Private Equity × AI Adoption
Protect Your Investment.
AI-Enable Your Portfolio.
I help PE firms work out where AI actually creates value across their portfolios, then deliver it. Roadmap where that is what's needed, working software where it isn't. I still write the code.
The Shift
Product and engineering economics have changed.
Most PE portfolios haven't caught up.
AI is changing both sides of the equation. What gets built - discovery and validation cycles that took months can now be compressed into weeks. How fast it ships - work that previously required coordinated teams, sprint planning, and weeks of handoffs is increasingly achievable by a small team moving with the right tools. The cost of building software is falling, and it will not stop.
Private equity firms are beginning to ask the right questions: why are our portfolio companies still running six-month discovery cycles and the same engineering headcount they had three years ago?
AI doesn't eliminate the need for engineers; it changes how many you need, and what the best ones are worth. Done well, this is an opportunity to right-size engineering cost ahead of exit: a leaner, faster team that delivers more. Done poorly, or not at all, and you're explaining to your LPs why your engineering burn looks identical to three years ago while your competitors have already made the shift.
I've built the playbook for this inside PE-backed software businesses, with real teams, real results. Now I bring it to yours.
The Work
Four kinds of engagement.
One underlying claim.
In most organisations capability is rarely the constraint. Structure, process and accumulated friction usually are. Each of these is a different way of proving that, and of getting the value out.
Tell me what is real
Independent validation of AI claims across a portfolio
Every company in a portfolio now reports AI progress, and the people reporting it are the people being measured on it. Almost none of it has been checked from the outside. I assess a whole portfolio independently, place each holding against evidence rather than self-assessment, and hand the investment team a written view that stands up in a board or valuation discussion.
Four weeks for a full mid-market portfolio, with almost no management time and without tipping off the companies.
Make delivery cost less
Resetting what a programme costs and how long it takes
A small group of expert engineers with AI tooling, insulated from governance layers and approval queues, delivers what a conventional programme takes multiples longer to produce. The interesting part is never the speed. It is what the speed reveals about how much of the original estimate was structure rather than work.
A core product rebuild estimated at 18 months, delivered in 90 days, on time and to strong customer reception.
Build it, don't scope it
Working software instead of a delivery queue
Some problems do not need a roadmap, a business case and a place in the queue. They need someone to build the thing. I still write code, and a good deal of what I deliver is software rather than advice: production applications with authentication, access control, reporting and an audit trail, on the company's own infrastructure.
Spreadsheets to a deployed, enterprise-grade application in one week, for the cost of a monthly software subscription.
Make the programme legible
Governance an investor and an engineer both believe
Large programmes go dark long before they go wrong. Status becomes a negotiation, forecasts get re-argued every cycle, and nobody can say what is actually true. I put in the reporting line that rolls delivery up from the work itself, so the board sees the same picture the engineers do, and decisions stop being re-litigated.
One view of a multi-workstream programme, produced from the delivery data rather than assembled by hand each month.
Engagements are described in outline only. Client names, sectors, portfolio composition, budgets and any figure that would identify a business or its financial position are withheld, including where no confidentiality obligation requires it. Detail is available in conversation, within whatever the relevant agreement allows.
Engagement Model
What working together looks like.
Step 01
Assess product and engineering maturity
AI readiness, product-market fit, discovery practices, team capability, and where AI creates the most immediate leverage for the portfolio company.
Step 02
Discovery and roadmapping
Map where AI is realistic, what's premature, and where investment should go. Produce a clear maturity view and roadmap before committing to larger build teams.
Step 03
Demonstrate ROI
Track delivery velocity, time-to-insight, cost per feature, and time-to-market. Build the investor narrative with real numbers from inside the business.
Step 04
Hand off and repeat
Hire the right permanent product or engineering leadership, or take the playbook to the next asset in the portfolio. This is designed to scale.
Background
Simon Taylor
I'm a fractional CTPO with over 20 years in product and engineering leadership across PE-backed businesses, scale-ups, and enterprise. I've held the line between investor expectations and engineering reality, and I know what both sides need to hear. I am also still building. I work in Claude Code every day, and a good deal of what I deliver is software rather than advice.
Most recently inside a large PE-backed enterprise software business, I ran two AI delivery experiments that have since become the reference points I use with every PE client. One proved that a small team with the right tools can compress an 18-month product rebuild into 90 days. The other proved that a single developer, given AI tooling and no bureaucratic overhead, can deliver production software in a week for the cost of a monthly subscription. Since then I have worked at fund level, validating AI claims across a whole portfolio, and inside portfolio companies as fractional CTO on programme governance and delivery.
Both projects pointed to the same pattern: in most organisations, capability is rarely the bottleneck. Structure, process, and accumulated organisational friction usually are. I know how to assess where a company actually sits, build a realistic roadmap, reduce the friction, and construct the investor narrative around what becomes possible when you do.
I work at both levels. Strategically, I run discovery and roadmapping - assessing AI maturity, identifying what's realistic and what's premature, and framing the business case. Practically, I sit with the engineers, write code alongside them, and show rather than tell. The two reinforce each other in ways that purely advisory work rarely achieves.
Previously: product and engineering leadership at LearnPro Group, Filtered, and a range of PE-backed and VC-backed software businesses across the UK and Europe.
Let's Talk
If you're a PE firm looking to accelerate AI adoption across your portfolio.
A short call is the right first step. No deck, no proposal: just an honest conversation about whether there's a fit.