Private Equity × AI Adoption
Protect Your Investment.
AI-Enable Your Portfolio.
I help PE firms accelerate AI adoption across their software portfolios, with a proven playbook built inside PE-backed businesses. Not slides. Working results.
The Shift
Engineering economics have changed.
Most PE portfolios haven't caught up.
AI agents now write, review, and deploy production code. The output one well-equipped engineer can deliver has shifted materially: work that previously required coordinated teams, sprint planning, and weeks of handoffs is increasingly achievable by a single person 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 question: why do our portfolio companies still carry the same engineering headcount they did 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 the engineering function
Understand the current setup, team capability, tooling, and where AI can create the most immediate leverage for the portfolio company.
Step 02
Install the AI playbook
Embed AI-assisted development workflows into how the team actually works. Hands-on sessions that create genuine believers, not passive trainees watching a demo.
Step 03
Demonstrate ROI
Track delivery velocity, 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 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 was involved in 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 reduce that friction, and how to build the investor narrative around what becomes possible when you do.
I work at both levels. Strategically, I help frame the transformation and build the investor narrative. 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.