AI is no longer an experiment. It’s an expectation.

Enterprises are investing at speed, yet many are still struggling to prove value, establish accountability and scale beyond isolated wins.

The ISG AI Impact Summit focuses on what’s slowing impact including the gap between AI ambition and measurable ROI, unclear ownership in human-AI decision-making, operating models not built for autonomy and data foundations that fail under real-world pressure.

This isn’t about launching more pilots. It’s about redesigning how business gets done and delivering results that stand up in the boardroom.

WHAT YOU'LL DISCOVER

Stop celebrating efficiency. Start redesigning how value is created, owned, and measured.

Gain a practical framework to connect AI initiatives to enterprise-level outcomes, align incentives and operating models to those outcomes, and arm yourself with CFO-ready proof points that unlock funding for what scales not what demos well.

- How do you distinguish performance theater from durable enterprise value?

- How do you connect isolated AI wins to revenue, risk reduction, and customer impact?

- Who owns the outcome when AI participates in the decision—and how is accountability designed?

AI doesn’t eliminate jobs, it eliminates clarity of ownership.

Walk away with a governance and operating model framework for human-AI collaboration, including clear decision rights, escalation paths, and criteria for when AI can act independently, assist humans, or must defer entirely.

- What does the workforce look like when decision-making is distributed across humans and machines

- How do roles, incentives, and performance models change in AI-augmented teams?

-How do you design accountability so autonomy increases trust instead of risk?

AI isn’t changing the price of what you buy. It’s changing what you build—and how the enterprise operates.

Navigate the new economics of AI with a focus on long-term operating sustainability, not short-term savings: GCCs vs sourcing partners, platforms vs custom builds, licensing vs consumption and how architectural choices shape autonomy, control, and cost over time.

- How do you structure deals and partnerships for a governed, autonomous enterprise?

- Which procurement models accelerate AI adoption and which quietly block it?

- How do you avoid locking in today’s AI choices as tomorrow’s technical debt?

Your AI works in the lab. Autonomy fails in production when data, context, and accountability break down.

Discover what data readiness truly requires to support real-time, AI-driven decisions at scale including contextualization, trust, observability, and integration into live operating workflows.

- Is your data fit for AI-driven decision-making or just model training?

- How do you close the gap between analytics outputs and executable decisions?

-How do you prove your data, models, and decisions are auditable, explainable, and ready for autonomy in the real world?