Earley AI Podcast - Episode 83: AI, Governance, and the Execution Gap with Brian Stafford

In this episode, Seth Earley speaks with Brian Stafford, CEO of Diligent, a $700 million global software and AI company focused on governance, risk, and compliance. They explore why most organizations understand that AI is transformative but still struggle with the how of actually getting there, and what it takes to move beyond pilots into real operational change. Brian shares how Diligent is helping clients in compliance, audit, and risk functions do more with less through AI-wired software and agents, and why context, leadership, and process understanding are the real drivers of successful AI transformation.

Key Takeaways:

• Most organizations have crossed from asking what AI can do to struggling with how to actually execute and drive measurable transformation.
• Calling initiatives pilots gives organizations an excuse to fail - framing AI as transformation from the start changes accountability and outcomes.
• AI maturity is less about sector or company size and more about the quality and commitment of executive leadership driving change.
• Compliance, risk, and audit functions face a structural mandate - increasing obligations with flat or shrinking budgets - making AI adoption a necessity, not a choice.
• Agents should be thought of like a smart new associate - trained gradually, checked in with frequently at first, then trusted to operate with more autonomy over time.
• Context is the key differentiator for AI solutions - partners who already understand your domain, regulatory environment, and workflows will deliver faster, better outcomes.
• AI-native employees who are intellectually curious and fluent in modern tools can deliver 5 to 10 times the output of peers who resist adopting new capabilities.

Insightful Quotes:

"I hate the term pilot. Pilot gives organizations the license to call something unsuccessful. You're not piloting a transformation - you're either driving it or you're not." - Brian Stafford

"Most of our clients don't care if I ever said the word agent. They care about an outcome. The technology is just what helps deliver it." - Brian Stafford

"You can't automate what you don't understand. And once you do understand it, agents change everything - but the process clarity has to come first." - Seth Earley

Tune in to discover how forward-thinking leaders are closing the gap between AI ambition and real operational impact across governance, risk, and compliance functions.

Links

LinkedIn: https://www.linkedin.com/in/brian-k-stafford/

Website: https://www.diligent.com

Ways to Tune In:

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Podcast Transcript: AI, Governance, and the Execution Gap

Transcript introduction

This transcript captures a conversation between Seth Earley and Brian Stafford about the gap between AI ambition and operational execution, and how organizations can close it. They discuss why framing matters, how agents are reshaping governance and compliance functions, and why context and process understanding are the true foundations of successful AI transformation.

Transcript

Seth Earley: Welcome to the Earley AI Podcast. I'm your host, Seth Earley, and each episode we jump into different challenges and issues around AI adoption - how organizations need to be prepared, how they need to execute, and how they need to operationalize AI. Today we're looking at how AI is influencing governance, risk, and compliance, and what it takes to lead in this rapidly changing environment. The technology is changing so quickly that it's very difficult even for experts to keep up. Joining me today is Brian Stafford, CEO of Diligent, a $700 million global software and AI company focused on governance, risk, and compliance. Brian has served as CEO for the past 10 years, guiding the organization through growth and innovation. Before joining Diligent, he was a partner at McKinsey and Company and an entrepreneur. Based in New York City, he draws on a diverse background in consulting, entrepreneurship, and executive leadership to help organizations navigate complex challenges. Brian, welcome to the show.

Brian Stafford: Thanks for having me, Seth. I'm looking forward to the discussion.

Seth Earley: I'd like to start with misconceptions around AI. What are organizations not getting? When you go into an enterprise, what are the things that are still great misconceptions on the part of leadership, boards, and organizations?

Brian Stafford: For most organizations where we stand now, at least from a CEO perspective, there is a widely held belief that AI is going to be transformative. So I think we've crossed - for most organizations - beyond the what, and now many are into the mode of how. How do you actually get there? How do you actually achieve the productivity, the insights, the growth? And I think a lot of organizations are still struggling with the how. I joke about what I call website AI - you go to a company's website and it talks about all these AI capabilities, but in practice it amounts to a button that does really basic stuff. How do you actually fundamentally drive that change? That's where the gap is.

