Future of Technology speakers
Technologists and futurists exploring how emerging innovation will reshape industries, economies and daily life
Speakers Associates represents 253 speakers on Future of Technology, including Kemal Apaydin, Michael Lyon, Mark Stevenson, Edvard Moser, Nikolas Badminton, Marc Saltzman, Alex Goryachev, Timandra Harkness, Chris Heemskerk and Dame Wendy Hall.
Most organisations treat innovation as a technology question and culture as a brand question. The two functions report separately, fund separately, and rarely produce anything a customer can actually use. The leaders who build durable advantage are the ones who can run cultural intuition and product engineering as a single discipline.
Boards are being asked to make consequential bets on generative AI without a stable read on what the technology can actually do, what it cannot, and what its deployment will mean for the workforce. Most executive briefings collapse into either hype or alarm. Leaders need a sober technical interpreter who can separate marketing from mechanism, and tell them which decisions matter now.
Leaders are being asked to make consequential bets on quantum, AI, and biotech without the tools to separate genuine scientific progress from marketing. Boards over-index on confident vendors and under-index on the slower, harder question of what the science can actually do. The cost of getting this wrong is years of misallocated capital and credibility lost when claims fail to land.
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 large companies have run AI pilots. Few have moved them into operating advantage. The tension is no longer whether to invest, but how to convert experimentation into revenue, new business units, and customer interfaces that legacy organisations can actually run.
Connected products generate more value as data than as objects, and most organisations have not worked out who owns that data, who monetises it, or what their business looks like when a competitor figures it out first. Boards know the shift is happening. Few have a defensible position on what to do about it.
Every organisation now has a digital transformation strategy. Very few have the executive fluency to decide which emerging technologies actually deserve investment, which are years away from being usable, and which belong on the regulator’s desk rather than the roadmap. The cost of getting that distinction wrong, in smart-city programmes, public-sector IT and corporate digital strategy, is quietly absorbed as failed projects and stranded spend.
Artificial intelligence is moving from pilot to protocol inside hospitals, space agencies, and infrastructure programmes, and most leadership teams are still arguing about what is real and what is theatre. The cost of getting this wrong is not slower innovation. It is patient harm, missed regulation, and capital deployed against the wrong assumptions. Boards want a translator who has actually built and deployed clinical AI, not a commentator describing it from the outside.
Audiences are fragmenting, advertising revenue keeps falling, and the platforms that once delivered scale are now extracting it. Publishers and content businesses have to decide what readers will actually pay for, then rebuild the product, the newsroom, and the commercial engine around that decision. Most do not know where to start, and the cost of getting it wrong is the title itself.
Sustainable competitive advantage has stopped behaving like it used to. Incumbents with strong positions, talent, and capital still lose share to entrants who reframe the question rather than win on the answer. The work is no longer protecting a moat; it is detecting where the moat has already moved.
Boards have signed off on AI ambitions that the operating business has no idea how to execute. Pilots multiply, vendor decks pile up, and the gap between strategy slides and what customers actually experience keeps widening. The job leaders need help with is choosing where AI changes the commercial model, and where it is noise.