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AI FinOps: The New Discipline of Measuring the Cost of Intelligence
Sep 9, 2026
How enterprises can trace AI spend from tokens and models to use cases, outcomes, and business value.
Every company buys AI tech.
Few make it work at scale.
It's not just SMBs, 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.
ENTERPRISE AI FAILURE POINTS
Eight systemic areas where AI at scale underperforms.
Click any card to inspect the hard data.
01
Strategy & Value Realization
22% say AI returns met or exceeded expectations
02
Data Readiness & Privacy
87% say poor data quality impairs AI value
03
Integration & Enterprise Scaling
57% deploy AI in 3 or fewer business functions
04
Governance & Accountability
78% unready for an independent AI audit in 90 days
05
Security & Operational Resilience
Only 20% have tested an AI incident response plan
06
Talent & Change Management
78% say AI skills matter — only 33% train all employees
07
Reliability, Ethics & Trust
Hallucination rates range from 22% to 94% across leading models
08
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.
NOTES FROM THE TRENCHES
Perspectives from the field.
Why Enterprise RAG Breaks as AI Scales
Sep 1, 2026
Retrieval-Augmented Generation (RAG) hits performance ceilings, creating a massive execution bottleneck for enterprise deployments.
FROM THE FIELD
See the Work Behind the Thinking
Real case studies from enterprise AI deployments. What we built, what broke, and what we learned.
2 Dozen Studios, Billions of Events Daily, and Making Sense of It All
25+ studios. Fragmented data. No way to make sense of it at speed.
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Interested in comparing notes?
I'm always open to thoughtful conversations with enterprise leaders, builders, investors, and others working through the realities of AI at scale.
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