AI is moving faster than you: How leaders can keep up and win

8 May 2025|Strategy

Many people feel frozen under the shiny bright glare of AI possibilities. Potentially game-changing techniques and tools continue to stack up faster than organisations can absorb or apply them, meaning that technology is no longer the limiting factor – human leadership is.

How to overcome possibility paralysis and lead from the front

A recent report shows that nearly 98% of organisations plan to increase AI investment (aka everyone), and 94% of Fortune 1000 firms are already seeing value from AI initiatives. Yet most companies are stuck in pilot purgatory: over 76% are still only testing or partially deploying AI, with just 24% reaching full production at scale. What’s holding them back? Not algorithms, but alignment. 91% of organisations say cultural resistance is a bigger barrier to AI success than technology. In other words, the challenge has shifted from what AI can do, to what we as leaders enable it to do.

Strategic choices, leadership methods and organisational structure must be an integral part of the conversation. Conway’s Law, long used within software engineering (and beyond) is gaining greater currency in the AI age: “Any organization that designs a system will inevitably produce a design whose structure is a copy of the organization's communication structure." [Read the original 1968 paper here]. We are at a pivot point, and must look to redesign organisational communication structure if we want to avoid simply replicating what we have now in the AI age.

In this article, we’ll explore how to move from frozen-in-the-headlights to forward momentum. At the heart of this are the fundamentals of strategic clarity, organisational agility, and human-centric execution.

Emphasise Strategic Clarity and Comfort with Ambiguity

For smaller organisations and teams just starting their AI journey, strategic focus is your best friend. It’s tempting to chase every shiny AI tool, but leaders must curate a clear strategy: identify where AI can truly advance your business goals, and concentrate efforts there. This clarity acts as a filter against the overwhelming hype. For example, a fintech startup might decide to apply AI only to automating customer onboarding, rather than trying to sprinkle AI everywhere from HR to marketing on day one. That kind of focus ensures AI investments actually support the mission, rather than create distraction.

Clear strategy should not preclude space for creativity and challenge. Fostering a culture that’s comfortable with some ambiguity is helpful. Early-stage organisations often thrive on experimentation – this should extend to AI adoption. Teams need psychological safety to pilot new AI ideas and learn. Only about a third of large companies today report having a truly AI-driven culture (32%, up from 24% five years ago), so smaller firms can create an edge by cultivating openness and agility now. Make it clear that ambiguity is not chaos.

In short, smaller companies should start with a narrow, well-defined AI game plan, and also build a culture that treats ambiguity as a natural part of innovation. If your team isn’t a little uncomfortable, you’re probably not stretching far enough.

Adaptive Organisational Design

As an organisation grows into a mid-size enterprise, cool AI prototypes need to turn into scalable solutions. Leaders should ask: is our structure helping us adapt to new technology – or hindering us? Adaptive organisational design means building a company that’s flexible, cross-functional, and able to pivot quickly as AI evolves.

In practice, this might involve creating dedicated AI teams or new leadership roles to drive integration. It’s telling that 84% of companies have now appointed a Chief Data or Analytics Officer, and one-third have a Chief AI Officer in place (with another ~44% planning to). This wave of new roles shows businesses realizing that traditional org charts may not suffice for the AI age. We’re even seeing companies re-org entirely around digital innovation. A recent example from the UK: BBC News overhauled its structure in 2025 to establish a new “News Growth, Innovation & AI” department. This team’s mandate is to personalise content and drive audience engagement (especially among under-25s on platforms like TikTok) using AI as a core tool. It’s a bold structural change aimed at embedding AI into the BBC’s DNA, rather than keeping it as a siloed IT project.

Flexible structures go hand-in-hand with agile ways of working. Many mid-size firms are adopting models of cross-functional “squad” teams, similar to those popularised by startups and tech companies, to speed up AI implementation. These teams might bring together a data scientist, a product manager, an ops specialist and, crucially, a business domain expert to collaborate on an AI initiative from start to finish.

We also need to think in new ways about how these teams are comprised: human-agent teams are coming into view and will rewrite the org chart. This new type of team can centre on objectives rather than function, powered by agents with adaptable expertise.

Importantly, adaptive design isn’t just about internal teams – it also means being open to external partnerships and ecosystems. Many organisations team up with AI startups, consultancies, or even competitors to co-develop solutions. Larger banks, for instance, have partnered with fintech AI firms to improve fraud detection rather than trying to reinvent the wheel internally. This kind of networked organisational thinking can give mid-size players the leverage of a much larger firm.

