As a Strategy Advisor at DNYC, I've witnessed firsthand how the AI revolution is fundamentally reshaping executive leadership requirements. The question is not whether senior leaders need to adapt, but how quickly and effectively they can develop the critical capabilities necessary to navigate this transformation successfully.
Based on my extensive consulting experience, I believe the key for senior leadership lies in combining technical understanding with business acumen whilst using interpersonal skills to navigate increasingly complex stakeholder dynamics. Let me share the specific capabilities I've identified as absolutely essential:
As both an executive and non-executive, I don't need to understand code, but I must understand AI's capabilities, limitations, and business implications (especially costs!!) to make informed decisions. This is not about becoming a technologist: it's about developing enough technical fluency to ask the right questions to the right people and evaluate proposals critically.
The leaders I work with who succeed in AI transformations are those who can engage meaningfully with their technical teams without getting lost in implementation details. They understand the difference between what AI can do today versus what vendors promise it might do tomorrow.
Senior leadership must be able to reconcile business growth with legal imperatives, particularly as AI touches compliance, risk, operations, and customer experience simultaneously. In my work with fintech clients, this capability has proven absolutely critical: AI initiatives rarely exist in isolation and almost always create ripple effects across multiple departments.
I have seen too many well-intentioned AI projects fail because leadership could not effectively coordinate across functions or anticipate the broader organisational implications of implementation.
Drawing on my experience negotiating with tech developers, leaders must arbitrate conversations between technical teams and business stakeholders, ensuring AI initiatives remain both strategically aligned and technically feasible. This is perhaps one of the most underestimated skills in AI leadership.
Technical teams often focus on what's possible, whilst business stakeholders focus on what's desirable. The executive's role is to find the intersection—what's both possible and valuable—and communicate that vision clearly to all parties involved.
I regularly steer organisations through the cultural shifts and workflow changes that AI transformations demand whilst maintaining momentum. This is probably one of the most difficult aspects, like trying to change the tyre of a car without stopping.
AI implementations do not just change what people do; they often change how people work, collaborate, and make decisions. Leaders must be prepared to manage this human dimension of transformation whilst keeping business operations running smoothly.
This is the ultimate responsibility of every leader: building sustainable, trustworthy AI systems that create long-term value whilst proactively managing risks and compliance requirements. In my advisory work, I consistently emphasise that ethical considerations aren't afterthoughts. Instead, they are foundational to successful AI implementation.
Leaders who treat ethics as a compliance checkbox rather than a strategic imperative invariably run into problems down the line.
When clients ask about particular resources, networks, mentors, or methodologies I rely on to remain informed about the rapidly evolving AI landscape, my answer often surprises them: I don't follow any particular resources.
The sheer volume of vendor content, webinars, and "breakthrough" announcements makes it incredibly difficult to separate genuine innovation from incremental improvements wrapped in compelling marketing. The noise-to-signal ratio in AI content is frankly overwhelming.
Instead, I keep an eye on venture capital investments and try to follow the money to see which solutions or players attract investors' attention. This isn't a guarantee of success, but it's an indication of what's actually being developed and where serious money is betting on real innovation rather than marketing hype.
This approach has served me well: investors with significant capital at stake tend to conduct more rigorous due diligence than content marketers trying to generate leads.
What I have learnt from advising executives through AI transformations is that development in this space is not about reaching a destination. It is about building the capability to continuously adapt and learn. The specific AI tools and techniques will continue evolving rapidly, but the fundamental leadership capabilities I have outlined remain constant.
The executives who succeed are those who focus on developing these enduring skills rather than trying to keep up with every new AI announcement. They build strong foundations in strategic thinking, stakeholder management, and ethical decision-making, then apply these capabilities to whatever AI developments emerge next.
As I continue working with senior leaders navigating this landscape, I'm reminded that the most powerful AI tool any executive can develop is their own judgement, i.e. the ability to cut through the hype and focus on what actually creates value for their organisations and stakeholders.
I have spent considerable time working with clients to establish robust AI governance frameworks. One of the most critical questions I encounter regularly is how to structure AI leadership effectively. The answer, I've found, depends largely on the nature of the organisation itself.
My clients are primarily small to medium-sized businesses that are tech-natives, and this presents a unique opportunity to structure the broader governance. Rather than centralising AI leadership in a single role or department, I've observed that distributed leadership across functions and roles proves remarkably effective.
The tech side of these businesses tends to be naturally solution-oriented, diving deep into the technical possibilities and implementation details. Meanwhile, the leadership and control functions maintain their focus on outcomes and overall business sustainability whilst leveraging AI. This division of labour creates a natural balance—technical innovation paired with strategic oversight. It helps when the overall structure is generally flat and the headcount stays between 70 and 100 individuals.
