Artificial Intelligence & Generative AI speakers
Artificial intelligence is changing how organisations operate, compete and make decisions. Speakers Associates can help you find an AI keynote speaker or artificial intelligence speaker, as well as expert-led training and workshops that help your people understand what the technology means and use it effectively.
Speakers Associates represents 356 speakers on Artificial Intelligence & Generative AI, including Kemal Apaydin, Olivier Sibony, Rahaf Harfoush, Purna Virji, Itai Green, Limor Ziv, Tom Goodwin, Daniel Trabucchi & Tommaso Buganza, Jennifer Willey and Katja Schipperheijn.
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For a conference, leadership retreat or all-hands, an AI keynote speaker can give your audience a clear view of what is changing, which developments matter and where artificial intelligence can create practical business value.
A keynote is only one option. If your priority is adoption, implementation or better use of the technology across your organisation, a workshop or training programme may be more useful. AI training for employees, AI training for executives and tailored programmes can be brought to focus on your own teams, workflows and business priorities.
The aim is not to make every delegate a technical specialist. It is to help people make informed decisions about where artificial intelligence and generative AI can improve productivity, strengthen existing work and create new opportunities.
Most boards overreact to economic news that will not matter in six months, and underreact to the news that will. A Fed decision or a fresh tariff round lands inside the business as margin compression and forecasts that stop working. Leaders need someone who can tell them which indicators will actually move the next two quarters of performance.
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.
The integration of brain data, AI, and consumer-grade neurotechnology is moving faster than most senior leaders realise. The organisations engaging with this territory now will set the terms others have to accept later. Most boards do not yet have a real position on it.
Most leadership teams have run their generative AI pilots and now face a harder question: where does the technology actually sit inside the operating model, and which categories of work change shape entirely. The answer is rarely visible from the inside, where vendors pitch tools and consultants pitch frameworks. It comes from people who have built original commercial product with these systems and watched the next layer of human-machine technology arrive in a hospital bed.
Digital transformation has become the flag every board agenda flies. The hard question is which parts of the business model actually change, who is accountable for the outcome, and how governments and regulators will reshape the ground beneath a strategy as it is being executed. Leaders who treat technology, policy and strategy as separate conversations keep losing the argument in all three.
Most leadership teams know the operating environment has shifted. Far fewer have changed how they decide, allocate, or hold their nerve when the assumptions underneath the strategy are moving. The gap between knowing disruption matters and leading through it is where senior teams quietly lose ground.
Most enterprises now have an AI strategy on paper and very little of it in production. The board wants returns, the engineering organisation is still rewriting pilots, and personalisation, agents and generative AI are stuck behind unresolved questions on data, privacy and operating model. The gap between AI ambition and AI in revenue is now the defining technology problem of the cycle.
Most boards have approved a digital strategy and an AI roadmap. Few can say with confidence what their company would look like if either succeeded. The gap between announced ambition and operating substance is widening, and the leaders most exposed are the ones who treated digital and AI as IT projects rather than as questions about how the business itself runs.
Sales and revenue teams are being asked to apply AI without a clear theory of what it is for. Pilots accumulate, dashboards multiply, and the pipeline still depends on the same human effort it always did. The harder question is what a commercial organisation actually looks like when autonomous agents do the work that headcount used to do.