For most of the last two decades, the arc of technology leadership has been clear: build reliable systems, scale them, keep them secure and resilient, and ensure the product vision is tethered to engineering reality. But the rise of AI has bent that into something much less predictable. Organisations are discovering that the shift from software engineering to intelligence design demands new instincts, new governance, and a far more nuanced understanding of their own data than many had previously bothered to acquire. For CPTOs, this is not an incremental evolution. It is a change in professional identity.
What has become apparent in recent years is that the barrier to entry for using AI has collapsed, while the barrier to using it responsibly has risen sharply. Vendors promise pre-cooked solutions, executives ask why their competitors sound more confident about AI than they feel, and suddenly every product team is expected to “just add intelligence” to the roadmap. But intelligence is not an ingredient you can sprinkle on top of a stack; it is an expression of your organisation’s underlying data health, operational maturity and product discipline. And if any of those foundations are weak, AI will not fix them. It will amplify them.
A peer of mine, Nick, expressed it perfectly: “If your data is chaos, your AI will be premium chaos.” It is a line that should be written above the door of every boardroom currently debating their AI strategy. Poor data quality has always been a problem, but in a traditional software environment its impact could be contained: reports might be wrong, features might be slightly clunky, decisions might be slowed by manual reconciliation. In an AI-driven product, however, messy data becomes a source of distortion. It produces systems that look intelligent but behave erratically, recommendations that feel personalised but are subtly misleading, and automations that create more work than they remove.
And if AI is premium chaos, then the product built on top of it becomes something worse: a beautifully-designed, fully-scaled, enterprise-grade chaos delivery mechanism. The surface might be elegant; the underlying experience might even appear to work for a while. But the moment the product is deployed at scale, the gaps will begin to show. The CPTO becomes the person responsible not only for the integrity of the technology but for the integrity of the intelligence. That is a fundamentally different level of accountability.
This shift changes almost every part of the CPTO’s role.
First, it elevates data governance from a background operational concern to a strategic imperative. When your product experiences depend on probabilistic outputs and learned behaviour rather than deterministic code paths, the quality, lineage and meaning of your data become board-level issues. The CPTO must become the steward of organisational truth: ensuring that data definitions are consistent, that sources are trustworthy, and that the organisation understands the consequences of ambiguity.
Second, it forces a new kind of product sensibility. In the classical product world we designed features; in the AI product world we shape behaviours. That requires CPTOs to work more closely with behavioural scientists, ethicists, domain experts and policy teams, and to push back against the temptation to replace thoughtful design with algorithmic shortcuts. Intelligence without intentionality is just automation with delusions of grandeur.
Third, it transforms engineering leadership. AI systems demand teams that can experiment safely, manage model lifecycles, interrogate outputs and explain behaviour. This isn’t simply a matter of hiring a few machine-learning engineers; it is about creating a culture where intellectual humility is valued, where models are treated as fallible colleagues rather than oracles, and where data quality is as celebrated as new feature releases. The CPTO must champion that mindset.
And finally, it changes the relationship between technology and the board. Where once the conversation centred on systems reliability and delivery velocity, it must now include model risk, data provenance, ethical impacts and unintended consequences. The CPTO becomes not only the translator of technical complexity but the guardian of organisational alignment around what responsible intelligence looks like.
The reality is that AI will not make every product better, nor every company smarter. What it might do is expose the gaps in your data, in your processes and in your understanding of your users. The organisations that thrive in the AI era will be the ones that treat data as a product, intelligence as a discipline and technology leadership as a strategic act of stewardship.
For CPTOs, this is the moment to step forward, no longer the people who ship features, now those who ensure that intelligence is built on something better than premium chaos.