The takeaway for leaders of growing organisations is clear: structure for speed and learning. If a process or hierarchy is slowing down your AI progress, redesign it. One Microsoft study envisions that in the future, rigid org charts could give way to fluid “work charts” where teams assemble around goals on-demand. At Futurice, our low-hierarchy and multidisciplinary way of working has positioned us nicely to take on this kind of analysis and redesign: we were building internal tools to map the flow of knowledge around our own org several years back (2020 article). As a knowledge-based business, we’ve seen since then the acceleration of opportunities and rapid growth of possible futures for our organisation and the type of work we do. Your company doesn’t have to go that far overnight, but it should be willing to shake up job roles, teams, and reporting lines to truly embed AI. The era of AI demands an agile organisation as much as agile code.

Long-View Capability Building

Finally, we come to the long game: preparing your organisations’ people and capabilities for sustained success in the AI era. It’s easy to get caught in quarter-to-quarter project thinking but business leaders must invest in long-view capability building. This includes upskilling talent, upgrading data infrastructure, instituting continuous learning programmes. All to ensure their organisation can absorb new technologies for years to come.

Start with your workforce. The half-life of skills is shrinking, and AI is accelerating that. Every employee will need a degree of AI fluency. Forward-looking organisations are already making massive investments here. In 2023 PwC announced a $1 billion programme to upskill its workforce in AI over 3 years, aiming to make all 65,000+ employees “savvy, responsible users” of generative AI tools. Ikea last year announced a plan to provide AI literacy training to around 30,000 workers and 500 managers. In a recent survey, 47% of business leaders globally said upskilling their existing employees is a top workforce priority for the next 12–18 months, even above hiring new talent.

Building capabilities isn’t just training – it’s also about processes and governance that solidify lessons learned. For instance, after a few AI pilot projects, a company might develop a “AI playbook”: a repeatable process for identifying AI use cases, assembling the right team, delivering and measuring results. This playbook becomes an organisational asset that endures beyond any single project or hype cycle. Similarly, investing in better data infrastructure (data lakes, integration platforms, etc.) is part of capability building – it’s laying down highways so that future AI vehicles can drive faster.

Another facet of long-view thinking is leadership development. The truth is, even top executives are learning as they go with AI. Companies that encourage their leaders (not just the IT folks) to get smarter about AI will have an edge. Whether that’s through formal education (e.g. sending execs to AI executive programs) or simply creating forums for leaders to regularly review emerging tech and question how it could disrupt their business – it matters.

Lastly, long-term capability building must include what we call being “future-capable”, in other words, instilling a mindset that change is the only constant. Today it’s AI, tomorrow it might be quantum computing or some new paradigm. If you’ve built a culture of continuous learning, strong cross-functional collaboration, and agile strategy shifts, your organisation will be in a much better place to absorb whatever comes next. The comfort with ambiguity from earlier works best as ingrained capability.

Stepping Up to the New Era

The leaders who will thrive are those who step up to this new era with clarity, agility, and humanity. In practical terms, here’s what you can do right now

Revisit Your AI Game Plan Does your organisation (no matter its size) have a clear, focused AI strategy? Identify one or two areas where AI can make a meaningful difference and double down there. Communicate that focus relentlessly so everyone from the board to new juniors knows “this is how we’re using AI to win.” And if you don’t have a strategy – convene your team and craft one. Even a 1-page outline is a start.
Infuse Flexibility and Ownership Look at your org chart and team structures. Are they helping AI initiatives flourish, or creating bottlenecks? Break silos by forming cross-functional teams for key projects. Empower a capable leader (or a team of “AI champions”) to drive adoption across departments. Make it someone’s job to ensure AI isn’t everyone’s afterthought.
Champion a Human-Centric Culture Set the tone that AI is there to assist, not replace. Celebrate wins where employees used AI to achieve better outcomes, and equally celebrate when employees wisely intervened or adjusted an AI output to get it right. Encourage feedback, questions, even skepticism – this will surface issues early and help you refine. And invest in training, not just the technical “how to use tool X” but also the critical thinking around it – why and when to use AI (or not).

Above all, lead with curiosity and courage. The organisations that master this moment will be those whose leaders are not afraid to ask big questions, try new things, and occasionally make mistakes – all while keeping their people and purpose at the center of every tech deployment. AI can do amazing things, but it’s human vision and values that will determine whether those things truly benefit us.

The AI revolution isn’t slowing down and now is the moment for leaders to lead. Your move.

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