Future of Technology
Technologists and futurists exploring how emerging innovation will reshape industries, economies and daily life
Boards are being asked to deploy AI faster than they can govern it. The question is no longer whether to adopt the technology but how to make decisions about it that hold up under scrutiny from regulators, employees, and the public. Most organisations have no working model for that, only policies that lag the systems they are meant to oversee.
Boards are being asked to commit capital across a world where the rules of trade, alliance and supply have stopped holding. China exposure, sanctions regimes, climate-driven migration and the reordering of supply chains now sit inside investment cases that were once treated as macro background. Leaders need a way to read the new map before they price the next decision.
Boards now own cyber risk in a way they did not a decade ago, and most are not equipped for it. Threat actors are using AI to industrialise social engineering, deepfakes and intrusion at a pace that outruns existing controls. Executives need someone fluent in both the intelligence-grade threat picture and the commercial reality of running a business through it.
Boards now own AI decisions that used to sit two layers below them. The EU AI Act, the OECD framework, and UNESCO’s ethics recommendation increasingly govern the same call, and they do not always agree. The hardest cases now involve AI acting in the physical world and in public services. That is where the rules are least settled, and where a wrong answer is hardest to defend.
Net zero commitments have outrun the engineering and capital plans behind them. In aviation, motorsport, shipping and heavy transport, the existing fleet runs on liquid hydrocarbons and will for decades. Boards now need a credible answer for how to decarbonise that fleet without waiting for full electrification, and the engineering decisions made in the next three years will define which industries lead the transition and which import the technology from elsewhere.
Most planning tools were designed for a world that no longer exists. Strategy cycles built for predictable horizons break down when disruption compounds across technology, geopolitics, and social change at once, producing false confidence rather than genuine foresight. Organisations that cannot distinguish structural change from noise will always be reacting to a future someone else shaped.
Most deep technology never leaves the laboratory. The gap between a working prototype and a regulated, commercially viable product is where ambitious R&D programmes quietly fail, and where boards lose patience with science-led ventures. The harder question for leadership is what discipline lets a research breakthrough survive the journey to market without losing its scientific integrity.
The hard question for senior leaders is no longer what generative AI does. It is what comes after: spatial computing, digital twins, autonomous machines, physical AI. Each arrives with a vendor narrative and a decision attached: where to invest, and which shifts actually reshape the business.
Leaders keep treating digital as a channel when it is now the substrate of their industry. The pattern is consistent: software, data and networks erode the unit economics of physical products, intermediaries and distribution before the incumbent sees the shift. By the time the financial impact lands, the strategic options have already narrowed.
Most senior teams now agree AI matters. Far fewer can say what it changes about their specific business this quarter. The gap between abstract enthusiasm and operational decision sits at board level, and it widens every month a leadership team relies on vendor decks for its mental model of the technology.
Most organisations want the upside of AI but cannot share the data that would make their models useful. Regulators, customers, and competitors all push in opposite directions, and the standard answer is to slow down. The harder question is how to use sensitive data across institutional boundaries without giving it up, and that question is now sitting on the desk of every senior leader running an AI programme.
Generative AI is being deployed faster than the governance, voting, and ownership systems around it can adapt. Boards now have to decide which AI systems get a seat at the decision table, who is accountable when those systems shape public opinion, and what legitimacy looks like when a model can speak with more authority than an executive. The hard question is no longer whether to use AI. It is how to keep human institutions credible while doing so.