Artificial intelligence is no longer an experiment on the margins of business. It is embedded in products, shaping customer interactions, and altering how companies operate at scale. With this comes heightened risk, from bias and regulatory exposure to the unknown consequences of handing decisions to machines.
“The future belongs to organizations that responsibly harness data and AI, not just to automate, but to differentiate,” says Christopher Bannocks, former Group Chief Data & AI Officer at QBE and now a sought-after advisor. A recognized authority on data and AI transformation, he draws on more than 25 years of global leadership experience, helping organizations in financial services, FMCG, and technology unlock growth while establishing frameworks that safeguard trust.
Real-World Experience in Risk and Growth
At QBE, Bannocks led an AI rollout that did more than just deliver numbers. Within 90 days, quote to bind ratios rose 58%. Just as important, he insisted on governance structures that kept fairness and compliance at the forefront. “Implementing real applications that help organizations drive growth whilst also making sure that reputations and customers are protected has always been a critical objective” he shares, reflecting on this experience from his time as Chief Data & AI Officer. That balance of innovation and caution now defines his work through Fractional Leadership Ltd., where he advises boards, private equity firms, and executives on how to unlock transformation responsibly. “I’ve had the coal face experience of managing this problem with boards and senior leadership teams, which is why I focus so much on connecting strategy to execution and the human implications of change,” he explains.
Principles for Responsible AI
For Bannocks, every AI strategy must start with a guiding principle: do no harm. From protecting personally identifiable information to avoiding systemic bias, the risks of getting it wrong scale faster than many executives realize. “Bias is super important in that context,” he says. “If there was bias in an individual 20 years ago, it was bias in an individual. If that is then programmed into an application, then it scales. Suddenly, you’ve got bias affecting hundreds of thousands, if not millions of people.” Managing this risk means ensuring humans remain central to AI decision-making. “Always have a human in the loop. Think of AI and people in your organization as a partnership, not as resources that need to battle against each other.”
Building Balance: Innovation Meets Control
Many organizations struggle to move AI from experimentation to production. Some apply so many risk controls that projects stall before delivering value. Others leap in too quickly, exposing themselves to reputational or regulatory pitfalls. Bannocks argues for a measured, incremental path. “It is about the organization becoming more comfortable incrementally around their control environment that they wrap around the opportunity space,” he notes. “Test and grow. It’s a more incremental, balanced journey towards value creation, not towards a locked down environment where no one can do anything with it.”
In other words, strategy first, value second, platform third, controls fourth. Without a clear business objective, enthusiastic experimentation rarely leads to impact. “Unless it’s in production, engaging with your customers in some way, or improving your process, it’s of no value. You just spent money you didn’t need to spend.” Third-party risk is another pressing issue, with AI now embedded across countless SaaS platforms. Bannocks warns that organizations often have “no clue what it’s doing with data, no clue what it’s doing to customers.” Managing this hidden layer of risk will be critical in the next wave of adoption.
Looking Ahead: The Agentic Future
The pace of AI advancement is already outstripping traditional planning cycles. Bannocks points out that while computing power has historically doubled every two years, “AI capability is doubling every four months.” This exponential growth is accelerating the rise of agentic systems, where AI agents collaborate autonomously across functions. “We will see a massive acceleration of agentic and real agentic systems,” he predicts. “Imagine a customer service agent speaking to an operations agent, which then feeds information to a sales agent who initiates a fully automated follow-up call. That’s not science fiction. It’s possible today.” The implications are profound: lower costs, higher margins, and fundamentally new ways of structuring human-machine collaboration. The challenge for leadership will be to create management structures that ensure visibility, accountability, and trust as AI agents increasingly interact on behalf of the enterprise.
The Leader’s Imperative
Companies that move too slowly risk irrelevance, yet those who leap too quickly without the right safeguards risk eroding trust. The solution lies in clarity of strategy, disciplined platform building, and controls that enable rather than stifle progress. “The key is stepping into this domain, not jumping into it with both feet,” he says. “It’s about finding the balance where AI drives growth and efficiency, but with humans guiding the outcome.”
To follow Christopher Bannocks’ work and insights on AI transformation and governance, connect with him on LinkedIn.
