Digital transformation speakers
Strateger och teknologer som hjälper organisationer att hantera de tekniska, kulturella och kommersiella utmaningarna i digital transformation
Speakers Associates represents 234 speakers on Digital transformation, including Rahaf Harfoush, Purna Virji, Itai Green, Limor Ziv, Tom Goodwin, Daniel Trabucchi & Tommaso Buganza, Jeremy Blain, Dr Sidney Shapiro, Blake Morgan och Marc Saltzman.
Fashion businesses run on a development model that was already strained before AI changed what was possible. A typical garment moves from sketch to production through six to eight weeks of manual pattern work, multiple physical samples, and inventory commitments made months before a customer is asked anything. The operational question is no longer whether to automate. It is whether the leadership team understands which parts of the cycle can now be compressed, what the supply chain looks like when production becomes on-demand, and how to integrate digital and physical product lines without losing brand identity.
Consumer brands keep buying reach and getting compliments. The harder problem is converting attention into shelves, repeat orders and category credibility before the moment passes. Most marketing teams can describe what worked on TikTok last week; few can explain how to build a product business that survives the spike.
Most large organisations talk about innovation as culture and end up funding pilots that never reach the P&L. The gap is not ideas, it is process: how a bank, telco or pharma company moves a creative concept through the same operational rigour it applies to risk, finance and supply. Without a repeatable method, innovation stays personality-led and stops when the sponsor leaves.
A senior leadership stage is only as good as the person running it. A weak host lets time slip, leaves panellists unchallenged, and turns a marquee moment into a forgettable session. The buyer’s real risk is not the speakers on the bill, it is the editorial judgement of whoever holds the room.
Technology-first approaches to AI and digital transformation tend to produce systems that solve technical problems, not organisational or civic ones. When the people affected by those systems have no stake in how they are designed or governed, trust erodes and adoption fails. The gap between deployment speed and governance readiness is where most digital strategies break down.
Most enterprises now have AI on the agenda but no method for getting it into the operating model. Pilots stall, design teams default to features instead of customer problems, and the organisation cannot tell the difference between a real innovation portfolio and a list of experiments. The gap is not ambition. It is discipline.
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.
Boards now operate inside a thicker regulatory perimeter than at any point in the post-2008 cycle, with competition, digital and capital markets rules tightening at EU and national level at once. Most leadership teams read these moves as compliance cost, not as a market signal. The blind spot is structural. Pricing, M&A, data strategy and capital allocation are all being repriced by regulators while executives still treat regulation as a downstream constraint.
Boards have approved AI strategies they cannot fully explain, govern, or defend. Pilots multiply, ethical frameworks lag, and the human side of the operating model erodes faster than anyone planned. The question is no longer whether to deploy AI, but how to do it without losing the judgement, trust, and accountability that hold the enterprise together.
Most large organisations in emerging and developed markets are running digital transformation programmes that have stalled at the pilot stage. Boards want exponential technology translated into operating advantage, not slide decks. The harder question is whether the leadership team, the culture, and the customer model are set up to absorb it.
Most large organisations have run AI pilots. Few have turned them into operating advantage. The harder problem is cultural: senior teams know they need to move faster on AI, but the internal mechanics of how decisions get made, how creative work is commissioned, and how risk is held have not caught up. Without that translation, AI sits adjacent to the business rather than inside it.
Most organisations talk about high performance. Few operate under conditions where every deadline is fixed by regulation, every decision is scrutinised in public, and the gap between winning and losing is measured in hundredths of a second. Senior leaders looking for a credible reference model for executing under that kind of pressure rarely find one inside their own sector.