In my experience, this structure works precisely because it mirrors how these organisations already operate. They're built for agility and cross-functional collaboration from the ground up.
Given the specificities of the fintech space, I actively advise against creating dedicated executive roles such as a Chief AI Officer. Here's why: fintech companies are already required to maintain mature Risk and Compliance functions, and these control functions must encompass AI thematics within their scope and day-to-day responsibilities. Adding another layer creates unnecessary complexity and potential confusion about accountability.
Similarly, any successful fintech organisation must have Product and Development teams that are future-oriented and naturally include AI in their roadmaps. The expertise and accountability already exist within the organisation—it's simply a matter of ensuring these functions evolve to encompass AI considerations.
I've seen this pattern before. In the past, there were calls for Chief Information Officer (yes, I am that old!), Chief Digital Officers or Chief Cloud Officers (yes, there are “creative” businesses out there!), particularly amongst more traditional players in the financial services space, as those technologies were emerging and rapid transformation was occurring. It didn't succeed, and it shouldn't be replicated for AI, especially not within digitally native organisations that already possess the structural agility to adapt.
When working with clients on AI initiatives, I focus heavily on facilitating cross-functional collaboration, which is already at the heart of any successful fintech organisation. The beauty of the fintech model is that services and products are already subject to strict regulatory frameworks and internal controls.
The introduction of AI simply follows the same established pattern—it's an additional technical layer added to the services and products offered to clients. This means the governance structures, accountability mechanisms, and collaborative processes are already in place. We're not reinventing the wheel; we're ensuring the existing wheel can handle the additional load.
In practice, this means ensuring that Risk and Compliance teams understand AI implications, that Product teams consider AI opportunities in their roadmaps, and that Development teams can implement AI solutions within existing governance frameworks. The key is coordination, not reorganisation.
What I've learnt from working with these organisations is that accountability for AI initiatives works best when it is embedded within existing structures rather than layered on top of them. Risk teams remain accountable for managing AI-related risks, Product teams own AI feature development, and senior leadership maintains strategic oversight, just as they would for any other technology or business initiative.
This approach ensures that AI becomes integrated into the organisation's DNA rather than treated as a separate, parallel concern. It also prevents the common pitfall of creating AI initiatives that exist in isolation from core business objectives and governance structures.
As I continue to advise clients on AI governance, I'm increasingly convinced that the most effective approaches are those that build upon existing strengths rather than creating entirely new structures. For tech-native organisations in particular, the distributed, collaborative model offers the perfect balance of innovation and accountability.
The future of AI governance is not about creating new hierarchies: it's about ensuring existing ones evolve intelligently to meet new challenges whilst maintaining the agility that makes these organisations successful in the first place.
Over the past few weeks, heavyweight institutions have lined up to deliver their verdict. First, the Bank for International Settlements (BIS) declared that stablecoins “perform badly” against criteria for sound money. Then ECB adviser Jürgen Schaaf weighed in with his own analysis.
The headlines were predictable: stablecoins lack central bank backing, they enable illicit activities, and they can’t serve as lenders of last resort. The FT’s coverage — which I’ll admit led me astray initially — painted a stark picture of technological innovation versus institutional stability.
But here’s what caught my attention: while the BIS delivered exactly what you’d expect from the world’s central banking hub (surprise: they prefer central banks!), Schaaf’s take was refreshingly nuanced. Yes, he warned about monetary sovereignty. But he also acknowledged something crucial — the opportunities are real and shouldn’t be dismissed.
This got me thinking. Are we having the right conversation about stablecoins? Or are we trapped in a binary debate that misses the bigger picture?
Myprofessional journey provides a multifaceted perspective on finance. Beginning with solid grounding in finance, strategic transactions, and macroeconomics, my career evolved into fintech and digital payments leadership. I drove innovation and growth strategies while navigating complex regulations. Now, as board advisor and finance committee chair, I bridge vision with execution to ensure solid governance and financial sustainability. As an avid reader staying current on key development in the industry, I’m at the center of heated stablecoin debates. From this vantage point, I felt compelled to write this article, offering a clear perspective on one of modern finance’s most critical topics.
After diving into these institutional positions and the broader discussion, I’ve summarised some of the points I think are missing from this debate. Because when you strip away the ideology and look at what’s actually happening in markets today, the stablecoin story becomes far more interesting — and important — than either critics or champions want to admit.
Let me share my humble musings.
Currency pegging has always been a high-stakes game — even for central banks. During the 1992 European Monetary System crisis, respectable institutions defending official pegs still couldn’t prevent the inevitable collapse. Yet the EMS was a necessary stepping stone to the euro, proving that sometimes imperfect solutions serve their time and purpose.
