Services · AI adoption
AI adoption for PE portfolio companies in Asia Pacific: past the pilot
For funds with an AI thesis across an Asia-Pacific portfolio, and portfolio-company leadership in the region who have run a pilot, seen it work, and watched nothing change.
The decision you are actually making
Whether AI is a tool you bolt onto an unchanged process, or a reason to redesign the process.
The first is why adoption stalls: the same work, marginally faster, with a subscription attached. The second is harder, slower to start, and the only version that changes a number on the P&L before the hold period ends.
What I do
- An AI maturity diagnostic spanning process-level automation through full agentic redesign, by process rather than by company
- A prioritised roadmap sequenced by what compounds, against the clock you are actually running
- Hands-on build support: I build these systems, not only advise on them
What you get
- An honest read of where adoption actually stands
- A sequenced roadmap with the compounding logic explained
- The build, where you want it built rather than described
- A view on what to buy, what to build, and what to leave alone
The record
Every system below is one I designed or operate. None of them is a private equity portfolio company. They are the proof I can show you: a corporate investing standard, a growth unit's discovery pipeline, and the estate this practice runs on. The portfolio-company version is the same work under a hold-period clock.
AI-powered investment screening, adopted as the client standard, 2025
A leading Asian firm where corporate investing, M&A and the core business each assessed targets their own way: three groups, three vocabularies, three spreadsheets, no shared answer. I designed the investment thesis and sourcing framework first, because a screening engine without a thesis is a faster way to be busy. Then I built the evaluation workflow on top. A large company universe screened to a qualified shortlist, every target scored the same way, each carrying a recommended route: partner, invest, or acquire. Read the case.
Innovation discovery on AI rails, 2025
The non-core growth unit of a global consumer group asked whether its slowest, most expensive stages could run materially faster without losing rigour. I moved the entire discovery front end onto AI rails: clustering thousands of market signals into themes, generating candidates inside each theme rather than waiting for a workshop, then filtering and ranking. The validation was not a survey. People paid with their own money in a live trial. The same sweep threw off a pipeline of real companies for the client's investing and M&A teams. Read the case.
The operating estate I run this practice on, 2025 to present
Several AI sessions working in parallel, a coordinator handing tasks to builder agents, and nothing shipping until an independent check has tried to break it. I did not buy this. I built it, I operate it daily, and it is the clearest proof I can offer of the capability I sell. Read the case.
A B2B sales engine at SME scale, 2025 to present
Enterprise sales discipline: pipeline stages, clocks on every deal, a system that remembers every conversation, rebuilt so a lean team or a single operator can actually run it. Same standard, a fraction of the headcount. Read the case.
How the first conversation works
Send the materials behind the decision. I will arrive at the 30-minute conversation with a written first view: three things I can already see, and the one question a first phase would have to answer. If I am not the right person for it, I will say so on the call. The person on the call is the person doing the work.
After the call, if it is a fit: a diagnostic sprint. Two to three weeks, one question. You leave with a one-page verdict and a go or no-go on anything further. How an engagement actually runs.