For many CIOs, AI has moved beyond curiosity. Pilots are running, tools are in use, and boards are asking the same question: when does this turn into something real?
The challenge is not experimentation. It is production.
Moving AI from pilot to production is less about algorithms and far more about delivery discipline, governance, and leadership intent. At board level, the concern is no longer whether AI works, but whether it delivers predictable value without introducing unmanaged risk.
What follows is a practical five‑point framework CIOs can use to move AI into production in a way that is commercially grounded, operationally sound, and aligned to business strategy.
1. Anchor AI to a business outcome
Production AI starts with clarity. Every initiative must be anchored to a defined outcome that matters to the business – growing revenue, driving margin, or improving resilience.
At board level, the mantra should be simple: no ROI, no AI.
AI enthusiasm is understandable, but technology leaders have a responsibility to stay grounded in commercial reality, regardless of how compelling the technology feels. If an AI use case cannot clearly move one of these outcomes, it should remain a pilot or be stopped entirely.
Production environments amplify impact, cost, and risk. That amplification must be intentional.
This is the shift from “what can AI do?” to “what will AI change?”
2. Treat AI as part of the operating model
AI does not sit alongside the business. It sits inside it.
Once AI moves into production, it becomes part of day‑to‑day operations, decision‑making, and performance management. That means ownership must be explicit – not shared vaguely across innovation teams, data functions, and technology.
CIOs need to ensure AI has a clear home in the operating model, with defined accountability for performance, risk, and continuous improvement. If ownership is unclear, production will stall.
3. Apply delivery discipline, not innovation theatre
Pilots tolerate ambiguity. Production does not.
Moving into production requires the same delivery discipline expected of any core system – release management, service levels, security controls, monitoring, and incident response.
This is where many organisations hesitate. AI feels new, and leaders worry about constraining innovation. In reality, the opposite is true. Discipline is what allows AI to scale safely and credibly.
At board level, confidence comes from predictability, not novelty.
4. Design governance that flexes with maturity
Governance is often cited as the reason AI cannot move faster. More often, it is because governance has not evolved.
Early‑stage AI needs guardrails. Production AI needs oversight that is proportionate, risk‑based, and embedded into existing governance structures rather than bolted on.
CIOs should design governance that tightens as impact increases – with clearer controls, stronger assurance, and regular review – without slowing delivery to a standstill.
Good governance enables momentum. Poor governance kills it.
5. Invest in people at the same pace as automation
Production AI changes how work gets done – and where work gets done.
As with cloud before it, AI will not reduce the need for technology capability. If anything, it increases it. AI shifts demand towards stronger engineering, data, architecture, security, and product capability.
The real structural change happens across the rest of the business.
When AI is woven into core processes, something has to change. Roles evolve, activities disappear, decisions move closer to data, and work gets redesigned. That is where value is created – and where leadership discomfort often shows up.
CIOs need to ensure AI production plans explicitly address this reality: fewer manual handoffs, less low‑value effort, and clearer accountability for outcomes.
Two outcomes pay the bills – revenue growth and margin improvement. Resilience matters, but it must support one of those two.
AI delivers value through people – and, in some cases, instead of them.
AI will replace certain activities and roles. Being open and honest about that reality is part of responsible leadership. Avoiding the conversation does not protect people – it simply delays the inevitable and reduces trust.
What matters is intent and transparency: where AI replaces work, leaders must be clear about why, how value is created, and what support or transition looks like for those affected.
From experimentation to execution
Boards are right to push for results. The experimentation phase has delivered learning. The next phase must deliver value.
Production AI is not about moving faster at all costs. It is about moving deliberately – grounded in commercial outcomes, disciplined delivery, and leadership accountability.
For CIOs, this means holding the line when hype outpaces reality, and being explicit about where AI will – and will not – move the dial.
No ROI. No AI.
Move AI out of the lab and into the business – in a way the board can trust.
