Five ways to “rewire” your leadership for AI success in 2026

In 2026, it’s time to transform this experimentation stage into something more meaningful.

2026 will be the year of building solid foundations upon which AI can scale strategically, in a way that is trustworthy, transparent, and governed. So how do we build this foundation?

It all starts with leadership. AI transformation will only thrive if CIOs, AI leaders, and data executives champion it to achieve real business impact. The process of empowering teams, accelerating AI decisions, and building trust across the organisation starts at the top.

To help avoid AI chaos in 2026, here are my top five tips for enterprise executives to consider as they develop their AI strategies for the year. From reshaping leadership approaches, to cementing trust and explainability in every AI decision – with these elements front of mind, you’ll be setting up your teams and organisation up for AI success in 2026.

1.     Prioritise ‘organisational rewiring’ over ‘digital transformation’. It’s time to retire the phrase “digital transformation.” CEOs and business leaders need to pivot from viewing AI as a digital project, in 2026, it should be seen as an organisation redesign initiative. This “organisational rewiring” will help businesses to prepare for AI-run operations.

2.     Hybrid leadership will allow AI to flourish. With AI sitting at the centre of business operations in 2026, leaders will need to address a long-ignored question: who is actually owning AI transformation? Is it the CEO or the COO? In fact, the answer won’t be the CEO or COO alone, but instead combined leadership through a new hybrid executive role. This role must blend operational rigour with workforce and culture mandates to drive adoption, trust, and upskilling throughout the whole company. AI will impact every corner of the business, so it’s crucial that its responsibility doesn’t sit on one person’s shoulders.

3.     Integrate your platforms to avoid AI chaos. As it stands, AI is scattered in most enterprises. Tools are disconnected and scrawl across platforms, leading to an explosion of shadow AI.. If AI is to deliver ROI, there can be no separation between 'data platforms' and 'AI platforms'. Instead, AI and data should be run as one integrated platform with your people, models, LLMs, and agents all working seamlessly as a connected system. This will not only help leaders to control AI chaos but also reduce costs and deliver real AI impact across the enterprise.

4.     Trust will make or break AI adoption. As AI continues its upward trajectory, the biggest hurdle for leaders driving AI adoption will be trust. We need to close this trust gap for AI to thrive within businesses. But how? Trust is created when teams experience an “AI-native” moment, a breakthrough use case that galvanises not just one person but an entire team or department to become AI-first. Real trust forms when the results are so strong that people can’t imagine returning to their old workflows, or a working life without AI-enhanced operations.

5.   Treat explainability as an AI deal-breaker. We are also witnessing a major gap in AI explainability. Our recent research found that 95% of data leaders say they couldn’t fully trace an AI decision end-to-end if regulators were to ask. However, as AI increasingly becomes a lynchpin in enterprises' operations, it has never been more crucial for leaders to understand how AI decisions are made. In 2026, do your due diligence to ensure your AI deployments meet regulatory and internal audit requirements. Every action must be transparent and explainable, so leaders know exactly how AI arrived at its decision. If the decisions behind AI actions aren’t clear, don’t deploy them until they are.

From experiments to impact: Make AI work at scale

If you’re going to take one thing away from this article, it should be that enterprise AI is shifting from scattered experimentation to real business impact. And for this transformation to succeed, your organisation’s C-suite and leadership must buy into it.

The ultimate end goal for enterprises should be to move from AI projects to AI success at a large scale. This year will be the year that companies build a governed, integrated ecosystem that showcases AI’s work into one that's scalable, strategic, and has a measurable impact.