PE Firm BayPine Says New Factory Floor Is Digital, and Everyone’s an Engineer

When the assembly line reshaped 20th-century manufacturing, it did more than improve productivity. It redefined what productivity meant. Tasks once limited by human dexterity became scalable through process engineering.

Today, artificial intelligence (AI) is triggering a parallel revolution, not in the world of physical goods, but in the vast expanse of services that power the global economy. If the last industrial revolution mechanized muscle, this one is industrializing the mind.

That makes certain back-office domains especially compelling.

“The [immediate use case] for AI application is across areas with large amounts of repetitive tasks that are largely hand-done or highly exposed to software engineering,” Tim Kiely, head of data, analytics and AI at private equity firm BayPine, told PYMNTS.

“Things like revenue cycle management in healthcare … highly manual, highly repetitive, super value-add … that is an area where we’re already seeing tremendous disruption,” Kiely said.

A second vector of opportunity, he said, is customer acquisition. “Anywhere customer acquisition is a really important facet of the business model … customer service is changing in an incredibly dramatic fashion right now in front of us. [our portfolio companies are] using voice AI agents to acquire more customers at a much lower cost.”

BayPine itself is putting its money where its mouth is, having signed a recent partnership to embed OpenAI technology across all aspects of its internal operations and deploy ChatGPT Enterprise across its portfolio companies.

The firm’s overarching thesis is that AI is not a standalone sector, but a pervasive capability and new “industrial logic” that can reshape every layer of operations and decision-making. Rather than chasing the frontier of generative AI research, the firm is prioritizing repeatable automation and measurable return on investment (ROI). The kind of gains that might reshape a profit and loss statement (P&L) before they revolutionize a discipline.

The organizations pulling ahead in the AI race aren’t necessarily the ones with the most advanced models, but the ones that achieve firm-wide AI fluency. As AI tools become commoditized, differentiation comes from cultural adaptability: empowering every employee to experiment, automate and problem-solve with AI.

“One of our heads of FP&A sat down and created a labor forecasting model … another head of marketing didn’t know how to write SQL or Python, but is now querying their database through natural language and using those insights to drive return on ad spend,” Kiely said. “[Internally,] we’re not just talking about the investment team or the ops team — everybody, our legal team, our IR team. We’re trying to get AI fluency across the board.”

Those examples, he added, underscore AI’s most under-appreciated virtue: confidence. “It enables people to do things that they really weren’t able to do before … My experience is people tend to be quite delighted by this technology.”

That delight is what distinguishes AI from previous waves of enterprise transformation.

“Unlike cloud, or unlike even personal computers … this technology is broadly appealing, and it seems like people have genuine fun when they are working with it,” Kiely said. “Once you enable people and you train them and you give them the tools, then they come back with all of these different ways to use it.”

This notion, that transformation is social before it’s technical, has become a theme of BayPine’s AI playbook.

BayPine’s experiment is unfolding in a private equity industry that, by nature, is skeptical of hype. Yet the firm’s early returns suggest that the AI era may demand a redefinition of what operational excellence means.

Where previous decades prized financial engineering, today’s differentiator may be data engineering. And where PE firms once looked for margin through cost discipline, they may now find it through code.

The compounding effect, Kiely said, is that AI allows BayPine’s companies to reclaim capabilities they once had to outsource.

“Anywhere there’s software engineering across the portfolio, we are seeing teams insourcing a lot of what they used to outsource,” he said. “AI development tools have given them the confidence and the leverage to insource a tremendous amount of that work, which is a huge cost saving and gets them higher-quality outputs.”

For all his optimism, Kiely was candid about the technology’s current limits. “Will [one of the frontier models] release ‘CFO AI’ and then you plug it in and it just runs your books for you? No, that’s not going to happen,” he said. “It’s going to be steps at a time, discrete individual things … people will gradually become more comfortable with it.”

Data privacy, Kiely added, is the hinge between experimentation and enterprise adoption. “Companies have resisted bringing the technology onboard because they’re worried about data security,” he said. “[In our experience], you can put in place the proper agreements [with the model developers] so that you have the data security and the peace of mind that you need in order to work freely through the interface.”

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