This changes everything: Business leadership in the age of AI
December 5 - AI is not simply another technology wave. It is reorganising the strategic conditions under which societies plan, invest, compete and govern. These shifts affect every long-term national priority, including security, economic resilience, social stability and the viability of energy and industrial transitions.
Much attention is paid to AI’s immediate applications or environmental footprint, and far less to how these structural shifts will shape future markets, institutional stability and the social resilience on which national prosperity depends.
Several pressure points between current AI deployment and the stability and resilience of societies are becoming harder to ignore.
The first is geopolitical. Strategic advantage is shifting toward states that control compute, data, semiconductor capacity and the mineral and cheap, reliable energy required to build new energy infrastructure and run advanced models. It is contributing to the geopolitical fragmentation that was visible at the recent COP30 and G20 meetings, intensifying U.S.–China rivalry, and leaving Europe – with its high energy costs and limited mineral resources – exposed.
These dynamics are also reshaping energy demand and costs, infrastructure investment and flows of finance. Long-term transitions – industrial, economic or environmental – must be navigated inside these shifts, not treated as parallel or insulated activities.
The second pressure point is the information environment. Social media algorithms have already fragmented public attention and exacerbated polarisation; generative models accelerate this by producing plausible content at scale, crowding out evidence-based analysis.
Research by the Minderoo Centre for Technology & Democracy highlights how AI distorts public reasoning, weakens shared sources of trusted information and erodes the institutions that anchor democratic accountability.
This is also an economic issue: markets cannot allocate capital effectively when the information environment is degraded and unreliable. These questions about where agency lies in increasingly digital systems are explored further in our recent podcast conversation with Thomas Lingard from the Centre for Future Generations, which examines how AI and digital infrastructures are concentrating power and what this means for democratic accountability.
A third pressure point lies in institutional capacity. A small group of firms now control the compute, data, talent and model architectures that set the direction and pace of AI, while policy systems built for scrutiny and deliberation struggle to keep up. This concentration of technical and economic power deepens dependence on proprietary infrastructures and weakens the levers of public authority.
Institutional capability is becoming a core determinant of national competitiveness in an increasingly AI-driven economy. Deficits in public-sector capability and public-purpose infrastructure have knock-on impacts for core national priorities, all of which depend on institutions able to govern market activity, challenge monopolistic behaviour, set standards, coordinate across borders and mobilise investment.
A fourth pressure point runs through the social contract – from labour markets to the direct community impacts of AI infrastructure. Industrial revolutions once unfolded over decades; AI-driven change is happening within a single business cycle. Early evidence shows firms shedding skills and narrowing organisational capability ahead of proven productivity gains.
Long-term job growth may be possible, yet the near-term disruption could be significant, with concerns about unemployment rising. This will deepen political polarisation and strain public finances. That, in turn, constrains the state’s capacity to invest in long-term priorities such as climate transition and adaptation.
Communities are also starting to feel the local impacts of AI infrastructure, from higher energy costs to water availability, and this is already entering electoral debates. Political legitimacy will depend on the ability to offer good jobs, fair distribution of costs and benefits and credible routes to advancement during rapid technological and industrial change.
Given the scale and speed of AI deployment, there are no neat solutions. But there are practical approaches that can help determine whether AI strengthens or weakens the economic, institutional and societal foundations on which future stability depends.
The infrastructure that will govern AI is not yet fixed, and public institutions can still shape it – but only if they develop technical literacy and strategic insight. This requires rapid investment in in-house expertise, supported by programmes such as Apolitical, engagement with think-tanks such as the Centre for Future Generations, and research from organisations such as AI@CAM and Ada Lovelace Institute.
It also needs implementation-focused “coalitions of the willing”, where capable states and institutions pool skills and build shared public-benefit infrastructure. Such work is catalysed by organisations like the Patrick J McGovern Foundation, which fund public-purpose AI collaborations and deployment.
For businesses, a core task is to direct AI toward specific, high-value applications. For example, The University of Cambridge’s Supply Chain AI Lab offers tools to improve energy performance, optimise materials use and inform supply-chain resilience.
Monumo, a deep-tech engineering company, demonstrates the same principle in product design, using AI to cut design cycles from months to days and deliver efficiency gains in motors that, at scale, could remove emissions equivalent to those of France and Germany combined.
Achieving such gains in any sector depends on retaining human judgement, contextual understanding and system knowledge needed to interpret outputs. Equally, choices around data governance, model transparency, workflow design and digital infrastructure will determine whether AI supports long-term business resilience and sustainability – or undermines it.
At a human level, this is demanding work. AI is unfolding at a speed that leaves little room for perfect governance or settled consensus. Leaders will need a firm grip on what their organisation is actually for and who it really serves, even as new technologies pull them toward constant expansion. They will need to protect time for real judgement while still making swift decisions in fast moving markets, and they will have to look beyond their usual circles to understand how AI is reshaping the wider context in which they operate.
It also calls for moral courage, to reject narratives that present AI’s trajectory as fixed, recognise what is not known, and be ready to adapt as reality diverges from prevailing assumptions.
Leadership now rests on ensuring that AI strengthens, rather than erodes, the institutional and societal foundations on which resilient economies and future markets depend.