entering the age of agentic AI, not just clever copilots. The evolution from basic AI assistants to multi-agent, reasoning systems means more and more daily decisions will be made autonomously. Leaders need to design workflows, governance, and operating models for humans and AI agents working together, not in parallel universes.

• Silos are the silent killer of AI ROI. Many organizations are still making fragmented, department-level AI bets, as they did with early digital initiatives. Because AI needs connected data, people, and processes, siloed investments make it nearly impossible to show meaningful ROI or build an “AI-powered business,” not just pockets of automation.

• Governance and innovation have to walk hand in hand. Over-rotating to compliance slows everything down; ignoring governance creates risk and backlash. The organizations that win are building centers of excellence, boards, or federated models that keep governance “front and center” while still leaving room to experiment, learn, and even fail quickly on the way to scale.

• Start small, but design for big. High-impact, low-complexity use cases are the best entry point, but they should be chosen as part of a broader roadmap, not as isolated experiments. Each successful workflow becomes a proof point, a pattern, and a piece of the broader AI operating system you are building across the enterprise.

• Empower people, don’t just upskill them. Training matters, but what really unlocks transformation is helping employees see how AI can change the nature of their work – shifting from repetitive tasks to higher-value creativity, curiosity, and problem solving. That means transparent communication, visible early wins, and AI champions in every business function to normalize AI as part of everyday work.

• Cloud plus workflow is where scale actually happens. Together, ServiceNow and AWS provide an integrated platform for AI workflows: unifying data, hiding operational complexity, and enabling industry-specific agents that deliver intuitive, personalized experiences. With our “customer zero” approach, we test AI internally first so customers can move faster and with more confidence in their own environments

If you’re serious about moving beyond pilots and building an AI-driven enterprise that can truly scale, I encourage you to watch the full webinar, “Powering AI-Driven Workflows on the Cloud Built to Scale,” on HBR and download the accompanying webinar summary playbook to dive deeper into the frameworks, examples, and next steps we discussed.

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