by Gavin Gray, Head of Corporate Resource Group at Owner Resource Group
We often joke that we’re “the blue cheese of private equity” here at Owner Resource Group – an acquired taste, not your typical mold. We’re a boutique Austin-based firm of about twenty. Our mandate is straightforward, to provide our partners with the resources they need to grow their business. In support of this mandate, we recently decided to make our first dedicated hire in AI and data science. We see a recurring dynamic across our partner companies: smart operators doing human-intensive work that newer tools could do better, faster, and cheaper. The application of these new tools should help our partner companies to create operating leverage, ie. growth without the addition of headcount to do it.
Why now
We are investing out of our fourth fund and looking forward to our fifth. We remain focused on manufacturing, distribution, and business services companies. The types of businesses where a few percentage points of margin change an owner’s outcome. Over the past couple of years, we’ve watched AI use cases move from “interesting demos” to “high ROI, quickly implemented solutions”.
Similarly, we’ve seen AI use cases develop within ORG. We’d gotten more sophisticated with our own data and analytics, but we were still doing a lot of work the way it has always been done. We wanted to bring in someone who could make us more operationally efficient, and allow us to make better/faster decisions with the ever-improving data and modeling. We recognized the need for a ‘doer’, not a consultant who’d talk strategy in the abstract.
What this hire does for our partner companies
This role is all about value-creation across our partner companies through the accelerated use of AI, automation, and machine learning. In concrete terms, that means:
- More efficient processes that let us grow partner companies on the same employee base. When we can automate order entry, document handling/matching, or reporting, the people already on the team get to do higher-value work instead of more headcount doing more of the same.
- Predictive Analytics. Forecasting employee growth and hiring needs, using models to better understand demand signals from customers, better lead scoring and improved ideal-customer profiles.
- Enhanced Data Infrastructure. Cleaner data, automated reporting, modernizing data/systems as a prerequisite for future AI deployments.
Our new AI specialist will work alongside our Corporate Resource Group (CRG), the part of ORG that focuses on best practices and special projects that can increase portfolio company earnings. The end goal: to find AI- and automation-rich opportunities and workflows inside each partner company and then help execute them.
Why this kind of person, and how we found them
We knew early that the right fit wasn’t a pure data scientist or a pure software engineer. We wanted a “Swiss army knife” of data science who also understands business practices well enough to spot where AI can actually help. Someone who independently digs into the data, finds the projects, and can sell the vision to a portfolio company CEO with conviction.
Figuring out whether to bring in a dedicated recruiter for a search like this and then working through different backgrounds and candidates to learn what actually fit was its own chapter, and it’s worth its own follow-up. The short version: we partnered with a specialist recruiter (shout out to Ryan Kellner with Hudson Gate Partners) who understood the hybrid nature of the role, and we ended up hiring someone who is an exceptional fit.
Ryan had this to say about the search: “The team at ORG brought me in to help find a very specific type of individual, and we had a strong collaboration throughout the process. Unlike some firms who just need someone technically strong at coding, data science, and AI/ML tools, this search was unique in that ORG needed all of those traits, but extra ones too. Since this person was going to be standing up the practice they needed leadership experience and a consulting mentality…but they ALSO needed to have the polish to talk to C-Suite executives at portfolio companies AND be strong at talking about advanced methodologies to non-technical audiences. We finally settled on a great candidate who had been a top AI Engineer/Architect at Booz Allen, a Data Science Instructor at Indiana University, and a Director of AI at a large national insurance company.”
What comes next
We’ve set clear expectations for the first 30, 90, and 365 days: an audit of existing workflows, a prioritized roadmap, a few quick-win automations, baseline metrics to measure ROI, and eventually an ORG Partner Company AI/Automation Playbook we can take to any new company we invest in. A year from now I’d like to come back and chronicle what our AI Engineer has actually accomplished in numbers we can quantify, rather than just the intent.
If you’re an owner thinking about whether AI is finally practical for a business your size, or a fellow operator trying the same thing, I’d love to hear how you’re approaching it. We’re learning out loud, and we think the best is still ahead.