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The Single Biggest Barrier to AI Adoption Isn't the Technology — It's This | Errol Gardner of EY

54 min episode · 2 min read
·
Errol Gardner

Episode

54 min

Read time

2 min

Topics

Leadership, Artificial Intelligence, Software Development

AI-Generated Summary

Key Takeaways

  • Agentic AI adoption scale: Enterprise agentic AI sits below 1 out of 10 on an adoption scale, with only roughly 20% of organizations using it in any production capacity — and even then, only within a small fraction of their overall business operations. Cloud took 15 years to reach approximately 7 out of 10, and agentic AI faces steeper implementation hurdles.
  • Data governance before experimentation: Organizations must establish private LLMs within their own firewall before encouraging employee AI experimentation. Without this, employees using consumer AI tools will inadvertently expose corporate data to public training models. EY built a controlled internal LLM first, then rolled out access to its 400,000 employees with monitored guardrails.
  • Change management outweighs technology readiness: The primary barrier to enterprise AI deployment is human resistance across all organizational levels — leaders, middle managers, and frontline workers. Workforce anxiety about job displacement drives active resistance to enterprise-grade agentic systems, making structured communication from the C-suite about AI philosophy a prerequisite for adoption.
  • Intergenerational leadership gap: Younger employees readily experiment with AI tools, but frequently report to senior leaders from generations less inclined to adopt or reward AI-driven innovation. Organizations deploying AI should audit whether management layers are actively incentivizing AI experimentation or inadvertently penalizing it through traditional performance and output measurement frameworks.
  • Consulting shifts from inputs to outputs: AI forces consulting firms to move away from billing measured by hours and days worked toward outcome-based delivery models. EY uses this internally — AI enables faster deliverable production, which compresses traditional time-based billing assumptions and requires renegotiating how client engagements are scoped, priced, and measured.

What It Covers

Errol Gardner, EY's global consulting leader, examines where agentic AI actually stands in enterprise adoption, why human resistance — not technology — blocks deployment, and how large organizations like EY's 400,000-person firm are navigating GenAI and agentic workflows in real production environments.

Key Questions Answered

  • Agentic AI adoption scale: Enterprise agentic AI sits below 1 out of 10 on an adoption scale, with only roughly 20% of organizations using it in any production capacity — and even then, only within a small fraction of their overall business operations. Cloud took 15 years to reach approximately 7 out of 10, and agentic AI faces steeper implementation hurdles.
  • Data governance before experimentation: Organizations must establish private LLMs within their own firewall before encouraging employee AI experimentation. Without this, employees using consumer AI tools will inadvertently expose corporate data to public training models. EY built a controlled internal LLM first, then rolled out access to its 400,000 employees with monitored guardrails.
  • Change management outweighs technology readiness: The primary barrier to enterprise AI deployment is human resistance across all organizational levels — leaders, middle managers, and frontline workers. Workforce anxiety about job displacement drives active resistance to enterprise-grade agentic systems, making structured communication from the C-suite about AI philosophy a prerequisite for adoption.
  • Intergenerational leadership gap: Younger employees readily experiment with AI tools, but frequently report to senior leaders from generations less inclined to adopt or reward AI-driven innovation. Organizations deploying AI should audit whether management layers are actively incentivizing AI experimentation or inadvertently penalizing it through traditional performance and output measurement frameworks.
  • Consulting shifts from inputs to outputs: AI forces consulting firms to move away from billing measured by hours and days worked toward outcome-based delivery models. EY uses this internally — AI enables faster deliverable production, which compresses traditional time-based billing assumptions and requires renegotiating how client engagements are scoped, priced, and measured.

Notable Moment

Gardner challenges the cloud adoption benchmark by arguing that even after 15 years, cloud penetration in enterprises is likely below 70% when measuring actual depth of usage — not just organizational uptake — making agentic AI's timeline to meaningful scale far longer than current market enthusiasm suggests.

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Episode Transcript

Most organizations have embraced the cloud and have moved most of what's movable to the cloud. It seems like it's sort of morphing into a a new industry. The single biggest impediment for any organization changing is usually something related to the human beings. It could be the leaders. It could be the sponsors. It could be middle managers. It could be the workers. It could be whole it could be a combination of. There's huge anxiety in the workforce about whether AI effectively is going to dis displace their job. Okay. Well, I'm interested in talking because I speak quite a bit to one of the other big consulting firms. I actually do some, writing for them. And so I have a good sense of of what how they view, this world, the new AI and particularly a Gentic world. And I thought this would be an opportunity to hear from, you guys, how you see the corporate landscape, the penetration of AI in, production in large corporations, and in particular, Agentic Systems. Because the you know, I I end up having a lot of startups on the podcast, and they're all selling systems, and they all say, oh, it's you know, everyone's using our system. But, you know, when you get beyond an agent, you know, watching your e email inbox, I'd it gets more complicated, and I'm I just that's what I'm interested in. So, usually, I have the guest start by introducing themselves, giving a little bit of their background and so far as it's relevant, or, either consulting or AI. And then maybe you can start by talking about view or or thesis on what's happening with AI in the workforce or in enter in the enterprise, I should say. Well, thank you for having me. It's it's great to be speaking to you about this, the, arguably the hottest topic, in most, corporate environments, government environments, and many other organizations. So my name is Errol Gardner. I lead our consulting business here at globally. I've been doing that for about six years. I've been in this industry for over thirty five years. So I've seen a number of different technology trends during that time, and probably can look at AI in the context of many things that have been historically spoken about as changing the world very rapidly, and then maybe give some context on that. But look forward to talking to you about all these different topics. And maybe just start in terms of, how I see AI currently within the corporate environment and what businesses are doing and how they're responding to that. I think the first thing maybe just to to highlight, is is actually the reality that change within large organizations is very difficult to execute. So, anybody who references or or pretends that to be otherwise, I think, is, either has never done it before, or potentially, maybe being slightly economical with what is achievable. And there are lots …

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