From AI Hype to Hard ROI: Scaling What Actually Works
AI adoption has surged, but execution is lagging. According to McKinsey & Company, while over 70% of organizations report using AI in some capacity, fewer than 20% have successfully scaled it across the enterprise. At the same time, Gartner predicts that by 2026, over 80% of AI projects will fail to deliver expected value without proper governance and operational alignment. Executives are facing mounting pressure to prove ROI while navigating fragmented data, unclear ownership, rising costs, and a growing disconnect between experimentation and real business impact.
Leading organizations are shifting their focus from tools to transformation. This means building strong data foundations, embedding governance and accountability from the outset, and aligning AI initiatives to measurable business outcomes. It also requires rethinking operating models—integrating AI into core workflows, enabling cross-functional collaboration, and preparing the workforce for a future where humans and intelligent systems work side by side. Those making progress are not just deploying AI—they are operationalizing it with discipline, clarity, and purpose.
Previous Speakers Include:
Lambert Hogenhout
Queena Cheung
Agenda
9:00am - 9:15am
MOC Opening
9:15am - 10:00am
Industry Panel
The Agentic Enterprise: When AI Starts Doing the Work
AI is no longer just augmenting decisions—it’s beginning to execute them. From customer interactions to financial analysis to operational workflows, autonomous agents are reshaping how work gets done. But as organizations experiment with agentic AI, most are unprepared for the complexity it introduces: fragmented systems, unclear accountability, and a workforce model that hasn’t caught up.
This panel explores what it really takes to operationalize agentic AI—how to integrate it into core workflows, define ownership, and redesign the enterprise for a future where humans and AI agents work side by side.
Discussion Points:
• Where agentic AI is delivering real value today vs. where it’s overhyped
• The operational and organizational challenges of deploying autonomous agents
• Redefining roles, workflows, and accountability in a human + AI workforce
10:00am - 11:00am
Workshop Tracks
Track 1: From Insight to Action: Closing the AI Execution Gap
Many organizations believe they have control over their AI strategy, supported by dashboards, pilots, and growing investment. Yet fragmented data, unclear ownership, and slow decision-making continue to stall progress at critical moments. In an environment where AI operates at machine speed, the gap between what leaders see and how quickly they can act has become one of the biggest barriers to value.
This session explores how leaders are moving beyond visibility into execution, rethinking decision rights, operational alignment, and accountability to accelerate AI from insight to action and deliver measurable outcomes at scale.
Track 2: Proving AI ROI: Moving Beyond Experimentation to Accountability
Organizations have invested heavily in AI, yet many still struggle to prove its value. Dashboards show activity, pilots demonstrate potential, but clear financial impact remains elusive. At the same time, rising costs, unclear metrics, and disconnected initiatives are making it harder for executives to justify continued investment.
This session focuses on how leaders are redefining success—moving beyond vanity metrics to measurable business outcomes. It explores how to align AI initiatives to revenue, efficiency, and risk reduction, while embedding accountability into every stage of the lifecycle.
Track 3: Governing AI at Scale: Ownership, Risk, and the Future Operating Model
As AI becomes embedded across the enterprise, organizations are facing a new reality: more autonomy, more risk, and less clarity on who is accountable. Leaders often believe governance is in place, yet in practice, siloed teams, inconsistent policies, and shadow AI are creating exposure that is difficult to detect and even harder to manage.
This session examines how organizations are evolving governance from static frameworks to dynamic, operational models. It will explore how to define ownership, manage risk in real time, and redesign operating structures to support a future where humans and AI systems work side by side.
11:15am - 11:45am
Visionary Implementation Interview
12:00pm - 12:30am
Visionary Adoption Interview
2:00pm - 3:00pm
Roundtable Tracks
Track 1: Breaking Out of AI Pilot Purgatory
Most organizations aren’t struggling to start with AI—they’re struggling to scale it. Despite dozens of pilots, real enterprise impact remains limited. Fragmented ownership, disconnected data, and unclear success metrics are keeping AI stuck in experimentation while expectations from the business continue to rise.
This roundtable explores what separates organizations that scale AI from those that stall—focusing on execution, prioritization, and the operating discipline required to turn promising use cases into production outcomes.
Track 2: Data Readiness in the Age of AI: Foundation or Fiction?
Organizations often believe they are data-ready for AI—but reality tells a different story. Siloed data, inconsistent quality, and limited accessibility continue to undermine even the most advanced AI initiatives. As AI shifts from human-driven queries to machine-driven consumption, the cracks in existing data strategies are becoming impossible to ignore.
