Beyond AI: where enterprise knowledge becomes the advantage

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 shifting 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 impact your margins. The risk now is that AI is redesigning – in real-time – the entire foundations of your industry.”

But it’s this very imperative for change that also brings jeopardy, says Magimay, because “while bold leaders are taking action, speed without strategy risks taking you in the wrong direction.” Worse still is the risk of irrelevance from not taking any action at all.

In Magimay’s view, the organisations making progress are those who take strategic steps to protect their enterprise knowledge: the proprietary knowledge, data, intellectual property and unique ways of working accumulated over the years. He adds: “Now that AI is becoming commoditised, advantage comes not from the technology itself but from the strategies you use to gain and preserve enterprise knowledge.” 

For Magimay, enterprise knowledge is a core step in the journey to becoming an intelligent enterprise. This, he adds, 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. And this means that AI deployments deliver pilots and proofs of concept, but none of the scalable, lasting value that leaders are seeking.

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, however, invest heavily in analytics while neglecting their data foundations. They launch AI initiatives without clear ownership of data quality, lineage or governance, or treat data as a departmental responsibility rather than a core business asset. 

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 enable systems to talk to each other. They invest in metadata, master data management and data pipelines that make information usable across the whole business. 

A further risk is that organisations, in their rush to deploy tools, inadvertently gift highly valuable proprietary data to AI platforms. That’s not to say that AI capabilities should always be in-house, but that the right assurance mechanisms are needed to retain data sovereignty.

As Magimay warns: “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.”

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. He explains: “Perpetual beta is a fundamental shift in how you design organisations. You’re not just teaching people to use an AI tool; you’re teaching them to fundamentally rethink how they do their job with AI in it. It’s the difference between ‘deliver it and move on’ and continually evolving as the data, environment and business changes.”

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?

“They’ve sat down and mapped their most important decisions into clear categories,” van Gelderen notes. “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?”

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. The organisations treating AI as a way to do the same things slightly quicker are missing the point entirely,” he says. Agentic AI gives us the opportunity to fundamentally rethink how a business operates, how work flows, how decisions are made, how teams are structured and where value is actually created.”

For leaders, this demands leadership and courage. Reinvention can disrupt established revenue streams, unsettle power structures or require new capabilities. And it’s not a one-off exercise. In an intelligent enterprise, strategy becomes a living framework that evolves as data accumulates and insight grows.

Perpetual beta doesn’t mean endless experimentation. Nor does it imply that organisations abandon discipline. Rather, it reflects a structural shift in how enterprises strategise, design and execute plans. 

“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 almost like having 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.”

As long as you fail quickly in a stage-gated way, he adds, it’s acceptable. Magimay 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.”

He likens it to a venture capital portfolio, where learning, testing and pruning are essential. “In the venture capital world, they plan for up to eight out of ten investments to 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.”

It 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 – as with the adoption of any new technology – rely on highly engaged evangelists: the enthusiasts who champion new tools and push boundaries. But these workers are already engaged, so 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. For over a century, work has been organised sequentially and hierarchically. That’s now changed. 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: from basic analysis to refined judgement; from linear execution to ongoing orchestration; and where human talent works alongside AI agents to identify, prioritise and protect enterprise value. This is a new type of learning attitude: not just willing to learn new methods, but willing to let 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 the strategic direction, treats data as enterprise capital and mobilises and energises the AI neutralists.

“Intelligent enterprises protect data while unlocking insight, and think in sprints – acting on long-term ambition while simultaneously tolerating failure and demanding learning. Leaders know to mobilise 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.

As AI adoption accelerates, a leadership gap is opening. Leaders have invested in AI tools and platforms but haven’t aligned ownership, accountability and governance in ways that protect institutional expertise and create a competitive edge.

“This disconnect is how you end up with one foot on the accelerator and one on the brake,” says Derreck van Gelderen, global head of AI strategy at PA Consulting. He explains that the most common mistake leaders make is treating AI deployment as a technology decision: “Deploying AI is a series of leadership decisions: speed versus assurance; build or buy; and weighing up which enterprise knowledge gives you a competitive edge versus what you can safely open up to share with partners and AI platforms.”

Safeguarding and supercharging enterprise knowledge

Proprietary knowledge is critical for operational efficiency, informed decision-making and minimising risks.

