The transformation brought about by AI is so profound that the ultimate impact of the changes we are experiencing will only become apparent once the problems are solved.
At this moment, AI is moving from concept to commodity, and organizations across all sectors and industries are scrambling to adapt.
“Boards are feeling pressure to move quickly on AI,” says Alwin Majmey, global AI leader at PA Consulting, a global innovation consultancy. “It is no longer just that AI-driven disruptors will reduce profits. The risk now is that AI is redesigning the entire foundation of the industry.”
Magimay says that because change is urgent, it also comes with risks. “While bold leaders are taking action, speed without strategy risks going in the wrong direction.”
In Mazimay’s view, the organizations that make progress are those that take strategic steps to protect their corporate knowledge – the unique knowledge, data and intellectual property accumulated over many years. He added: “Now that AI is a commodity, it’s no longer an advantage. Enterprise knowledge is the advantage.”
For Magimay, enterprise knowledge is a core step on the path to becoming an intelligent enterprise. This is where “every process, workflow, service, and even workforce can be powered by digital, data, and AI.” These organizations stand out because their strategy, governance, data, and decision-making structures are clear.
“Many organizations bought the tools but skipped the thinking,” Majmei points out.
Data as critical capital
Data is at the heart of an intelligent enterprise. Not as a passive by-product of an activity, but as a core asset that requires ownership and protection. Some organizations begin their AI journey without clear ownership of the quality, lineage, and governance of their data.
Siled datasets limit insights and blunt the impact of AI, so integration is required to transform data into well-structured enterprise knowledge. Intelligent companies break down these silos by designing architectures that connect systems.
A further risk is that in the rush to implement tools, organizations inadvertently provide highly valuable proprietary data to AI platforms.
“Without proper governance and protection, you risk undermining your organization’s differentiation. So when you think about AI, you also need to think about what makes you special and how to protect it,” Majmei warns.
This requires clear responsibility from top management. Who owns corporate data? How are standards enforced? How are access and security balanced? These are board-level questions, not technical afterthoughts.
Eternal beta concept
Traditional digital and AI systems are mostly deterministic. Once built, it works the same way until someone changes the code. Services that utilize AI are different. Performance changes as data and context change. The model will be updated. And workflows will evolve as well.
This is where many organizations stumble. They embrace AI tools but retain industrial-era planning cycles and governance models. They experiment at the edges while protecting traditional assumptions at the core. The result is fragmentation and value leakage.
For Derreck van Gelderen, global head of AI strategy at PA Consulting, this change requires a “perpetual beta” mindset. “Forever Beta is a fundamental shift in how organizations are architected. It’s the difference between ‘deliver and move on’ versus continually evolving as your data, environment, and business change,” he says.
Organizations that achieve “forever beta” are those that are willing to continually reinvent how they operate and ask uncomfortable questions. “If we were to start today, how would we design this organization?”
In an intelligent company, strategy must become a living framework that evolves as data accumulates and insights grow.
“They mapped the most important decisions into clear categories. For example, when speed and pattern recognition are more important than nuance, which decisions should be AI-first? When judgment, ethics, and stakeholder trust are non-negotiable, which decisions should remain human-first? And which ones sit in the middle of the issue where competitive advantage lies?”
According to Van Gelderen, this kind of thinking goes beyond optimizing a process from 20 steps to 10 steps. “We’re not aiming to build faster horses, we want to imagine new ways of moving and new possibilities,” he says.
This requires courage. Reinvention can disrupt established revenue streams, destabilize power structures, or require new capabilities. In an intelligent company, strategy must become a living framework that evolves as data accumulates and insights grow.
“The difference with digital transformation is that instead of just teaching people how to use AI tools, we’re teaching them to rethink how they do their jobs with AI. This is a new muscle that needs to be trained,” he added.
Repetition rather than perfection
This perpetual beta approach is at the heart of a larger cultural shift, Majmei says. “The mindset of today’s leaders is that every investment needs to be successful,” he says. “They invest in a project and expect it to succeed. ‘Failure’ is perceived as negative.”
But Majmei recommends intentionally breaking down your AI investments into small, limited-scale experiments to see what will scale. “Test quickly, learn quickly, and migrate quickly so you can de-risk AI and identify use cases that truly drive value.”
“In the venture capital world, 8 out of 10 investments fail,” Majimei comments. “But the successful two will pay the rest. This early and intensive experimentation will provide clarity and direction for future AI deployments.”
This model also reconfigures accountability. Instead of asking whether the project was delivered on time and on budget, leaders ask whether each sprint produced insights, reduced risk, and created measurable value.
“If we don’t make this leap in thinking, investments in AI will effectively become pilots that collect dust. They’ll bring in revenue, but they won’t bring benefits,” Van Gelderen adds.
mobilize the masses
All of the above depends on dedicated evangelists – passionate evangelists who champion new tools and push boundaries. But how do you mobilize the broader workforce?
The key word here is “neutralist.” They are neither early adopters nor active resisters. They are a pragmatic majority, waiting to see if change is credible, supported and worthwhile. It takes more than inspiration to convince them. It needs structure and that is given to it from above.
A lot of advice in this area currently fails to recognize how profound the changes in the workforce are. Today, AI engines can take on the heavy lifting of routine, repetitive tasks. This means that rules and roles will be redefined.
In practice, this means a shift in required skills, with human talent collaborating with AI agents to identify, prioritize, and protect enterprise values. This requires a new learning attitude. It’s not just about adopting new methods, it’s about letting go of old habits that no longer serve your business.
The actions of senior leaders will set the tone here. When senior executives visibly use AI tools, ask data-driven questions, and participate in sprint reviews, it shows that intelligence is a priority for the company.
intelligent advantage
Intelligent companies are not defined by the number of algorithms deployed. It is defined by how leadership sets the tone, treating data as corporate capital and energizing AI neutralists.
Unlock insights while protecting your data. Think short-term, but act with long-term ambitions. Failure is acceptable, but learning is required. And instead of letting engineers tinker, we mobilize the masses.
In a business environment that is being reshaped by AI, a combination of clarity, repetition, and continuous reinvention is no longer an option. It translates AI ambitions into commercial benefits that continue to grow in value over time.
Visit www.paconsulting.com/global-shifts/future-organisations/next-made-real.
The transformation brought about by AI is so profound that the ultimate impact of the changes we are experiencing will only become apparent once the problems are solved.
At this moment, AI is moving from concept to commodity, and organizations across all sectors and industries are scrambling to adapt.
“Boards are feeling pressure to move quickly on AI,” says Alwin Majmey, global AI leader at PA Consulting, a global innovation consultancy. “It is no longer just that AI-driven disruptors will reduce profits. The risk now is that AI is redesigning the entire foundation of the industry.”
