Every company buys AI tech.
Few make it work at scale.

90% of enterprises are running AI experiments, but only 22% see real financial returns. The gap is not the technology—it is governance, integration, and the discipline to turn pilots into defensible operating capability.

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ENTERPRISE AI FAILURE POINTS

Eight systemic areas where AI at scale underperforms.

Click any card to inspect the hard data.

Strategy & Value Realization

22% say AI returns met or exceeded expectations

“Your AI portfolio may be producing demos faster than defensible earnings.”

90% of organizations report employees using AI — only 22% see expected returns
Organizations with fully integrated AI see 58% revenue growth vs. 15% for those still piloting
89% say technology investments have not fully delivered expected results

ISACA 2026 AI Pulse Poll · Grant Thornton 2026 AI Impact Survey · PwC 2026

Data Readiness & Privacy

87% say poor data quality impairs AI value

Integration & Enterprise Scaling

57% deploy AI in 3 or fewer business functions

Governance & Accountability

78% unready for an independent AI audit in 90 days

Security & Operational Resilience

Only 20% have tested an AI incident response plan

Talent & Change Management

78% say AI skills matter — only 33% train all employees

Reliability, Ethics & Trust

Hallucination rates range from 22% to 94% across leading models

Cost Transparency & AI FinOps

Only 26% have real-time AI cost visibility

MY PRACTITIONER'S PERSPECTIVE

Why I work on solving these.

When you look at these eight areas, it becomes clear why so many enterprise AI initiatives stall: these are the foundational friction points that determine whether AI scales or stays an expensive experiment.

This is precisely why I focus my work here.

While generative AI introduces unique technical complexities, its core structural hurdles (governance, security, change management, and data readiness) are fundamental challenges that occur during any once-in-a-generation technological shift.

My last three years inside Electronic Arts leading AI product and engineering teams don't stand alone. They build on a 22-year foundation of navigating exactly these kinds of enterprise disruptions and landing deployments successfully. Long before LLMs, directing audited identity infrastructure at Visa or leading global commercial platform transformations at EA taught me the same underlying truth: scaling technology is ultimately a discipline of governance, workflow redesign, data, and organizational change.

I care about these failure points because the playbook for enterprise AI at scale isn't being invented from scratch. It is being adapted by seasoned practitioners who have led large-scale enterprise transformations before.

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