At Sellafield, the UK’s first nuclear power station and now a decommissioning and reprocessing site, engineers relied on technical archives of critical documentation, from operating procedures to safety cases, stretching back more than 60 years. Finding or updating just one of these documents could require months of manual research. Sellafield also faced a major knowledge gap, with institutional knowledge lost as experienced staff retired.

To protect enterprise knowledge, Sellafield and the Nuclear Decommissioning Authority worked with PA Consulting to develop DANI2, the industry’s first AI agent, designed to transform how engineers access institutional knowledge in real time.

“For decades, the answer to knowledge loss in industries like nuclear was digitisation, but this meant engineers spent weeks searching through digital folders instead of paper ones,” says van Gelderen. 

“We now have the technology to let the employees of the future have a conversation with those of today, to ask a question and get contextual intelligence at the point of need. For an industry where critical expertise is retiring faster than it can be replaced, it changes the entire equation.”

Reimagined decision-making

Carl Dalby, head of AI & digital at Nuclear Decommissioning Authority Group says the team had the opportunity to rethink how knowledge was managed and decisions were made. “With the use of AI, we’re turning dusty archives into living intelligence,” he says.

From the outset, the programme prioritised governance and expert oversight. Engineers helped identify more than 80 potential AI use cases before narrowing the focus to those delivering the greatest operational value. Validation frameworks were built into the system so subject-matter experts could verify outputs, while guardrails ensured responses remained within defined operational boundaries.

“There’s a temptation to hand everything over to the model and optimise for speed. But when you’re dealing with safety-critical knowledge, the governance architecture matters as much as the AI architecture,” van Gelderen says. “DANI’s design means subject-matter experts stay in the loop, outputs are verifiable and Sellafield retains full ownership of its institutional knowledge. The goal was to ensure judgement is informed by 60 years of expertise, not whatever someone can find in the time they have.”

Ownership and access

With AI models, organisations can inadvertently share proprietary expertise with external platforms without clear ownership and architectural control. 

“Process templates, common standards and widely understood methodologies can sit on a shared platform. But domain expertise built over decades, or tacit operator knowledge, must be protected,” van Gelderen says. DANI2 gets this right. By integrating multiple language models, including open-source options, Sellafield avoids locking critical knowledge into a single vendor’s ecosystem. The infrastructure stays flexible and the intelligence stays theirs.

Turning enterprise insight into innovation

Accumulated expertise remains the most underused growth asset most organisations own. Used well, enterprise knowledge becomes the raw material for everything organisations haven’t built yet. This means the return on AI shouldn’t be measured in hours saved, but in the ideas that wouldn’t have existed without it. 

With consumer expectations evolving rapidly across global markets, consumer goods company Unilever needed new ways to translate insights into faster product innovation.

Working with PA, Unilever developed DelphiAI, a platform that consolidates market research, consumer feedback and formulation expertise into a single intelligence layer. The system enables R&D and marketing teams to interrogate large datasets and generate recommendations for product features, ingredients and market-specific positioning.

“We wanted to use data to look beyond annual product and brand planning cycles to come up with better ideas, more quickly, that unlock the consumer delight that we strive for,” says Kumar Subramanyan, director of digital R&D at Unilever.

For PA, the programme highlights the importance of balancing central governance with driving innovation.

“By putting the right decision forums, leadership ownership and ways of working in place, we ensured the programme could move fast without fragmenting,” says Nyree Basdeo, digital, AI change and transformation expert at PA Consulting.

Everyone on board

Building quicker buy-in and AI adoption meant helping Unilever’s workforce understand the rationale and benefits of DelphiAI – particularly how the platform arrived at its decisions. By grounding adoption in trust and transparency, Unilever ensured DelphiAI strengthened enterprise knowledge – ensuring AI functioned as a strategic growth asset.

“With DelphiAI, we balanced innovation and IP by designing shared data layers for co-creation, while ring-fencing proprietary models behind controlled interfaces,” says Richard Chamier, business strategy and data science expert at PA Consulting.

The shift to next-level strategic asset

As AI capabilities rapidly commoditise, competitive advantage will come from how deliberately leaders protect and mobilise proprietary knowledge. The intelligent enterprises pulling ahead recognise that it’s this knowledge that determines where speed creates value, where caution is essential and how AI strengthens decision-making. Not a by-product of AI, but its foundational differentiation.