Insights Strategy
Strategy
The leadership-level decisions that determine whether AI changes how an organisation works, or just adds a tool to how it already works.
Most organisations already use AI somewhere. Independent research keeps landing on the same shape: adoption is nearly universal, and financial return from it is rare. The gap is not a technology gap. Close to seventy percent of the value AI produces comes from redesigning how a company works, its workflows, roles and decision rights, not from the model underneath it. That redesign is a leadership decision, not a procurement decision, and it cannot be delegated to whichever team happens to be piloting a tool.
In the modern AI era, strategy work is what separates an organisation running dozens of disconnected pilots from one compounding a real advantage. It sets the outcome a deployment is meant to produce before a vendor is chosen, decides which functions get redesigned rather than merely augmented, and puts a board or executive committee on the hook for the result. Every workshop and framework in this section is built for that layer of the decision, not the tool layer beneath it.
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What a deep-tech founder should protect first when AI can copy the interface in a weekend
AI coding tools have made software features easy to reproduce. That moves the defensibility question away from the interface and toward the layers underneath it, and the order in which a founder protects them matters.
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In a regulated market, AI marketing needs a review gate before it needs a content calendar
AI makes content cheap to produce. In fintech and other regulated, multi-jurisdiction markets, the real cost sits in the claims, and a short gate in front of publication is what keeps the brand defensible.
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AI transformation in a traditional industry is run from the operating seat
Most AI advice assumes a technology company. A manufacturer has shift patterns, older equipment, tacit knowledge and thin margins, and the person best placed to change it is the one accountable for output.
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Forecasts break when the rules change. Foresight is built for that.
Tariff shocks and fast-moving AI regulation teach planners the same lesson: a single forecast assumes the rules hold still. Strategic foresight plans for the rules moving, which makes it the better tool for AI strategy.
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A joint CFO and CAIO framework for AI benefit realisation
AI benefits go unproven because the work falls between two executives: the Chief AI Officer runs the programme and the CFO owns the numbers, but neither owns the chain from baseline to realised benefit. Written with Dr. Christina Chua, this sets out four controls both should sign.
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The fear of being late is making leaders buy the wrong things
Fear of falling behind on AI usually arrives with a deadline attached: a competitor's announcement, a vendor's quarter-end price, a board meeting. Deadlines reward whatever can be reported fastest, and a purchase is the fastest thing there is. Being late costs less than buying wrong.
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What DBS's S$1 billion in AI value measures, and how a board should read it
DBS's figure is often repeated as AI savings in US dollars. It is neither. It blends three kinds of value, it is self-reported and unaudited, and its definition has moved. The method behind it is sound, and it shows what a board should demand before quoting its own number.
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How DBS runs 33 platforms with two leaders each
DBS's platform model is usually told as a technology story. It is really a decision about ownership and money: a business leader and a technology leader on every platform, one shared scorecard, and transformation written into their pay before anyone was asked to behave differently.
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The lean way to build AI: small bets, real evidence, fewer failed pilots
Most AI pilots fail on how they were scoped, well before the model gets a chance to be wrong. Toyota's production system and the Lean Startup method already solved this problem for factories and software, and five of their practices carry straight across.