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. Discovery, roadmapping, and hands-on execution. Not slides. Working results.
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.
Case Studies
Done inside a PE-backed software company.
Not a methodology: two experiments that shipped.
Core Product Rebuild
vs. an 18-month waterfall estimate
A core B2B product, serving hundreds of long-standing customers, was overdue for a full rebuild. The conventional programme would have taken 18 months and multiple teams. Instead, the CPTO ran a deliberate experiment: a small group of expert engineers, a separate £500k budget, AI tooling replacing traditional delivery overhead, and a fixed 90-day deadline.
It delivered. On time. To strong customer reception. But the more important finding wasn't the speed. It was the proof that the real constraint in large enterprises is rarely capability. It is structure. Strip away the governance layers, the approval bottlenecks, the committee-driven prioritisation. A small team with AI tools can move at a pace that rewrites the business case for how software gets built.
This became the blueprint. A replicable model for AI-native delivery, applicable across the portfolio and beyond.
Internal Tool Sprint
vs. a £85–125k traditional estimate
A programme team was managing cross-portfolio activity across a patchwork of spreadsheets: no consolidated view, no audit trail, no way to hold anyone accountable. The IT delivery queue was long. The team decided not to join it.
Instead: one developer, AI tooling, five working days. The result was a full production web application: SSO authentication, role-based access, live dashboards, automated notifications, and a complete audit trail. 13,600 lines of application code. Deployed to the company's Kubernetes infrastructure. Enterprise-grade from day one.
Total additional cost: £360, one month's AI tooling subscription. The developer was already on payroll. No contractors, no project manager, no QA cycle, no DevOps setup fee.
Both projects ran inside the same PE-backed enterprise software company. Both used Claude Code as the primary development tool. Neither required additional headcount. Both are live in production.
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.
Most recently at a Blackstone portfolio company, 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 five days for the cost of a monthly subscription.
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.