So profound is the transformation being brought about by AI, that the ultimate impact of the changes we’re living through will only become clear once the dust has settled.
For now, with AI having shifted from concept to commodity, organisations across all sectors and industries are scrambling to adapt.
“Boards feel pressured to move fast on AI,” says Alwin Magimay, global AI leader at PA Consulting, a global innovation consultancy. “It’s no longer just the case that AI-driven disruptors will diminish your margins. The risk now is that AI is redesigning the entire foundations of your industry.”
It’s this very imperative for change that also brings danger, says Magimay. “While bold leaders are taking action, speed without strategy risks taking you in the wrong direction.”
In Magimay’s view, the organisations making progress are those who take strategic steps to protect their enterprise knowledge: the proprietary knowledge, data and intellectual property accumulated over the years. He adds: “Now that AI is a commodity, it’s no longer the advantage. Enterprise knowledge is.”
For Magimay, enterprise knowledge is a core step in the journey to becoming an intelligent enterprise. This is “where every process, workflow, service and even employee can be supercharged by digital, data and AI.” These organisations stand out because they have clarity in their strategy, governance, data and decision-making structures.
“Many organisations have bought the tools but skipped the thinking,” Magimay notes.
Data as critical capital
At the heart of an intelligent enterprise lies data. Not as a passive by-product of activity, but as a core asset that needs ownership and protection. Some organisations launch AI initiatives without clear ownership of data quality, lineage or governance.
Turning data into well-structured enterprise knowledge calls for integration, as siloed datasets limit insight and blunt AI’s impact. Intelligent enterprises break down these silos by designing architectures that connect systems.
A further risk is that organisations, in their rush to deploy tools, inadvertently gift highly valuable proprietary data to AI platforms.
“If you don’t have the right governance and protection, you risk giving away your organisational differentiation. So when you think about AI, you also need to think about what makes you special – and how you protect that,” Magimay warns.
This requires clear accountability at the top. Who owns enterprise data? How are standards enforced? How is access balanced with security? These are board-level questions, not technical afterthoughts.
A perpetual beta mindset
Traditional digital and AI systems are largely deterministic. Once built, they behave the same way until someone changes the code. AI-enabled services are different. Performance shifts as data and context change; models are updated; and workflows evolve.
This is where many organisations falter. They adopt AI tools but retain industrial-era planning cycles and governance models. They experiment at the edges, while protecting legacy assumptions at the core. The result is fragmentation and value leakage.
For Derreck van Gelderen, global head of AI strategy at PA Consulting, this shift calls for a ‘perpetual beta’ mindset. “Perpetual beta is a fundamental shift in how you design organisations. It’s the difference between ‘deliver it and move on’ and continually evolving as the data, environment and business changes,” he says.
Organisations who achieve ‘perpetual beta’ are those willing to continuously reinvent how they operate and ask uncomfortable questions: if we were starting today, how would we design this organisation?
In an intelligent enterprise, strategy must become a living framework that evolves as data accumulates and insight grows
“They’ve mapped their most important decisions into clear categories. For example, which decisions should be AI-first, where speed and pattern recognition matter more than nuance? Which must remain human-first, where judgement, ethics, or stakeholder trust are non-negotiable? And which sit in that contested middle ground where competitive advantage lies?”
This type of thinking, says van Gelderen, means more than just optimising processes from 20 steps to 10. “We’re not looking to create faster horses,” he says, “we want to imagine a new way to travel – and new possibilities.”
This demands courage. Reinvention can disrupt established revenue streams, unsettle power structures or require new capabilities. In an intelligent enterprise, strategy must become a living framework that evolves as data accumulates and insight grows.
“The difference compared to digital transformation is that you’re not teaching people how to simply use an AI tool; you’re teaching them to rethink how they do their job with AI in it. It’s a new muscle you need to train,” he adds.
Iteration, not perfection
This perpetual beta approach lies at the heart of a larger cultural shift, says Magimay. “The mindset of leaders today is that every investment needs to be successful,” he says. “They give you investment for a project and expect it to succeed. ‘Failure’ is perceived to be negative.”
Magimay, however, recommends purposely breaking AI investment into small, stage-gated experiments to work out what will scale. “Test quickly, learn quickly, move on quickly. That’s how you derisk AI and spot the use cases that genuinely create value.”
“In the venture capital world, eight out of ten investments fail,” Magimay comments. “But the two that succeed pay for the rest. That early, focused experimentation is what will give your future AI rollout clarity and direction.”
This model also reframes accountability. Instead of asking whether a project was delivered on time and on budget, leaders ask whether each sprint generated insight, reduced risk or created measurable value.
“If you don’t make this leap in thinking, AI investment will be a series of pilots that gather virtual dust. You will see your bottom line go up but without the benefits,” van Gelderen adds.
Mobilise the masses
All of the above will rely on highly engaged evangelists: the enthusiasts who champion new tools and push boundaries. But how do you mobilise the wider workforce?
‘Neutralists’ are key here. They’re neither early adopters nor active resisters. They’re the pragmatic majority, waiting to see whether change is credible, supported and worthwhile. Winning them over requires more than inspiration. It requires structure, and that comes from the top.
Much advice in this area fails to recognise how deep workforce change is at this point in time. Now, AI engines can take on routine, repetitive heavy lifting. This means that rules, and roles, get redefined.
In practice, this means a shift in required skills: where human talent works alongside AI agents to identify, prioritise and protect enterprise value. This requires a new learning attitude: not just adopting new methods, but letting go of old habits that no longer serve the business.
Senior leadership behaviour will set the tone here. When senior executives visibly engage with AI tools, ask data-driven questions and participate in sprint reviews, they signal that intelligence is an enterprise priority.
The intelligent advantage
An intelligent enterprise is not defined by the number of algorithms deployed. It’s defined by how leadership sets direction, treats data as enterprise capital and energises the AI neutralists.
It protects data while unlocking insight. It thinks in sprints but acts with long-term ambition. It tolerates failure but demands learning. And it mobilises the masses, rather than leaving the techies to tinker.
In a business landscape being reshaped by AI, that combination of clarity, iteration and continual reinvention is no longer optional. It’s what converts AI ambition into commercial returns – and keeps that value growing over time.
Please visit www.paconsulting.com/global-shifts/future-organisations/next-made-real
So profound is the transformation being brought about by AI, that the ultimate impact of the changes we’re living through will only become clear once the dust has settled.
For now, with AI having shifted from concept to commodity, organisations across all sectors and industries are scrambling to adapt.
“Boards feel pressured to move fast on AI,” says Alwin Magimay, global AI leader at PA Consulting, a global innovation consultancy. “It’s no longer just the case that AI-driven disruptors will diminish your margins. The risk now is that AI is redesigning the entire foundations of your industry.”
