Enterprise AI Governance Blueprint
A reference architecture to standardize how AI is governed across models, agents, and SaaS, with a shared language for the five failures.
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AI is no longer an innovation challenge. It is a governance challenge. As enterprises deploy hundreds of models, agents, and SaaS apps, the question isn’t whether AI delivers value, but whether you can govern it responsibly, securely, and profitably.
Why we are convening CIOs, CFOs, CISOs, and Heads of AI around the blind spots of AI adoption, and what you leave with.
A short introduction to the Enterprise AI Adoption Management Summit 2026
The recognition executive teams trust
SaaS Management Platform, 2024 & 2025
SaaS Management Platform, 2024 & 2025
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Chicago Innovation Awards, 2025
Gartner Peer Insights · 4.9 / 5
G2 · 4.8 / 5
Google Cloud Technology Partner
FinOps Foundation Member88% of organizations use AI. Only 36% can govern it. That gap is where value leaks, through five recurring failures. You cannot govern what you cannot see.
Shadow AI spreads. Model and SaaS spend turn invisible. Agents gain access without oversight. Compliance loses sight, FinOps can’t attribute cost, and security finds risk only after deployment.
Governance, not adoption, is now the defining challenge of enterprise AI.
Pilots are becoming enterprise deployments. Agents are making decisions. Model costs rise, regulation accelerates, and boards want accountability. The teams that govern AI now will outperform those still chasing adoption.
Only 39% of adopters see enterprise financial impact. Each quarter without a governance and FinOps model widens the distance to the leaders.
As agents act autonomously, cost and risk multiply. Gartner expects over 40% of agentic-AI projects to be canceled by 2027.
Worker trust in company GenAI fell 31% in a single quarter. Adoption is won through change management, not tool mandates.
EU AI Act enforcement and sector frameworks are now real. Ungoverned AI is a compliance liability, not just an operational one.
Boards now ask for AI ROI attribution, not roadmaps. Buying time with strategy decks and pilot announcements is ending.
Foundation-model and token costs keep climbing. Without financial governance, AI spend scales faster than the value it creates.
Governance is becoming the operating system of enterprise AI.
Thursday, 13 August 2026 · 11:00 AM ET to 2:30 PM ET. All times listed are Eastern Time (ET) and are subject to change.
How the next three and a half hours are structured, what you leave with, and an introduction to our first keynote speaker.
Why incremental thinking caps AI returns, and how leaders rewire innovation for step-change outcomes.
Alice Stein introduces Keynote 02 and Shailesh Kumar.
What changes when intelligence stops being a feature and becomes the operating layer of the enterprise.
Alice Stein introduces the Panel 01 line-up and topic.
Where ungoverned AI actually enters the enterprise, how to find it, and what to do once you can see it.
Alice Stein introduces the Panel 02 line-up and topic.
The signals that prove real usage and real value, beyond seat counts and license reports.
Alice Stein introduces the Panel 03 line-up and topic.
Policy, guardrails, and token economics that keep AI spend attributable and risk contained.
Alice Stein introduces the Panel 04 line-up and topic. Contingency buffer.
CIO truths on AI’s everyday blind spots: what breaks on the way to production, and the calls they would make again.
A live walkthrough of agentic AI in production: how agents are deployed, monitored, and optimized.
Where to find the session recordings and the executive operating assets.
Alice Stein introduces every session and panel line-up, with contingency buffers built between sessions to keep the summit on time. All times are Eastern Time (ET).
Every decision is framed for the executive team, not the engineering team. No technical background required. Join 100+ enterprise leaders governing AI at scale.
Every attendee leaves with the frameworks to govern AI: blueprints, checklists, and scorecards you can operationalize immediately.
A reference architecture to standardize how AI is governed across models, agents, and SaaS, with a shared language for the five failures.
Define who governs AI, who owns spend, and how decisions are made. The operating model that scales past pilots to 2030.
Attribute every dollar of token and model spend to an outcome, and operationalize FinOps for AI. Built for CIOs and CFOs.
Measure adoption, ROI, risk, and governance maturity on one board-ready page, so you can answer the questions boards ask.
Institutionalize acceptable-use, access, and compliance controls for shadow AI and agents. Governance that enables speed.
A practitioner-built plan to move from experimentation to governed, enterprise-wide deployment. From teams that have done it.
The next generation of enterprise advantage will come from governing AI, not simply deploying it. Governance is becoming the operating system of enterprise AI.
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