AI is advancing at an accelerating pace, with new capabilities emerging rapidly and adoption spreading across industries. This speed inevitably brings both breakthroughs and mistakes, alongside an urgent question for organisations: how can they keep pace?
Productivity gains from AI in software engineering are already tangible, and organisations that hesitate to operationalise it risk falling behind. The real imperative is clarity: you cannot manage what you cannot measure, nor sustain speed on foundations you do not fully understand. As the volume of AI-generated code rises sharply, the human capacity to review and govern it is not keeping pace. When generation outpaces oversight, the consequences are predictable—technical debt accumulates faster, security exposure widens, and core business systems become progressively harder to change, precisely at the moment adaptability matters most.
The rise of shadow and unauthorised AI means that many organisations are losing control simply through a lack of oversight and transparency. Leaders often cannot confidently answer simple questions about where the technology is being used and where potential vulnerabilities lie. This lack of visibility prevents organisations from achieving true AI maturity. While implementing new technology is easy, failing to maintain visibility during periods of rapid evolutionary change makes it impossible to maintain security or foster a culture of responsible innovation.
When leaders worry about losing control, their knee-jerk response is often to ban AI use altogether. Paradoxically, strict bans or overly restrictive policies usually achieve the exact opposite outcome: employees simply drive their AI use further underground, amplifying the very security and compliance risks the policies were meant to prevent.
This showcases that AI maturity does not come from no AI use but rather implementing the right AI, and advocating for its use, preventing shadow AI, where risks are much higher. AI maturity will not come from a single project, pilot, or purchase. It will come from a steady, deliberate shift in how organisations govern their software and AI as one portfolio.
What is AI Maturity?
AI Maturity in a practical sense means being able to assert control over the AI in use, tracking its impact with total transparency. The knock-on effects are that AI would then be able to clearly link to business outcomes, monitoring delivery and enabling deliberate choices on where to strategically invest efforts. Not only this, but it enables organisations to ensure that the AI systems they are using are compliant with regulations, addressing the 57% of leaders who currently cite regulatory compliance as a primary AI risk.
Ultimately, for leaders driving AI initiatives, maturity means being able to move fast without losing sight of risk, cost, and the state of software foundations. For those accountable for overall business performance, it means being able to see where AI affects revenue, cost, and risk, and to intervene when necessary.
Practising and Prioritising AI Maturity
AI maturity starts by ensuring total visibility, not by driving innovation underground through restrictions. Achieving this requires a structured, enterprise-wide strategy driven by the entire board, not just the CTO.
An organisation’s board has a tremendous opportunity to make the organisation benefit from AI – and is accountable for doing so responsibly. There have been many failed AI initiatives that could have been prevented by a sounded out foundation.
First, they must secure enterprise-wide portfolio visibility to eliminate AI blind spots across both internal operations and external third-party tools. Second, the board needs a business-critical classification system that maps AI initiatives directly to tangible business outcomes and performance metrics. Third, they must implement an integrated risk governance structure aligned with international standards like ISO/IEC 42001 to proactively manage compliance and system quality. Finally, they must enforce a role-based accountability framework that clearly defines governance, risk, and development responsibilities across leadership, compliance, security, and engineering teams.
Governance, risk, and value
AI maturity should be judged not only by how widely AI is adopted but also whether it can be governed safely at scale. This requires security, compliance, and engineering to operate from the same playbook, especially as AI-assisted tools and agentic systems become part of day-to-day development and delivery. Restricting AI use or banning it entirely will not reduce the need for AI maturity, as since everybody is inevitably using AI, banning it will only take it underground and expose it to more risks that are amplified by poor structures and ungoverned guidance.
The core issue is that AI is no longer just a productivity layer; it is now part of the software estate. Used well, it can accelerate coding, testing, and decision-making. Used poorly, it can introduce insecure patterns, compliance gaps, architectural drift, and new forms of technical debt that are harder to spot and costlier to unwind.
A mature approach links AI value to the condition of the underlying technology environment. The primary result is that the board successfully transitions the company from a state of reactive “AI chaos” to proactive “AI control.” By establishing strict portfolio visibility and clear role-based accountability, hidden or unauthorized AI usage is systematically flushed out and brought into the light.
The ultimate benefit is that the organisation can safely accelerate its AI adoption without exposing itself to catastrophic blind spots. Instead of banning AI, which only drives shadow behavior further underground, a structured governance framework gives employees a secure, sanctioned path to innovate.
The practical takeaway is that AI maturity is demonstrated when organisations can show that AI improves speed and capability without undermining resilience, trust, or control. In that sense, governance is not a constraint on innovation; it is what makes innovation sustainable.
