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The Incumbent’s Playbook for an MCP World
Eight moves that keep an incumbent relevant once its product is callable — and four things to stop doing immediately. The through-line: expose the capability generously, and own the things a schema cannot carry. Read ⇢
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Substrate or Façade — Who Survives Being Called
Being callable amplifies some products and hollows out others, and the sorting is not random. Four tests that tell you which side of the line you are on — before the market tells you. Read ⇢
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What Dissolves When You Become Callable
Exposing your product through MCP costs you six things, and only two of them show up in the integration plan. The interface, the context, the telemetry, the pricing model, the switching cost and the outcome — in roughly that order. Read ⇢
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When Your Product Becomes a Tool — What Opening MCP Really Reveals
Incumbents are shipping MCP servers at speed, and many of them are quietly accelerating their own irrelevance. But the protocol isn’t what does the damage. Opening your product as a set of callable tools doesn’t create the problem — it reveals whether you ever had a moat. Read ⇢
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What Do You Charge for a Data or AI Platform?
Pricing was the decision I revisited most often as a founder and got wrong the longest. Data and AI platforms are genuinely hard to price — the value is variable, the cost is variable and never zero, and the buyer has no budget line. A tour of the models, and… Read ⇢
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When Your Partner Ships Your Product
Partnerships across the stack were essential to us — and one of them shipped something that overlapped our product with no warning. That is not betrayal. It is how platform economics work, and a founder should design for it from the first conversation. Read ⇢
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Services Before Product-Market Fit
Before we found product-market fit, consulting and implementation work paid the bills — and taught us the problem in enough detail to eventually build the right platform. The AI wave has rediscovered this and given it a new name. Read ⇢
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Being Early Is Not an Advantage
I believed arriving early to a technology wave was the whole game. It isn’t. Early means you pay the education tax — funding the market’s learning so that whoever arrives after the dust settles can harvest the clarity you paid for. Read ⇢
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Start With the Problem, Not the Wave
In the Big Data era, organisations bought the infrastructure before writing down the question. Most of the value, when it finally arrived, came from the data they already had. The AI wave is running the same play — and the discipline of naming the problem first is the cheapest advantage… Read ⇢
Digital Leadership, Distilled.
Scaling transformation through Data, AI, and human–agent teaming — 23 years of hard-won lessons, distilled into strategic deep dives. Don’t miss the next shift.
