From Pilot to Production: Fractional AI’s Playbook for Making AI Real

We’re continuing our new Just Curious format, pairing excerpts from a recent interview with five focused questions designed to surface practical, repeatable lessons for operators and investors

This week, we’re featuring Chris Taylor, CEO and Co-Founder of Fractional AI, a company helping private equity firms and their portfolio companies accelerate AI adoption and turn prototypes into production-grade systems.

For many leaders, the challenge oftentimes isn’t finding AI opportunities but getting them into production across your organization or your portfolio.

Most companies can build a flashy AI demo. Few can turn it into business-changing results. That’s the gap Chris Taylor and his team at Fractional AI live in. Fractional AI helps organizations identify high-impact AI opportunities, build them fast, and measure ROI in months, not years.

What to expect:

• Why the biggest winners in AI may not be AI companies at all

• How to move from 500 “AI ideas” to one project that drives value

• What to look for in AI diligence (and what red flags to avoid)

• How an e-commerce client achieved an 84% cost reduction with AI

• The mindset shift PE leaders need to drive production-grade AI adoption

Want to connect with Chris and Fractional AI? Just reply to this message, and I’ll facilitate

Watch my full interview with Chris Taylor →

84% Cost Savings & 30-Second Turnarounds: The PE Playbook for Production-Grade AI

💬 Expert Q&A: Chris Taylor, CEO & Co-Founder of Fractional AI

(Each week, we ask applied AI experts a rotating set of questions to surface their frameworks, lessons, and insights. Chris’s answers follow, with Stu’s thoughts and context from our full conversation.)

1. Describe who you are, what you do, who you do it for, and what makes your approach unique.

“Fractional AI is an AI-native services provider that works with many of the largest PE firms in the world. We help leadership teams of portfolio companies sprint to put valuable AI workflow automations and product features into production. We also help PE firms with AI diligence — assessing AI risks and opportunities to help firms make better investment decisions. Our top-caliber AI engineering team and track record of successful AI deployments set us apart.”

Stu's Thoughts:

When I asked Chris how Fractional AI came to be, he said: “The biggest winners of this AI moment won’t be AI companies. They’ll be the companies that use AI best.”

That thesis drives Fractional’s work. Chris and his co-founders—who first met on the early LiveRamp team—saw a clear pattern: incumbents already have customer relationships, defensible markets, and access to the same frontier models as startups. What they lack is time and talent to turn AI potential into production.

“We realized that incumbents are in a much better position to win,” Chris told me. “They just need help building fast and measuring what works.” Fractional was designed for exactly that: an AI-native services firm built to get results in the environments that matter most.

2. What problem are you most focused on solving right now, and how are you anticipating solving this problem with AI?

“We’re building a variety of high-impact workflow automations and new agentic product features for our clients.”

Stu's Thoughts:

In our conversation, Chris described what he calls “the production gap,” the chasm between flashy prototypes and systems that actually deliver business value.

“Everyone can build a demo,” he said. “Production changes everything.”

Fractional’s approach starts with scoping high-value workflows that can go live quickly and scaling from there. The team doesn’t just prototype; they architect every project like a software product, building from day one with evals (automated evaluation frameworks) and reliability in mind.

“When we start a project, we’re already thinking about what it’ll take to get it into production in three to six months,” Chris explained. “The work you do on day one should be on the path to production.”

That production-first mindset has become a hallmark of their success: fewer endless pilots, more live systems producing measurable results.

3. Where have you seen AI drive the fastest ROI — in weeks, not years?

““Weeks” is too ambitious, but the majority of our development work is aimed at a production system driving real impact within 3–6 months. For longer-term builds we take a phased approach, with phase one aimed at a subset of useful functionality going into production within six months.”

Stu's Thoughts:

Chris’s obsession with speed-to-production isn’t about cutting corners. They focus on accelerating proof and adoption. His team encourages clients to prioritize one high-impact project that can go live quickly rather than chasing a long list of ideas.

He shared one of the strongest examples from a PE-owned e-commerce company that relied on an offshore BPO for a critical workflow. “They used to have a 24-hour turnaround time,” he told me. “We replaced that with an AI system that did it in 30 seconds. With higher accuracy.”

The result was an 84% cost reduction in year one, with further savings expected as QA declines and LLM costs drop. The system continues to improve automatically because it learns from every QA correction. “They used to rely on offshore teams,” Chris said. “Now the system runs automatically and gets better every week.”

 👉 Watch the case study →

4. What’s the most common mistake companies make when they try to “do AI”?

“Overinvesting in “data readiness” or core infrastructure without a clear understanding of their use cases. Your company can invest in organization-wide data readiness for years, but when you actually go to build high-impact AI automations and product features, step one will likely still be building a new custom ground truth dataset for each use case.”

Stu's Thoughts:

Chris sees a common pattern across companies trying to adopt AI: they start by investing heavily in preparation — data readiness, infrastructure, or tooling — before they’ve clarified what they actually want AI to do.

“You can spend years getting your data perfect,” he told me, “but when you finally build something meaningful, you’ll still need to create a new dataset specific to that use case.”

This leads to what he calls the “tools-first trap.” Instead of starting with technology or data infrastructure, Chris argues, start with the problem. “For personal productivity tools—fine, buy the software,” he said. “But for workflow automation or new AI-driven product features, the use case should come first. The infrastructure follows.”

That same logic extends into Fractional’s private equity diligence work, where Chris and his team help firms identify AI red and green flags in potential targets. They assess both disruption risk and opportunity: what parts of a business are defensible, what could be automated, and what AI means for its value over the next decade.

5. What AI tool, startup, or approach are you most excited about right now — and why?

“We continue to see the best results by using frontier model APIs and using evals to build reliable systems.”

Stu's Thoughts:

For Chris, evals are the unsung hero of production-grade AI. “Eval-driven development is what turns AI from a toy into a tool,” he said.

At Fractional, every project begins with a test suite that measures reliability and performance over time. “If you wait until week 12 of a project to decide how to measure success,” Chris told me, “you’ve wasted 11 weeks.”

It’s the same approach detailed in their collaboration with OpenAI, building reliable systems by design, not by accident.

Turning Insight into Leverage

1. Start with One Use Case. Don’t brainstorm 500 AI ideas—pick one high-impact process and sprint to production.
   

2. Think in Phases. Deliver something valuable in 3–6 months, even if the full roadmap is longer.
   

3. Use Case Before Infrastructure. Real ROI comes from applying AI to business problems, not from building the perfect tech stack.

Connecting with Chris

Here are a few options:

• Respond to this newsletter, and we’ll help facilitate (it’s free)

• Check out Chris’s Just Curious profile and connect with him there (also, free)

• Go find Chris on the internet and connect (also free, but a lot more work)

The common thread in all of Chris’s answers is discipline, not experimentation for its own sake, but a relentless focus on production and measurable value. It’s a useful mindset for any operator navigating this moment.

Enjoy this!

Stu