Digitale Transformation
Strategen und Technologen unterstützen Organisationen bei den technischen, kulturellen und kommerziellen Anforderungen der digitalen Transformation
Speakers Associates represents 234 speakers on Digitale Transformation, including Rahaf Harfoush, Purna Virji, Itai Green, Limor Ziv, Tom Goodwin, Daniel Trabucchi & Tommaso Buganza, Jeremy Blain, Dr Sidney Shapiro, Blake Morgan und Marc Saltzman.
Sustainability investments have not delivered the commercial returns most organisations expected. AI adoption has followed the same pattern – pilots multiplied across business units, producing modest efficiencies but no strategic differentiation. The pressure on growth and commercial leaders is to turn both into genuine sources of customer value before the window for competitive advantage closes.
Most brands have audiences they do not own and emotional equity they cannot monetise. The platforms sit in the middle, the data sits with someone else, and the relationship with the customer is rented rather than built. Turning fan affinity into a direct revenue line, at scale, is one of the harder commercial problems any consumer-facing organisation now faces.
Boards have approved AI investment. Most have not yet decided what good looks like. The question is no longer whether to deploy AI, but how to deploy it without inheriting failure modes that legal, regulatory and reputational teams cannot defend later.
Most large organisations have run AI pilots. Few have turned them into operating advantage at scale. The hard problem sits between proof-of-concept and production: legacy estate, unclear governance, talent gaps, and a board that wants commercial outcomes rather than experiments.
Boards know they need to convert AI and automation pilots into operating advantage, but the path between policy ambition, capital allocation and a working factory or service line keeps stalling. Megatrends are easy to name. Translating them into a sequenced bet that survives a budget cycle is not. Leaders need a frame of reference built from inside the policy and standards machinery, not above it.
Boards with exposure to China are trying to read a policy environment that no longer moves on the old signals. Consumption is weak, local government balance sheets are strained, and the line between monetary, fiscal, and industrial policy has blurred. Decisions about capital allocation, supply chain commitments, and market entry now depend on how Beijing chooses to respond, and most Western analysis is reading it from the outside.
Leaders of banks, central banks and other regulated institutions know their organisations are being rewired by AI, platforms and new regulation. What they struggle with is translating that awareness into sequenced decisions about capability, talent and operating model. The gap is not vision. It is a practitioner view of which AI moves build durable advantage and which ones become stranded pilots.
Boards know AI is not optional. What they do not know is which of the dozen initiatives on the deck will compound into advantage, and which will sink six quarters of budget into pilots that never scale. The gap is not ambition, it is a repeatable way to decide where the organisation actually stands and what to do next.
Most boards now treat AI as a strategic line item, but few know how to translate it into operating advantage without tripping the regulators, the workforce, or the customer. The gap between AI ambition and AI deployment is widening, not closing. Leaders need someone who has sat on both sides: the commercial side that has to ship, and the governance side that decides what shipping looks like.
Boards and executive teams are being asked to rebuild their businesses around technology while the companies themselves were built for a different era. The people making these decisions rarely have the dual fluency required: operator judgement about what a transformation actually costs inside a P&L, and board-level clarity about governance, risk and capital allocation. Without that combination, strategy decks multiply and execution stalls.
Large organisations know they need to innovate faster than their own R&D cycles allow. They have budget, scouting teams, and pilot programmes, yet most startup engagements stall before any technology reaches a revenue line. The hard question is not where to find innovation; it is how to build the internal structure that lets a corporate actually absorb it.
Most leadership teams have an AI strategy. Far fewer have changed how the business runs. The gap between stated intent and operating-model impact is where executive teams stall, and where the investment case quietly unravels.