The value beyond the hype: Re-Imagining IT through Operating Model transformation to maximise AI value

To realise the true value of AI, businesses need to enable growth, not unintentionally restrict it. And that calls for a major shift in the tech model – and mindset.

Experimenting with AI use cases is a great starting point; it’s how organisations build confidence and unlock performance gains. But turning individual wins into something bigger relies on the right technology operating model (TOM) and rethinking everything from processes, data and controls to people’s roles and skills.

Your operating model could be why early use cases don’t scale

Organisations that neglect redesigning their TOM and focus solely on piloting use cases often see common patterns:

• Early successes remain isolated, reducing confidence in further investments.
• Innovations operate on different platforms, held back by inconsistent data or varying controls.
• Value remains trapped in small pockets of the business.

And it leads to visible consequences:

• Local value that doesn’t scale.
• A slower response to contextual shifts.
• Increased vulnerability when governance varies.
• A backlog of AI use cases that can’t be prioritised.
• Interchangeably thinking of agentification and automation needs.

The key to success is to deliberately evolve and design for both your use cases and the TOM. It’s like yin and yang, each strengthening the other.

So why is it happening?

These consequences signal that the business isn’t set up for scaling, not that the use cases weren’t prioritised well.

Federated delivery of AI initiatives works well in theory, but it requires a minimum level of maturity across the organisation to be successful in its adoption and scale. Companies need to know where they sit on the AI maturity curve before they can achieve greater value.

AI maturity evolves in defined phases. And for each one, there’s a clear path to progression.

Defining the path to human-AI partnership

Three distinct paths emerge, each representing a different, but equally valid, model of human-AI collaboration.

These are not maturity levels, but design choices that reflect how human-AI partnerships will work within the business. They are shaped by context, risk appetite and sector.

The choice matters, because each path requires changes to the TOM. The difference is how deep and broad those changes go.

Organisations that choose paths A or B should deepen automation, strengthen governance and adjust their decision structures incrementally. However, those that follow path C are ready to redesign processes, workflows and decision rights, while reskilling people to fundamentally support creative and strategic co-creation with AI.

There is no universal ‘right’ path. Organisations need to decide a way forward that aligns with their objectives, risk appetite and long-term ambition. And the real risk is indecision.

Embarking on your AI TOM journey

Agentic AI changes how work gets done, decisions are made, performance is optimised and, ultimately, how benefits are realised. This elevates the role of IT from an enabler of workflows to an integrator of intelligence that drives business value.

As organisations start their journey, leaders often ask what their TOM should look like. To arrive at an answer, there are seven questions to consider:

Know where to move first, and why

Businesses that successfully build enterprise intelligence share characteristics that allow them to scale use cases without organisational friction. These are:

• Governed and adaptive decision-making.
• Trusted, well-managed data everywhere it’s needed.
• Architecture designed for seamless AI integration.
• Secure, reliable and automated operations.
• Teams with the skills and mind-set to co-work with autonomous systems.

Once these are established, organisations should pinpoint capabilities within IT where rapid AI investment can unlock immediate value and build confidence.

However, it’s important to recognise that they can only scale when capabilities are supported by strong foundations. These act as the structural bricks that ensure AI is deployed strategically, aligned with business priorities and supported by trusted controls and repeatable processes.

Operate with AI, don’t just experiment with it

AI momentum is accelerating, but speed alone doesn’t win the race. What matters is that readiness and capability advance together.

Different sectors will move at a different pace; digital-first organisations surge ahead in becoming AI-first, while regulated industries scale more deliberately. Yet, the direction is universal: AI is shifting TOMs from manual execution to self-improving systems, but only once strong foundations are in place.

Organisations with AI can continue to experiment, but real ROI could remain elusive. To drive maximum value, they must become truly AI-enabled and build the skills and resilience needed to respond and flex to this rapidly evolving landscape. All while placing people at the centre of the transformation.