Automatisierung
Experten erkunden, wie Maschinen Arbeit, Industrien und die Grenzen menschlicher Fähigkeiten neu gestalten
Speakers Associates represents 22 speakers on Automatisierung, including Kieran Gilmurray, Byron Reese, Ulrich Walter, Danilo McGarry, Cornel Amariei, Elena García Armada, Aaron Frank, Jason Bradbury, Spencer Kelly und Jochen Wirtz.
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.
The political and economic risk profile of the Americas shifts faster than most organisational strategy cycles can absorb. A market that looks stable in January can be structurally different by Q3 – government reversal, currency shock, or trade agreement collapse can arrive without warning. Automation is now compressing that timeline further: the same workforce that faces geopolitical disruption is simultaneously facing structural displacement from AI, and most organisations are treating these as separate problems when they are the same one.
Organisations are deploying AI in hiring, healthcare, and operations before they understand whose assumptions are encoded in those systems. AI bias is not a data problem – it is a design problem, and it traces directly to the homogeneity of the teams building the tools. The second risk is less visible: research shows that humans routinely defer to automated systems in ways that go well beyond the reliability of those systems, including in high-stakes scenarios. Boards that have approved AI adoption have often not reckoned with either problem.
Boards are being asked to make capital and workforce decisions on AI without a shared map of where the technology is actually heading. Internal teams default to either pilot-by-pilot caution or unchecked enthusiasm, and neither produces a defensible long-range position. What is missing is a credible read of what the next decade looks like, grounded in technology history rather than vendor marketing.
Organisations are deploying AI faster than they are rethinking what their workforces should do. The gap between automation investment and workforce strategy is not a technical problem – it is an institutional one. Every historical wave of technological disruption has produced the same error: treating short-term labour displacement as permanent decline, or resisting disruption until the window for adaptation has closed.
Vacancies and unemployment coexist even in growing economies, and most workforce strategies have no rigorous model for why. The mismatch between available work and employed workers is structural, rooted in search frictions that standard hiring logic does not account for. Automation and AI are accelerating job creation and destruction at the same time, introducing new versions of those frictions faster than institutions – or organisations – can adapt.
Most organisations talk about innovation and ship incremental product. The gap shows up in how invention is governed: which problems get resourced, how patents become products, and how a founder or intrapreneur converts a research prototype into a funded, regulated, commercial business. Boards want operators who have done both sides, scaled invention inside a multinational and built a venture from nothing.
Global supply chains are being rewritten under pressure from tariffs, geopolitical shocks, and cheaper industrial robots. Leadership teams that built a decade of margin on low-cost offshoring now face a harder question: which parts of the production network are still worth holding abroad, and which need to come back. Most boards are making that call on instinct, without the economic evidence to weight the trade-off.
Most large organisations have run AI pilots. Very few have turned them into an operating model that moves revenue, cost or risk at the scale of the business. The gap is not the technology. It is leadership conviction, governance design and the discipline to industrialise what works before the next cycle of tools arrives.
Most boards are now briefed on AI, but few have thought seriously about what happens when AI has a face. Customer service, healthcare, education and hospitality are all heading towards interactions with machines that look back at you, recognise you, and hold a conversation. The strategic question is no longer whether the technology works. It is how organisations design for trust, responsibility and emotional register when the interface is a humanoid.
Most boards now own an AI strategy on paper. Very few can describe the governance, the deployment route, or the human-machine boundary their organisation will actually operate against once the pilots end. The harder question is not whether to invest, but how to make defensible decisions about autonomy, accountability, and workforce design when the technology is moving faster than the policy around it.
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.