The issue of today’s stablecoin debate is the temptation of treating all stablecoins the same ignoring structure and context.
A USD stablecoin issued under MiCA’s stringent framework is worlds apart from a rouble-pegged token out of Kyrgyzstan (yes, it exists — look it up). Lumping them together makes as much sense as comparing the Swiss franc’s historical peg to the euro with Argentina’s various failed dollar pegs. Same mechanism, vastly different realities.
The truth is, every stablecoin is only as credible as the institution defending its peg. Just like traditional currency pegs, it’s not the structure that matters — it’s the execution. That is precisely why MiCA focuses on strict issuer requirements.
Today the strongest demand for stablecoins is coming from developing economies, already effectively dollarised in real terms, where local currencies are extremely volatile and financial institutions weak. There, USD-backed stablecoins aren’t ideological experiments — they’re survival tools. When your government’s dollar peg is disintegrating or your savings are evaporating, that any stablecoin suddenly looks like the most stable option available. The considerations from Frankfurt or Basel might be irrelevant in Buenos Aires or Lagos.
Hence, the BIS take on stablecoins is too generalised to hit the mark. Stablecoins are not all “poor performers” or inherently bad because of the peg structure.
Let’s move past the false equivalence: stablecoins aren’t official currencies (or fiat money), and outside of ideological echo chambers, everyone knows it. But here’s what’s interesting — official currencies aren’t the flawless paragons we pretend they are either.
Liquidity? It’s relative. Ever tried spending a €100 bill in rural Provence? I have. Despite holding perfectly legitimate euros backed by the ECB, I might as well be holding Monopoly money. My “official” currency becomes remarkably illiquid when the local café only takes cash and won’t break large bills.
Critics worry stablecoins enable illicit transactions, but have we forgotten that USD banknotes — both real and counterfeit — remain the currency of choice for global illegal trade? Last I checked, this hasn’t toppled the Fed’s credibility or undermined the dollar’s dominance.
Which brings us to the real battleground: elasticity and leverage.
Remember 2008, when excessive leverage caused the global financial markets to implode and the ripple effects plunged the world into a dramatic credit crunch? It prompted unprecedented government and central banks interventions to stabilize the system. Central banks saved the day showcasing exactly why monetary elasticity matters, as the interest rates interventions quickly trickled through the markets and the real economy.
This is the material risk affecting stablecoins and real currency alike and here’s what keeps central bankers up at night: highly leveraged stablecoins could recreate 2008’s conditions, but potentially on steroids and depriving central banks of effective intervention tools.
The stablecoin debate isn’t about whether they are “real” money. It’s about whether we’re building a parallel system that amplifies the very risks we barely survived before.
That’s a conversation worth having — minus the ideology.
Let’s challenge a fundamental assumption: Do stablecoins need to tick every box of traditional money to have value? I don’t think so — and here’s why.
Right now, stablecoins excel at one critical function: settlement. They’re making cross-border wholesale transfers faster and cheaper than traditional correspondent banking ever could. In short-term transactions, where default risk is minimal and central bank backstops matter less, they’re actually outperforming the status quo.
This is precisely why forcing stablecoins into the traditional money mold misses the point entirely. They don’t need to be units of account — that role stays with the currencies they reference. They’re not designed for long-term value storage — that’s what pure cryptocurrencies attempt to do. And honestly? That’s perfectly fine.
Yes, the BIS is right that stablecoins lack monetary sovereignty. Yes, their pegs can be shaky. But demanding they fulfill all three functions of money is like criticizing a Formula 1 car for having poor cargo space. It’s ideologically neat but practically irrelevant.
The proof? Six months after MiCA’s introduction in the EU — with its stringent asset-backing requirements — stablecoin usage remains robust. Despite the regulatory friction, they continue to thrive in their sweet spot: efficient settlement.
Sometimes innovation means doing one thing exceptionally well, not everything adequately. That’s the stablecoin story, and it’s one worth understanding beyond ideological debates.
Let’s be clear: dismissing stablecoins based on ideology while ignoring real-world evidence is a mistake we can’t afford to make.
Right now, stablecoins are doing what correspondent banks couldn’t — making cross-border wholesale transfers faster and cheaper. That’s not theory; that’s happening today.
Schaaf hits the nail on the head: the real concern for central banks isn’t whether stablecoins exist, but whether they’ll create an uncontrolled synthetic money supply that undermines monetary policy effectiveness.
That’s the conversation we should be having. Everything else is noise.
The future of finance isn’t about choosing sides — it’s about understanding what works, why it works, and how we can harness innovation without losing control of what matters most.