Leading in the Age of AI

Artificial intelligence is no longer an emerging technology—it’s a disruptive force that is reshaping industries, business models, and competitive landscapes. In January 2025, Innosight partnered with the London Stock Exchange Group and Britain’s Most Admired Companies to convene leading strategists, innovators, and AI executives to discuss how organizations can lead in the age of AI.

The event, moderated by Innosight’s Shari Parvarandeh and Andy Parker, featured insights from senior executives across two panel discussions, with Innosight’s Patrick Viguerie providing closing reflections. The discussions highlighted the immense opportunity AI presents—but also the strategic, cultural, and operational challenges companies must overcome to realize its full potential.

The first panel focused on designing a distinctive AI strategy and program, and featured insights from Laia Collazos, Chief Data & Analytics Officer, Coca-Cola Europacific Partners; Andrew Rochester, Group Head of AI, IAG; and Shweta Vyas, Chief Strategy Officer, RELX.

The second panel was on the role of leaders to build an AI-ready culture and organization and included Claire Calmejane, AI Board Member, Investor & Advisor, CAILEG; Emily Prince, Group Head of Analytics, LSEG; and Paul Donegan, Group Digital Innovation Director, Rentokil Initial.

Throughout the event, a mandate emerged: AI is not just a set of tools—it’s a once in a generation strategic shift that demands a rethinking of business models, culture, and leadership. But it also demands speed, because the longer incumbents wait, the more the freedom to act decreases, as competitors make faster moves. Here are five key takeaways that businesses should focus on to capture AI’s potential.

 

1. Shift from “AI-first” to “Outcomes-first”

AI should not be the goal—it’s the means to achieve strategic objectives. Organizations that rush into AI implementation without a clear business case risk wasting resources on projects that don’t move the needle. Instead, the question should be: What problems are we solving? Where can AI drive real impact? One panelist emphasized this point:

“This was about being clear on the problem and very clear on what success looks like. If you can approach that in a completely dispassionate way and say, ‘I know exactly what success looks like,’ and articulate it well, it gives you a huge level of freedom in how you solve the problem.”

To tap into the transformative power of AI,  organizations need a structured approach that aligns AI investments with long-term strategic priorities. Several leaders described their frameworks for prioritizing use cases, which include factors like customer impact, operational efficiency, and scalability. Another panelist noted:

“We used three criteria—relevance, viability, and value—to identify domains for AI-driven transformation. Rather than looking at AI in isolation, we considered adjacent technologies together, ensuring we built a transformative operating model with a profound impact.”

AI’s real power is in transformation—whether it’s streamlining decision-making, personalizing customer interactions, or optimizing supply chains. However, without a clear purpose, AI projects become disconnected experiments rather than sustainable drivers of value. One panelist described how AI shifted their organization’s approach to operations:

“The business problem is, can we be more effective? Can we be more efficient? Can we use technology to improve the services we offer to our customers? The big breakthrough wasn’t just adding AI—it was putting machines in place, so we didn’t have to constantly check. Instead of always monitoring, we could focus on reacting when something actually needed attention.”

 

2. Governance without Gridlock

AI introduces both opportunities and risks, which means governance must strike a delicate balance: too much oversight slows innovation, while too little can lead to ethical and operational pitfalls. One executive shared their approach:

“We developed a group AI policy, with oversight from the audit committee and strong board interest in ethical and responsible AI development. The goal is to strike the right balance between risk control, speed, and innovation—ensuring AI is both governed and effective.”

This dual approach—bringing together legal, compliance, and AI delivery teams—ensures that governance is pragmatic rather than restrictive. Another leader emphasized the need to integrate governance into existing workflows:

“I set up two groups: the group AI Risk Committee and the AI Working Group. The AI Working Group is really the delivery practitioners, while the AI Risk Committee is focused on risk, ensuring we strike the right balance between control and innovation.”

