For digital-led organisations moving towards AI-first operating models, reaching digital maturity is no longer measured by adoption of new technology alone. What is of equal importance to customers today is balancing opportunity with risk, the discipline, governance and compliance that sit behind these new technologies.
AI has moved rapidly from experimentation to becoming deeply embedded in enterprise infrastructure. More than ever, digital-first businesses are using it to improve CX, aid decision making and accelerate service delivery for their clients. All of this is being done at a pace few could have imagined even a couple of years ago, as data is fed into LLMs in enormous quantities. This makes AI one of the greatest opportunities for businesses, but also one of our greatest risks.
The race to effectively implement and embed AI will not be won by who moves fastest, but by those who can prove they are moving quickly and safely. Particularly for any organisation serving enterprise and government clients, this means demonstrating clear ownership, tested controls, consistent standards and a management system that can keep pace as AI continues to evolve.
As Probe Group has evolved to an AI-first, digital-led business, we have been on a journey to define what maturity looks like across our brands and services, including delivering digital transformation, product and customer experience, alongside the internal enterprise systems that support them.
We’ve spent the past two years building a single AI management system across three different businesses to ensure we can stay ahead of the curve and strengthen our ability to implement AI responsibly.
This is what we’ve learned.
The challenge: Fragmented governance at scale
Most organisations begin their AI journey in a fragmented way: a pilot project, a customer-facing solution in one part of the business or an internal productivity tool in another.
The risk with this approach particularly at scale, is that fragmented governance quickly becomes a risk. Different teams may interpret policies differently. Solutions may move at different speeds. Most importantly, in the fast-moving AI environment we all now exist in, the external landscape is shifting before annual policy cycles have time to catch up.
Digital maturity is more than just an AI use policy
Probe Group faced this fragmentation across Convai, Innovior, Probe CX and our enterprise systems. There was no possibility of simply issuing a policy and asking everyone across all businesses to follow it. Instead, we undertook a long period of consultation and engagement to arrive at an overarching AI governance framework that aligned the work already underway in each business under one common set of rules.
All of this means that digital maturity now requires so much more than simply implementing a policy and expecting teams to follow it. The pace of change in AI means that policies and procedures that once might have been reviewed annually need to evolve far more frequently as technology, regulation and customer expectations change. We were updating our own AI policy as frequently as monthly, because the environment kept shifting underneath us, and the AI management system we have built enables those changes to be made.
For businesses operating in enterprise, government or highly regulated environments, deeper governance frameworks like this are particularly important. Customers increasingly want assurance that providers are not just recklessly experimenting with AI, but have the structures, controls and accountability needed to deploy it safely.
A mature AI management system helps create that assurance by embedding minimum standards, testing whether processes are being followed and creating mechanisms for continuous improvement.
This is where maturity becomes a competitive advantage. Proactively staying ahead of high-risk areas builds agility to pivot, demonstrates thought leadership and gives customers clear insight into how our organisation thinks. Organisations with clear governance structures and frameworks in place are not forced to make up processes as they go. They can adapt existing controls, bring the right stakeholders into the conversation and make changes across business lines in a coordinated way. In a market where AI expectations are changing quickly, that ability to pivot safely is as important as the technology itself. Service providers that do not have these frameworks in place are now 18-24 months behind.
Five steps to digital maturity
Our experience showed that digital maturity is not achieved through a single policy, technology investment or compliance exercise. It’s an enterprise discipline built through practical steps. Our approach combined visibility, accountability, a single framework, tested controls and the ability to adapt.
Step 1: Understand where AI already exists in your organisation
You can’t manage what you can’t see. Gain a clear understanding of your own environment first to ensure you build the right governance structures. Start by establishing where AI is already being used across your businesses, who is responsible for implementing it, what data it has access to and what decisions it influences. This provides an understanding of your AI footprint and highlights the areas where risk, customer impact or operational complexity need closer attention.
Step 2: Treat AI risk separately to information security risk
The risks introduced by AI use are not the same as existing information security and data security risks, and should not simply be grouped in with these. Information and data security remain as important as ever, but an organisation still assessing AI against its existing infosec and privacy templates is missing the additional risks AI introduces.
Step 3: Build one AI governance framework
Separate approaches for different teams or businesses will result in fragmentation. In our case, rather than creating separate approaches across Probe CX, Convai, Innovior and our enterprise systems, we built a common AI management framework that could be applied consistently while accommodating different operating models. This gave each business enough flexibility to keep innovating, along with one shared set of expectations, controls and minimum standards, along with governance to bring all businesses together. Policies must be backed up by practical frameworks, integrating governance and security across our entire service offering.
Step 4: Test governance, do not just document it
Your review mechanisms, controls and assurance processes should be implemented to test whether governance is being followed in practice rather than simply documented. Independent testing also helps to strengthen governance frameworks and provide greater confidence that they will stand up under real operating conditions. This is where external assessment through certification to ISO-42001 comes into play.
Step 5: Build the ability to adapt
AI, regulation and customer expectations are changing too quickly for static governance models. Prioritise building systems and decision making structures that can evolve as the environment changes, rather than needing to start from scratch each time. A shared understanding of AI risk and management is critical to rapid change.
What leaders should do now
No organisation can predict exactly how AI regulation, customer expectations or technology will change, even in the next couple of months. And government regulation will likely remain behind the real emerging risks, risks that your customers will still expect you to manage, regardless of the regulatory environment.
The best thing leaders can do is build the maturity to respond to the new risks AI will continue to bring, for themselves and for their customers.
