Future of Technology
Technologists and futurists exploring how emerging innovation will reshape industries, economies and daily life
Most strategy functions are not built for exponential change. They forecast from the past and plan in quarters. When AI, energy transition, and geopolitical realignment compress decades of disruption into months, the system stops working.
Most leadership teams know they are behind on consumer technology, but cannot tell which trends will reshape their category and which will fade in eighteen months. The cost of guessing wrong is real: misjudged AI rollouts, security gaps, retail experiences that miss the customer, product roadmaps built on yesterday’s behaviour. Senior teams need a working filter, not another vendor pitch.
Most early-stage ventures fail at the same handful of decisions: how to enter a regulated market, how to price a frontier product, where to incorporate, when to raise, what to give up. Founders rarely get those calls in front of someone who has both built ventures in highly regulated sectors and sat on the institutional side when an entire industry had to be wound down. Accelerators help with structure. They do not always have a mentor in the room who has done both.
Most large organisations have run AI pilots. Far fewer have moved them into operating reality. The gap is not the technology, it is the absence of an internal innovation discipline that translates promising experiments into measurable change inside a workforce that is, in many cases, quietly resisting it.
Most AI investment is sitting between the slide deck and the operating model. Leaders have approved the strategy, but the people meant to use the tools are confused, sceptical, or quietly opting out. Closing that gap is a communications and adoption problem before it is a technology one, and very few organisations are treating it that way.
Most boards are now expected to take a public position on AI and immersive technology before the rules that will govern them exist. They are making capital decisions on cities, infrastructure and customer environments under standards that are still being drafted. Knowing who is writing those standards, and how to align to them early, has become a leadership question, not a technical one.
Boards have approved AI pilots, signed responsible-AI principles, and named ethics committees, and still cannot answer whether their deployed systems would survive a regulator’s audit or a serious public failure. The gap is not awareness. It is the operating distance between governance language and the decisions engineers, product leads and procurement teams actually make every week.
Most boards still treat AI as a software question their CIO will solve. The story is bigger than that. The contest is over compute, fabs, energy supply, and the sovereign infrastructure that will decide which companies and which countries hold the next decade of pricing power. Leaders who frame AI as a productivity tool are already a strategy cycle behind.
Most leadership teams know the pace of change has shifted, but their planning cycles, capital decisions, and org charts still assume a slower world. The cost of that mismatch is invisible until a competitor moves first, a category re-prices, or a technology curve bends. Boards need an outside voice that can name what is actually accelerating in their industry, separate signal from noise, and put a sharper time horizon on decisions already on the table.
Most AI deployments produce pilots, not capability. Tools land in the organisation faster than people can absorb them, and leaders default to vendor narratives because they lack a vocabulary for the human variables that decide whether productivity actually moves. The bottleneck is rarely the model. It is the gap between what AI can do and how the workforce learns to think with it.
Senior teams rehearse for crises they expect and freeze when the actual one arrives. The gap between a documented decision protocol and a leader who can run one in real time is where most organisations are exposed. Mission control culture closes that gap, and very few people in business have lived inside it.
Boards are asked to commit capital to AI before the returns are visible, and to do so while regulators, sovereign governments and a small group of US infrastructure companies redraw the rules around them. Most leadership teams do not have an internal source who covers all three at once. The gap shows up as exposure: investments made on vendor narratives, strategy decks built on last quarter’s headlines, and a quiet sense that the people in the room do not actually know who controls what.