Companies that succeed in AI governance focus on practical risk mitigation—such as bias detection, auditability, and explainability—while ensuring that AI can be deployed at scale. A participant put it succinctly:

“You have to have evidence that you’ve tested the extent to which this thing hallucinates, and that’s auditable. But if you’ve done that and the business signs off and they understand the implications, then AI becomes something to trust not fear.”

 

3. Data and Strategy Must Work as One

High-quality AI starts with high-quality data. The best models in the world can’t overcome poor data hygiene, which is why leading organizations prioritize data strategy before deploying AI. One panelist described the challenge:

“The quality of what comes out of your technology and your AI models is only as good as the data that is going into it.”

This means ensuring data consistency, accessibility, and governance across business units. Another leader pointed out:

“Our CEO started asking, ‘What is this money we’re spending on data?’ That question was on us because the problem is, data governance is boring—people don’t want to talk about it. And it’s long—there’s no shortcut. But it became clear that it’s an essential part of success, especially as we scaled AI.”

A structured data strategy doesn’t just improve AI performance—it creates long-term efficiencies. One executive noted:

“A lot of my time was spent, and still is spent, on working with leaders to take them through the whys of data strategy, because it’s a forcing function for AI.”

 

4. Leadership Curiosity Needs Direction

Curiosity about AI is essential for leaders—but curiosity without structure leads to distraction. Many organizations are encouraging AI experimentation, but without clear guidelines, these efforts can become unfocused.  One panelist emphasized the importance of clarity:

“Curiosity is the vehicle to help unlock better results and better returns—but it has to be guided by clarity and focus. Otherwise it can spark chaos….I don’t know if anyone had the same experience when you were at school, and it’s like, ‘Oh, here’s a library. Go be curious. Go read it all.’ But who ever read all the books in the library? The thing is, we have an abundance of information at our fingertips, but how much of that do we lean into? And so I think it’s about having that guided curiosity.”

Another panelist described how they approached leadership engagement:

“We have mandatory learning, and that includes the C-suite and the CEO.  Who gets to decide the content of that? That would be me and my team. I’ve spent one-on-one time with some of them for two hours, to explain. And that’s important, right? To make sure that people who are talking to them are able to explain it in a way that resonates.”

A third panelist underscored the importance of experimentation and learning, even from failures:

“You have to start because you have to learn. The best way to learn, including at the leadership level, is by trying. You do a few pilots and then you’ll realize you can’t scale, but at least you’ve tried it. Don’t be paralyzed.”

 

5. AI Demands Culture Change, Not Just Technology Change

AI adoption isn’t just about installing new technology—it requires a shift in how organizations operate. Organizations will need to redesign processes, restructure decision-making, and equip employees to harness AI as a critical tool in their work. One panelist described their company’s experience:

“There is a paradigm shift happening, and it’s a human one. It’s how we move from people, employees, colleagues actually doing things to a role where they are supervising things—supervising the outputs of algorithms or agents, if you like. Over time, that will be more and more embedded. It’s a major shift.”

This shift requires significant investment in training and change management. Another leader noted:

“We’ve been on this transformation journey for decades—moving from just providing data to giving customers search capabilities and now leveraging AI and machine learning. As AI becomes more sophisticated, we’re evolving how our products deliver value.”

Without cultural alignment, AI initiatives will stall. As one participant put it:

“What we don’t know is how fast things will evolve. Nobody really knows who the winners will be. The challenge is balancing speed and control—move too fast, and you waste resources; move too slow, and you lose competitive advantage.”

 

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From Vision to Action: Building the AI-Ready Organization

The conversation at the LSE made one thing clear: AI isn’t just a technological shift—it’s a leadership challenge. The companies that will thrive in this new era are those that:

• Embed AI into their strategic priorities rather than treating it as an experiment.
• Create governance models that support innovation while managing risk.
• Invest in data as the foundation for AI success.
• Equip leaders with the right knowledge and experiences to drive AI transformation.
• Align culture, processes, and incentives to make AI adoption sustainable.