Seth Earley: So it's not necessarily a misconception - it's a lack of knowledge. They understand it's going to be transformational, but they don't know how to get there.

Brian Stafford: Exactly. A CEO reads that function X is going to be made 50% more efficient, then goes and looks at that function and asks, how do we actually get there? You see some organizations that are further along, but many are still in the early stages. The CEO needs to be the champion of AI transformation, but also needs to make sure there are people capable within the organization who can actually take it on and drive it.

Seth Earley: A lot of organizations are doing point solutions, running pilots that don't get out of pilot mode. What are your thoughts about readiness and maturity, and do you help your client organizations understand that?

Brian Stafford: I hate the term pilot. I really, really hate it. Pilot gives organizations the license to call something unsuccessful. And a stat came out around six months ago that said 70% of AI initiatives fail - well, that's because you call them pilots and give people excuses to say the pilot didn't work and move on. AI is ultimately a transformation across an organization. As part of that transformation, you'll have things that work better than you thought and things that didn't work as well. But you're not piloting a transformation - you're either driving it or you're not. The most successful organizations are aware of what the problem is, upskill their teams, build operational capabilities, and have the leadership and vision to drive it through with the right governance around those projects being successful long-term.

Seth Earley: We call them proofs of value, because we want to use production data and make sure it can be scaled and operationalized. So I'm with you - proof of concept is often just an excuse to not succeed. Are organizations understanding that this needs to be multi-dimensional and embedded holistically across the organization?

Brian Stafford: I can give you the view from two sides - the transformation we're driving at Diligent, and the transformation our clients are driving. I'm really passionate about this: we're a company that sells software and AI to clients who are ultimately looking for outcomes. I fundamentally believe that making sure we're driving an effective AI transformation internally will make our products better, because we see what works in our own transformation across departments. And when I look internally, I'm not looking for people buying products - I'm looking for people transforming and getting more leverage out of their business. When we go into clients in legal, compliance, audit, and risk departments, it's helpful to know that each of those departments likely has an AI-driven goal handed down by the CEO. So you don't talk about features - you talk about how you can help them transform their compliance organization so they can meet all their obligations with the same or fewer people. People want to put a check mark next to "we're doing it through AI," but ultimately they're asking how to get much more efficient, and you can do that with AI-powered software.

Seth Earley: Tell me how Diligent helps catalyze those changes.

Brian Stafford: Diligent started as a governance company. We work with 70% of the Fortune 1000, 70% of the FTSE 100, two-thirds of the DAX 30, and thousands of mid-market companies globally. We help you improve how you do governance - how management connects with the board, how you make sure you have the right information, conversations, connectivity, and workflows around that. Over time, boards and CEOs started spending way more time on risk and compliance because the risks coming at organizations have increased dramatically over the last 10 years. Areas like compliance, risk, and audit are not getting massive incremental budget, but the complexity keeps increasing. Clients look at our software and ask: how do I keep up with an increasing set of needs when flat is the new up for budget? What we've done is wire everything we do with AI. So rather than just improving workflows, we now improve workflows and use agents. We tell clients: in order to meet and exceed your compliance requirements, you can now do it with a team augmented by agents that doesn't need to be as large. The mandate is clear - increasing compliance obligations with an expectation for decreasing budget - and you're not going to meet that any other way than with agents.

Seth Earley: When you think about governance and what a board and CEO need to do - they need to understand how AI investments are working, how well they're getting return, how things are progressing. How do you help facilitate that?

