The AI race has moved past experimentation. 2026 is about execution at scale. The winners won’t be the fastest adopters. They’ll be the ones with the governance to deploy AI decisively across their organizations. Everyone else is already behind.

When I was considering joining Dell Technologies in 2022, one thing stood out above all else. It was the culture around artificial intelligence. Dell had decided to take AI seriously. The organization was thinking disruptively, moving with intent, and treating itself as the first test case, not just the advisor. It made me curious and convinced me.

Nearly four years later, I can say the leap we’ve made in AI, both as an organization and in my own leadership, has been remarkable. It has fundamentally changed how I work, how I lead, and how I see the future, with a strong sense of optimism.

This shift is not just about productivity. It is about whether organizations can scale AI safely, effectively, and continuously innovate. At its core, this is a question of governance.

The leader who cannot look away

There is a temptation among senior executives to treat AI as a technology matter, as something to delegate to the CIO or CTO, while the real business of leadership continues elsewhere. That temptation should be resisted firmly.

A leader must have a horizontal view across the organization. Strategy, culture, operations, finance, and risk are all now shaped by AI. This is not something that can be delegated away from the top. Leadership teams that try to do so are not reducing complexity; they are allowing it to build, unseen and unmanaged.

My own experience confirms this. Since embracing AI tools in my daily work, my leadership has genuinely moved forward. I use my time more intelligently. I produce more value in the role, and I see the same effect ripple through the organization: people doing more meaningful work, freed from the routine tasks that once consumed their days. This is not a marginal efficiency gain. It is a qualitative shift in what leadership and professional work can mean.

Governance: The leadership trend that cannot wait

Among the many dimensions of AI leadership, one has emerged as the defining challenge of 2026: governance.  This is where the AI race will be decided, not in pilots, but in the ability to scale with control.

This is not primarily a regulatory question, though regulation matters. It is a leadership and competitiveness question. 

"As John Roese, Dell's global CTO and chief AI officer, wrote in a Dell blog post last December, “Top on the list is governance. We haven’t established strong governance frameworks yet.” He added that “governance in general will be a big deal in 2026,” and that inside the enterprise, “investment in a structured approach to AI will become a requirement.”

Companies are often moving faster than their organizational structures can absorb, from AI pilots to genuine production environments. In that transition, governance gaps appear. Who is accountable for an AI system's outputs? How is training data governed? What happens when a model fails, or behaves unexpectedly, at scale?

These questions are already surfacing in boardrooms. And the leaders who have clear answers will have a competitive advantage over those who do not.

Data is the asset and the vulnerability

AI does not merely use data. It amplifies data's value and its risk simultaneously.

Modern AI platforms ingest vast volumes of information, generate new data continuously, and concentrate an organization's most sensitive intellectual property in ways that were not true even five years ago. 

The security implications are direct. As Dell's President and Chief Security Officer, John Scimone, observed in a blog post last October: "Hackers go where the data is," and increasingly, that means where the AI is. 

This changes the risk calculus for leadership teams in a fundamental way. AI governance and data security are not separate conversations to be routed to different functions. They are two sides of the same strategic question: can we trust the systems on which our business depends?

An integrated, whole-of-company approach to risk and opportunity is no longer a best practice. It is a baseline.

Infrastructure as strategy

For much of the past decade, infrastructure was treated as a commodity, something to outsource, abstract away, or procure from whichever cloud provider offered the best commercial terms. AI has reversed that logic.

Where data resides, who controls it, and under what jurisdictional framework it is processed have become board-level questions. The concept of sovereign AI ensuring that data sovereignty, model ownership, and operational continuity remain under an organization's own governance is moving to practical architecture decisions.

The question organizations must now answer is not merely which AI tools to deploy, but what kind of AI platform to build on. 

Finland's moment if it chooses to take it

Finland carries some genuine advantages into the AI era. 

The Nordic country has technology-oriented people. Digital literacy runs deep. Trust in institutions, a precondition for data-sharing and AI deployment at scale, remains comparatively high.

And yet the Finnish economy has not grown. That is the uncomfortable fact sitting alongside those advantages.

AI offers a path to a growth leap that organic development alone cannot provide. The United States offers a preview: a meaningful share of recent GDP growth is now attributable, directly or indirectly, to AI-driven productivity. Projections for the coming years are more striking still. The same potential exists here. But potential is not destiny.

What is required is a change from companies, from workers, and above all from leaders. The AI revolution is not arriving. It has arrived. The only useful question now is what each organization will do about it.

The best place to start is with oneself. Leaders who have done that internal work, who have actually changed how they operate, not merely approved a strategy slide, are the ones driving genuine transformation in their organizations. At Dell, we have trained for this, measured it, and held ourselves accountable to it. We want to be the best reference for what we preach.

Governance is not the brake. It is the engine

Some leaders worry that governance frameworks will slow AI innovation. The concern is understandable but misplaced.

Ungoverned AI does not move faster. It moves recklessly, accumulating hidden liabilities in data quality, security exposure, regulatory risk, and organisational trust that eventually force a costly reckoning. "Governance is not about slowing down innovation," Roese argues. "It's about building the guardrails that allow us all to accelerate safely and sustainably." 

The organizations that will succeed with AI over the next decade are not necessarily those with the most impressive early pilots. They will be those who built the infrastructure, governance, and cultural readiness to operate AI at scale reliably, securely, and with clear accountability.

AI can help address major global challenges. But that requires trust. And trust requires governance. The opportunity is immediate, and so is the risk of inaction. Delays now will be difficult to reverse later.

Finland has the technological capability and institutional foundations. What remains is leadership, the courage to build trust and take the growth leap within reach. The work does not start with another strategy document, but with each leader choosing to step into the unknown. In a race already underway, delay is not neutral. It is a decision to fall behind.