Matt Cockbill, Partner in the CIO & Technology Officers Practice at Odgers, explains how AI executives can implement AI in legacy organisations
Artificial intelligence (AI) is transforming how businesses operate, compete, and grow. But while digital-native companies can adopt AI with relative ease, legacy organisations face a more complex challenge. For leaders within these traditional, often siloed environments, driving AI adoption requires more than just implementing technology, it demands cultural transformation, executive alignment, and a clear strategic vision.
This is how senior executives can effectively lead AI initiatives in legacy businesses. From building coalitions of change to balancing automation with augmentation, the goal is to provide practical guidance for unlocking AI’s full potential within established enterprises.
Why AI Integration Is Harder in Legacy Organisations
Legacy organisations are often built on long-standing systems, deeply embedded hierarchies, and risk-averse cultures. These factors create friction when trying to introduce something as disruptive as AI. Data is often fragmented or locked away in siloes, while ownership of AI strategy may be unclear or isolated within IT.
The result is slow or inconsistent adoption, and in some cases, stalled efforts altogether. AI thrives in environments that support experimentation, rapid iteration, and cross-functional collaboration—conditions that many legacy businesses are not naturally set up to provide. For executives leading these transformations, acknowledging and actively addressing these structural and cultural barriers is a critical first step.
Building Coalitions of Change
Successful AI leadership within legacy organisations hinges on the ability to mobilise support across departments. This begins with identifying internal champions who understand both the operational pain points and the potential of AI to solve them. These champions often emerge from areas like finance, operations, and supply chain—functions that see tangible value in automation and predictive insights.
Executives must actively foster cross-functional collaboration, breaking down siloes to ensure that AI is not confined to the IT department. Establishing internal task forces or steering groups that include both technical and business stakeholders helps create shared ownership of AI outcomes. Bringing in external advisors or partners can also help accelerate change by introducing new perspectives and best practices.
Laying the Foundation: From Automation to Intelligence
AI maturity within a business is best approached as a progression. Many legacy organisations begin with robotic process automation (RPA) to streamline repetitive tasks. This lays the groundwork for more advanced capabilities such as machine learning, which enables predictive modelling, optimisation, and real-time decision-making.
From there, organisations can move into generative AI applications that enhance productivity in marketing, software development, and knowledge management. At the more advanced end of the spectrum lies agentic AI, where autonomous systems can make and act on decisions across workflows.
Executive leaders need to differentiate between generative tools that support human input and agent-led AI systems that act independently. The latter introduces greater risk and complexity and must be underpinned by clear governance and accountability. Throughout this journey, data quality, governance, and availability remain foundational.
Automate or Augment? Making Strategic Choices
A critical leadership decision in any AI strategy is determining what should be automated and what should be augmented. Not every task is suitable for full automation. Many benefit more from AI tools that support, rather than replace, human workers.
Routine tasks like invoice processing or compliance checks can often be automated with minimal disruption. In contrast, customer service, talent acquisition, and product design may benefit more from augmentation, where AI enhances but does not replace human judgment.
Executives must take a deliberate, use-case-driven approach—one that weighs cost savings against customer impact, employee experience, and long-term value. This is not simply about reducing headcount, but about reshaping work to better combine human and machine strengths.
Culture, People, and the Human Factor
Technology is only one part of the AI transformation equation. Cultural readiness and emotional intelligence (EQ) are just as vital. As explored in our previous article, AI leaders must navigate complexity not just with technical skill, but with empathy, communication, and clarity of purpose.
In legacy environments, this means addressing the deeply ingrained fear of change, particularly fear of obsolescence. AI leadership requires more than introducing tools; it means engaging with people. CIOs and technology leaders play a critical role here: aligning AI strategies with organisational values, shaping the narrative around transformation, and ensuring the workforce is empowered, not sidelined.
Practical investments in digital literacy, reskilling, and change management are essential. So too is building a culture that embraces curiosity, experimentation, and intelligent risk-taking. Executives should champion the “art of the possible”—helping teams imagine how AI can transform their work, not just replace it.
The AI Leadership Challenge
Leading AI in legacy organisations is not just a technical challenge, it’s a leadership one. It demands a clear strategic vision, cultural sensitivity, and the ability to bring people and processes into alignment with new business models. This is where the CIO must step up as more than a technology lead: they must become an enterprise leader, driving the integration of AI into the very fabric of how the organisation operates and grows.
By building strong cross-functional coalitions, maintaining a deliberate approach to automation and augmentation, and staying relentlessly focused on outcomes, CIOs can lead legacy businesses into an AI-powered future. With the right leadership—anchored in EQ, collaboration, and data-driven insight—even the most traditional enterprise can become agile, adaptive, and ready to compete in a rapidly evolving world.