This roundtable focuses on what true data readiness looks like in practice—and how leaders are rethinking data architecture, ownership, and governance to support AI at scale.
Track 3: Who Owns AI? Governance, Accountability, and Risk at Scale
As AI becomes more autonomous and embedded across the enterprise, accountability is becoming increasingly unclear. Leaders believe governance is in place, yet shadow AI is growing, policies are inconsistent, and decision ownership is often undefined. The result: increased risk, slower adoption, and hesitation at the executive level.
This roundtable explores how organizations are evolving governance models to match the speed and scale of AI—defining ownership, embedding accountability, and managing risk without slowing innovation.
9:00am - 9:15am
MOC Opening
9:15am - 10:00am
Industry Panel
The Cost of AI: From Innovation Spend to Financial Discipline
AI investment is accelerating but so is scrutiny. What started as innovation spend is now under the microscope, with boards and executives demanding clear financial outcomes. The challenge: AI costs are complex, value is often indirect, and traditional ROI models don’t apply. Many organizations are spending heavily without a clear path to return.
This panel examines how leaders are bringing financial discipline to AI, tracking total cost, aligning investments to measurable outcomes, and making smarter decisions about where to double down or pull back.
10:00am - 11:00am
Workshop Tracks
Track 1: From Insight to Action: Closing the AI Execution Gap
Many organizations believe they have control over their AI strategy, supported by dashboards, pilots, and growing investment. Yet fragmented data, unclear ownership, and slow decision-making continue to stall progress at critical moments. In an environment where AI operates at machine speed, the gap between what leaders see and how quickly they can act has become one of the biggest barriers to value.
This session explores how leaders are moving beyond visibility into execution, rethinking decision rights, operational alignment, and accountability to accelerate AI from insight to action and deliver measurable outcomes at scale.
Track 2: Proving AI ROI: Moving Beyond Experimentation to Accountability
Organizations have invested heavily in AI, yet many still struggle to prove its value. Dashboards show activity, pilots demonstrate potential, but clear financial impact remains elusive. At the same time, rising costs, unclear metrics, and disconnected initiatives are making it harder for executives to justify continued investment.
This session focuses on how leaders are redefining success—moving beyond vanity metrics to measurable business outcomes. It explores how to align AI initiatives to revenue, efficiency, and risk reduction, while embedding accountability into every stage of the lifecycle.
Track 3: Governing AI at Scale: Ownership, Risk, and the Future Operating Model
As AI becomes embedded across the enterprise, organizations are facing a new reality: more autonomy, more risk, and less clarity on who is accountable. Leaders often believe governance is in place, yet in practice, siloed teams, inconsistent policies, and shadow AI are creating exposure that is difficult to detect and even harder to manage.
This session examines how organizations are evolving governance from static frameworks to dynamic, operational models. It will explore how to define ownership, manage risk in real time, and redesign operating structures to support a future where humans and AI systems work side by side.
11:15am - 12:00pm
Visionary Implementation Interview
12:00pm - 12:30pm
Visionary Adoption Interview
2:00pm - 3:00pm
Roundtable Tracks
Track 1: Breaking Out of AI Pilot Purgatory
Most organizations aren’t struggling to start with AI—they’re struggling to scale it. Despite dozens of pilots, real enterprise impact remains limited. Fragmented ownership, disconnected data, and unclear success metrics are keeping AI stuck in experimentation while expectations from the business continue to rise.
This roundtable explores what separates organizations that scale AI from those that stall—focusing on execution, prioritization, and the operating discipline required to turn promising use cases into production outcomes.
Track 2: Data Readiness in the Age of AI: Foundation or Fiction?
Organizations often believe they are data-ready for AI—but reality tells a different story. Siloed data, inconsistent quality, and limited accessibility continue to undermine even the most advanced AI initiatives. As AI shifts from human-driven queries to machine-driven consumption, the cracks in existing data strategies are becoming impossible to ignore.
This roundtable focuses on what true data readiness looks like in practice—and how leaders are rethinking data architecture, ownership, and governance to support AI at scale.
Track 3: Who Owns AI? Governance, Accountability, and Risk at Scale
As AI becomes more autonomous and embedded across the enterprise, accountability is becoming increasingly unclear. Leaders believe governance is in place, yet shadow AI is growing, policies are inconsistent, and decision ownership is often undefined. The result: increased risk, slower adoption, and hesitation at the executive level.
This roundtable explores how organizations are evolving governance models to match the speed and scale of AI—defining ownership, embedding accountability, and managing risk without slowing innovation.
2:00pm - 5:25pm
1:1 Meetings
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