Brian Stafford: Let me walk through the three main areas - governance, risk, and compliance. On the governance side, the question is: we have goals we've committed to our board, how do we make sure we're driving the right process to track those goals and tie activities to what we've communicated to the board and shareholders? We use AI to make it easy to track longitudinally - what did I say, what did I commit, how am I tracking, how does it tie to internal initiatives. On the compliance side, it's a cost optimization and efficiency job, making sure you don't lower the bar from a compliance obligation standpoint. The same is true of audit. On the risk side, the challenge is signal and noise. With everything coming at you, how do you separate the signal from the noise? AI gives you the ability to do that in a really powerful way. We make it very accessible for organizations to continuously monitor risk, because risk is what will keep you from achieving the goals you committed to your board and being successful in your AI transformation.

Seth Earley: When you look across your customers and industries, how would you classify their preparedness and maturity? Are there patterns by sector or size?

Brian Stafford: Fewer and fewer organizations are not at least AI-aware. It's going to be required - by their board, their shareholders, or just to keep up with competition. We did have clients initially who said they weren't touching AI, but a year later were asking how to pick the right places to experiment. I think you have a lot of clients in the launching-pilots phase who are going to have to manage through the fact that it can't just be pilots - it has to be transformation. Then you see the next tranche actually tackling functions and driving real change. And then there's a truly unique set of companies actually transforming their organizations with AI - more of those tend to sit in Silicon Valley and other innovation hubs, and they're 10x more productive than the average organization.

In terms of patterns, I have not seen as much variance by sector. Remove technology companies, where AI is as much their product as their business. For other organizations, it's much more leadership-driven than sector-specific. I was blown away by one large insurer who is a client - their CEO mandated that every single role, up to and including the board, has a digital twin: an AI capability built for each individual that they expect will eventually be able to perform that role. I would assume there are other insurers who are not as aggressive and are just picking a few departments to experiment. It really is as much leadership-driven as anything else. I'll also say I've been more impressed than I expected by European companies leaning into transformation - partly because excessive regulation in some places means AI is the only viable way to keep up with compliance demands.

Seth Earley: Where do you observe that organizations have the greatest gaps in their preparedness and ability to execute?

Brian Stafford: There is a massive gap in ability to execute - I think it's a chasm. It sits between the conversations that happen at the CEO and board level, even for the most committed CEOs, and the actual how. Reading a case study about how an organization made their support function 2x as productive is very different from actually doing it. Some companies and partners are helping to fill that gap, and organizations that find the right partner who can help them drive that transformation are growing really fast. Those that can't find the right partner get stuck trying to build the capabilities themselves. You see the impact most clearly today in functions like developer tools, engineering productivity, and customer support. For other functions, the right partner can help you accelerate dramatically.

Seth Earley: One area we're seeing a lot of interest in is the software development lifecycle - inserting coding assistance from requirements and user stories through documentation. And on the customer-facing side, call deflection, self-service, and support bots. The biggest gap we find there is data and content. Is that surfacing for your clients?

Brian Stafford: Absolutely. It all starts with having the right data in the right places. For most of our clients on the Diligent side, they already have their data in our systems, which is a significant advantage. The ability to take that and transform it - make it more accessible, continuously monitor it, add agents on top of it - is massive. For clients, they're already part of the way down the journey because the information is in the right system. The question then becomes: are you managing that workflow manually, or do you have agents running it? For our governance, risk, and compliance areas specifically, there are relatively standard workflows that just need agents added to automate. When the data is already in Diligent, it makes it really easy for us to help clients add agents and take things through the full useful life of that data.

Seth Earley: How do you see the agentic landscape impacting compliance and risk management? You're introducing capabilities that can operate somewhat autonomously, but you still need the right guardrails and process understanding. I like to say you can't automate what you don't understand.

Brian Stafford: You need the right data and the right process - those are just foundational. Beyond that, it's interesting: most of our clients are not asking for agents. They're asking for capabilities and outcomes. They don't care if I ever said the word agent - they care about the outcome. I like to liken agents to that really smart associate from my McKinsey days - the person who does the job you ask them for, but also knows what the next step likely is and asks if you'd like to do that too. Over time, you trust them more. Maybe at first they check in with you multiple times a day, then at the end of each day, then weekly, and eventually you're just getting reports. You're training that agent the same way you train a team member. If it would take a month to get someone up and running in a role, expect some level of that with your agent too.

