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.

Gain practical frameworks, operating models and peer insights to scale AI from isolated pilots to enterprise-wide transformation.

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 clear governance framework for human-AI collaboration, plus decision criteria to determine when AI can act independently and when human oversight is non-negotiable.

- What does the workforce look like when the enterprise truly runs itself?
- How do you decide which decisions AI can own, which it can assist, and which must remain human-led?

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?

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?