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    e272026-07-28

    Southeast Asia doesn't have an AI adoption problem, it has a scaling problem

    Originally published in e27 on 2026-07-28.

    Read Original Article on e27

    Core Argument

    Every quarter, reviewing the operating reviews of businesses using this scaling framework, one pattern has become impossible to ignore. The AI tooling line has grown fast, new copilots and agents stacked on top of CRMs and ERPs that haven't changed in years. But asking who owns the workflow that tool just automated, or what approval step disappeared because of it, usually gets silence. The tool got bought. The business did not get rebuilt. The adoption numbers are genuinely strong. Recent regional research puts nearly half of Southeast Asian companies past the pilot stage, ahead of the global average, with Singapore's SME adoption rate tripling in a year. None of that is in dispute. What is in dispute is whether adoption is the same thing as scale. It rarely is. Adoption is a purchase decision: a tool gets rolled out, usage goes up, that gets reported as progress. Scale is a redesign decision: the approval chain shortens because the tool now makes the judgment call a person used to make, the org chart changes because a role that existed to catch errors is no longer needed at that step. Most enterprises have done the first and skipped the second, and are calling it transformation. A logistics business automated document processing for vendor onboarding, cutting a five-day manual cycle to a few hours. The compliance review sitting downstream stayed untouched, still routed through the old sign-off chain. The bottleneck just moved. Talent shortages and integration debt are cited as the top blockers, usually framed as needing more data scientists. The actual shortage is different: people who can look at a workflow, decide what should be removed rather than augmented, and rebuild the operating structure around a faster core. That is a scaling skill, not a technical one, and it is far scarcer than the talent reports suggest. The sectors furthest ahead prove the point. Financial services in Singapore and Indonesia restructured underwriting teams around what the model decides versus what a human reviews, rather than just running fraud models.

    What I'd Revise Now

    This column's central distinction, adoption versus redesign, has since been quantified precisely, and the number is sharper than anything I had in July. McKinsey's 2026 State of AI puts enterprise adoption at 88 percent globally, but only 39 percent report measurable EBIT impact, and just 6 percent qualify as high performers attributing more than 5 percent of EBIT to AI. The variable separating that top tier from everyone else is exactly what this column argued from observation: 55 percent of high performers report fundamental workflow redesign, against 20 percent of everyone else. Redesign, not tooling, is the strongest correlate of financial impact, measured, not inferred. That is the logistics-business anecdote in this column, generalised across a global survey. The tool gets bought at a rate approaching universal. The workflow around it gets rebuilt in roughly one case in five. This column's claim was directional. The data now says how directional, and it's a wider gap than "most enterprises skip the second step" implied.

    Key Takeaways

    • Nearly half of Southeast Asian companies are past the AI pilot stage, but adoption and scale are not the same thing
    • Scale is a redesign decision, not a purchase decision: the approval chain and org chart have to change, not just the tool stack
    • Automating one step without rebuilding the downstream process just relocates the bottleneck
    • The real scarce skill is deciding what to remove, not hiring more AI specialists
    • Regulatory fragmentation across markets rewards businesses that design each market separately with AI as one input, not a single global rollout
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