What we make sure we can do is maintain the right governance and audit trails. Anything related to risk, compliance, audit, or governance - you may be fine with the agent making 10 decisions on its own, but at some point someone is going to say, show me how we got to this endpoint, show me all the decisions made along the way. You need to be able to pull that string. That is just the nature of how enterprise systems powered by agents are going to work, and it is incredibly important in areas like risk, compliance, and governance.

Seth Earley: Context is also a critical differentiator. What's your view on that as AI capabilities become more commoditized?

Brian Stafford: Context is the key differentiator. If you can work with a partner who already knows your context, you're up and running much faster - and that's huge. Context is ultimately how your agents will make decisions within a specific business function, process, environment, or regulatory framework. If that context is nuanced, your current domain-specific partners who can deliver on it will thrive. If the context requirement is more generic, general-purpose LLMs will serve those needs incredibly quickly. You can always offer more context to those models and they'll get better, but the most relevant solution to your specific situation will always get you to your outcome fastest.

Seth Earley: Let's shift to the workforce dimension. We were talking about how AI is transforming the job market, and you had an interesting perspective on AI-native employees - people coming in with a new set of skills. How do you see that evolving?

Brian Stafford: I would disagree with the narrative that says there are not going to be roles for recent college grads. I need team members who are willing to be all-in on AI, adopt all the latest tools, and be 10x employees. I'm going to create every opportunity for my current team to become 10x team members, but if someone isn't intellectually curious and doesn't want to learn the next set of tools and capabilities, at some point you move on. When I look at new employees coming in who are already using all the most modern tools and functioning at 5 to 10x the output of other team members, I see tons of opportunities for those individuals. My advice to an early-stage potential employee is the same as to a late-tenured team member: you've got to be functioning with all the newest tools, you need to be intellectually curious, and you need to keep building your capabilities. That's how you scale your career. The people who are hungry, eager, and already using those tools can deliver that value faster than those who resist change.

Seth Earley: Where does the enablement Diligent provides leave off, and where does the customer need to find a partner or build their own capability?

Brian Stafford: There's a really interesting evolution happening across the software industry. In the past, you needed a partner to do a lot of arms-and-legs work to piece things together. With AI, that's decreasing materially for a lot of use cases - you can just get up and running much faster. Agents can go and collect, pull, tag, and access different systems to pull data directly. There will always be some need for partner support, but way less today than in the past, and even less in the future. People expect things to just work. For what we do - specific departmental and functional tools - we already have the context and know what your organization is likely trying to accomplish. You educate us a bit, then you can turn us loose and we'll help drive the change and outcomes you expect.

Seth Earley: Where do you see things evolving in the next couple of years?

Brian Stafford: There's never been a more exciting time in my career to be building product. AI offers so many incredible opportunities. The organizations that transform themselves around AI and deliver those capabilities to clients are going to do unbelievably well. But in any massive transformation, you'll see companies that don't make it - that don't transform and don't reach the next level. It's both an exciting and a scary time. We're growing faster than we have in many years, because clients are looking for partners who can help deliver outcomes. You can see the volatility in the stock market as the market tries to figure out who the next winners are. I think the winners are emerging - you just have to look hard to find them. And I think we find ourselves in a really exciting time and place to win in a big way.

Seth Earley: Agree. Well, Brian, thank you for your time and your insights. This has been a fantastic conversation. I look forward to having you back in the future.

Brian Stafford: Thanks, Seth. Really appreciate the time. Fun discussion.

Seth Earley: And for our listeners, we'll include links to Diligent and related resources in the show notes. Thank you all for listening, and we'll see you next time on the Earley AI Podcast.