Oluwaseyi Ayodeji
AI infrastructure leader | Bridging Power, Silicon, Enterprise Governance and Workforce Scale.
Dallas, Texas, United States
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Oluwaseyi (Olu) Ayodeji is an AI infrastructure leader who specializes in the physical and systemic layer of artificial intelligence: the energy grids, hyperscale compute, semiconductor supply chains, and talent pipelines that dictate who controls the global AI economy.
With over two decades in tech, ranging from semiconductor process engineering and server manufacturing automation to cloud logistics, Olu bridges the gap between hardware engineering, enterprise policy, and business execution. At Google Cloud Logistics, he founded GCL University, an enterprise learning framework built on the Kirkpatrick Model that upskills a global organization of 300+ internal employees and an extended workforce in the thousands, all managing machine learning infrastructure deployment from rack to tray.
As the creator of Regal Stack and author of the Stack Sovereignty Test, a six-pillar framework for evaluating national and enterprise compute capacity, Olu provides actionable blueprints for organizations navigating digital sovereignty, infrastructure bottlenecks, and workforce transformation. His commentary on technology policy, data center scale, and emerging compute economies has been featured across TechCabal, ThisDay, The Nation, Nairametrics, and News Central TV.
Based in Dallas–Fort Worth, Olu speaks regularly to enterprise leaders, infrastructure developers, and policymakers on how to build, power, and govern scalable AI capacity.
Area of Expertise
Topics
Rolling Out AI Agents to a Team: What Training 200+ Professionals Taught Me About Adoption
Your team has coding agents now. Some engineers ship twice as fast with them. Others tried once, got a bad diff, and went back to doing it by hand. Most teams treat that split as a tooling problem and go shopping for a better model.
In my experience, it’s rarely the tool.
I’m not an Android developer, and I won’t pretend to be. I spent two decades in tech, including time as an automation testing engineer building acceptance tests into server manufacturing equipment, and I now work in cloud infrastructure program management for a global AI infrastructure supply chain organization. Over the past year I trained more than 200 program managers to hand parts of their governance work to AI tools and agents. The people, the skeptics, the early adopters and the quiet drop-offs looked a lot like what Android leads are describing with coding agents right now.
This session is about the human side of agent adoption: who picks it up and who doesn’t, why “everyone has access” isn’t adoption, and how to tell whether agents are changing how your team works or just getting opened. I’ll share what worked in my rollout, what failed, and a simple four-level way to measure adoption, adapted from the Kirkpatrick Model of training evaluation.
Key Takeaways
1. Access isn’t adoption. Why license counts and login data say almost nothing about whether agents are changing your team’s work, and what to track instead.
2. The four levels of agent adoption: reaction, capability, behavior change and results, and the questions to ask at each level.
3. Delegation is a skill, not a setting. How to teach people when to hand work to an agent, how to check what comes back, and when to take the keyboard back.
4. Spotting the quiet drop-off. The early signs someone has given up on an agent, and how to bring them back without a mandate.
5. Leveling up in an agent-heavy team. How individuals can become the person who makes agents work for everyone else, which is quickly becoming a career advantage.
The Agent Works. Your Organization Doesn’t: Why Agentic AI Fails at Adoption, Not Architecture
Most companies treat agentic AI as an engineering problem. Pick the model, tune the retrieval, wire up the tools, ship it. Then the pilot stalls. Usage fades after a few weeks, teams drift back to their spreadsheets, and leadership decides the technology isn’t ready yet.
Usually, the technology is fine. The organization isn’t ready.
I’ll be honest about where I’m coming from. I’m not the person in this room who builds the best agents. I’ve spent two decades in tech, from auditing and consulting at PwC Nigeria to automation testing in server manufacturing to cloud infrastructure program management. My most recent lesson came from teaching more than 200 program managers in a global AI infrastructure supply chain organization to hand parts of their governance work to AI tools and agents. Some took to it in a week. Some never opened the tool again. The difference had almost nothing to do with the agent.
That’s the gap this session is about: whether people know when to delegate to an agent, how to check what it gives back, and how to rebuild their work around it. I’ll share what worked, what didn’t, and the agent-assisted systems we built to replace manual enrollment tracking, stakeholder updates, and reporting.
I’ll also walk through a measurement framework adapted from the Kirkpatrick Model of training evaluation. It tracks AI adoption at four levels: reaction, capability, behavior change, and business results. Most organizations stop at the first one. They count logins and course completions and call it adoption. Very few ever prove the fourth.
You’ll leave with:
A quick way to tell whether a stalled agent project has an architecture problem or an adoption problem
A four-level framework for checking whether agents are changing how work gets done, not just getting opened
A function-by-function approach to redesigning workflows around agents
Practical guidance for leaders building AI fluency in non-technical teams, and for anyone who wants to be the person who makes agents actually work
The Attack Surface Is on a Pallet: Why Your AI Security Strategy Starts Too Late
Most AI security conversations begin at the model: prompt injection, data poisoning, agent permissions, model governance. That focus is necessary, but it rests on an assumption almost nobody questions. It assumes the compute underneath is trustworthy.
Before an AI workload runs, it passes through a long physical chain. Silicon is fabricated, boards are assembled, firmware is flashed, servers are tested, shipped, received, racked, and commissioned, often across several countries and dozens of handoffs. Each handoff is a trust decision. Security teams rarely see most of them, because they sit with operations, procurement, and logistics, not with security.
Drawing on two decades across semiconductor process engineering, automated acceptance testing in server manufacturing, and cloud infrastructure program management, this session moves the AI threat model upstream. It shows where the physical AI supply chain is exposed, why AI-scale buildouts make that exposure worse, and how AI itself can turn manufacturing, testing, and logistics data into security telemetry.
The session also addresses a growing executive concern: compute is now a geopolitical asset. Export controls, chip diversion, and national “sovereign AI” programs mean hardware provenance is becoming a governance, risk, and compliance issue, not just a procurement detail.
Attendees will leave with a broader definition of AI attack surface and a practical framework for bringing infrastructure, operations, and security into the same conversation.
Oluwaseyi Ayodeji
AI infrastructure leader | Bridging Power, Silicon, Enterprise Governance and Workforce Scale.
Dallas, Texas, United States
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