Future of Technology
Technologists and futurists exploring how emerging innovation will reshape industries, economies and daily life
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 strategic frameworks were built for a more orderly world. Boards are now making capital decisions across climate, geopolitics, technology and the loss of trust in institutions, and these have stopped behaving as separate items on a risk register. The harder problem is no longer choosing the right answer to any one of them, but holding a workable stance when the variables move together and feeding the wrong assumptions into the rest of the strategy carries real cost.
Artificial intelligence is built to imitate the brain, yet most leaders backing it cannot say how the brain actually works. The only proven model of general intelligence is still biological. Understanding how it remembers and finds its way is becoming useful in judging what machines can and cannot do.
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 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.
Boards and executive teams now make decisions about AI, data, and digital infrastructure that touch every part of the business. The technical case is well rehearsed. The harder questions, what these systems do to customer trust, to employee agency, to the meaning of the work, get pushed to ethics committees or deferred indefinitely. Leaders need a way to think clearly about technology that is neither uncritical adoption nor reflexive fear.
Most large organisations now claim an AI strategy and an innovation function. Few can show what either has produced in the last twelve months. Pilots multiply, capability stalls, and the question of how to move from experimentation to operating advantage stays open.
Boards are being asked to make capital and risk decisions on AI while the rules around it are still being written. The pressure is no longer whether to deploy, but how to deploy defensibly when regulators in Brussels, Washington and Beijing are pulling in different directions. Most executive teams do not yet have a clear view of who is setting those rules, on what timetable, and what compliance, data and infrastructure choices will look like on the other side.
Senior leaders are running ever larger events on AI, transformation and the energy transition, with regulators, investors and operators in the same room. The quality of the conversation, on stage and in the recording, decides whether the day reads as strategic clarity or as a logo parade. The chair has to be fluent in the subject and confident enough to interrupt a CEO when the answer is evasive.
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 large organisations have run AI pilots. Few have moved AI into operating reality at scale, with clear lines on governance, accountability and where it is allowed to make decisions. Boards now need a sharper read on what AI can actually do for their business, what it should not do, and how to deploy it without inheriting risks they cannot defend in front of regulators or customers.