The Automation Imperative: How Private Equity Must Lead the AI Transformation of Portfolio Companies
Automation is no longer simply an efficiency initiative for private equity operations; it is increasingly becoming a strategic and value creation imperative. For years, PE sponsors approached automation through a reactive lens by replacing aging equipment, while investing to improve throughput, expand capacity, or upskill labor as new needs arose.
However, as PE sponsors look to optimize automation solutions in the years ahead, upgrading machinery only on a reactive basis can leave value on the table. There are growing opportunities to take a front-footed approach and implement a broader catalogue of automation solutions within a portfolio company investment, especially as it pertains to customized machinery, vision systems, safety upgrades, and more recently AI-driven portfolio company analytics.
When automation is properly deployed, it can create sustained gains in throughput, labor productivity, quality,
visibility, and operational consistency.
One of the most underappreciated attributes of automation as a PE value creation lever is its durability. Many operational improvements implemented during a hold period depend on management teams maintaining processes and systems. Automation behaves differently. Once embedded into production, automated systems tend to persist. The cost savings and efficiency gains these systems deliver relative to manual processes in many cases outweigh the ongoing cost of maintaining and upgrading the automation. That durability can meaningfully influence the operating profile of the investment.
When automation is properly deployed, it can create sustained gains in throughput, labor productivity, quality, visibility, and operational consistency. As AI capabilities increasingly integrate into industrial workflows, from predictive maintenance to intelligent production planning, the impact becomes even more significant. For firms managing portfolios of manufacturing businesses, these differentiators have the potential to compound across the portfolio over time.
Automation at Work, Delivering Results
MiddleGround has applied this philosophy across a number of portfolio companies, using automation not simply to reduce costs, but to unlock operational improvements that persist long after implementation. At Race Winning Brands, a manufacturer of automotive and powersports components, we completed the first phase of an automated forging-press installation at the company’s Ohio facility, a project expected to generate approximately $9 million in equity value creation. The forging press had long constrained production due to lengthy die changeovers. Automating the process increased throughput while also improving ergonomics by reducing heavy lifting for operators and lowering temperatures on the production floor. The project represents the first phase of a broader modernization effort designed to make the forging operation safer and more efficient.
We applied a similar approach at the company’s Detroit-area facility, where MiddleGround engineers developed an automated system combining a robotic arm and hopper capable of processing hundreds of parts at a time. Previously, employees manually handled push rods during a heat-treatment process, holding them in induction heaters before transferring them to cooling stations, a repetitive task that added little value while exposing workers to unnecessary safety risks. Automation removed employees from a hazardous process, redeployed them to higher-value work and increased production throughput.
At exit, an automation roadmap signals that value creation is systematic rather than opportunistic, with a clear runway for the next owner to continue scaling.
Another area where we see a lot of promise is in gathering and applying data. At MiddleGround, we’ve worked on an AI application designed to compile operational data from a portfolio company’s systems that can be used to make better-informed business decisions. This system aims to produce data on different aspects of their business – providing visibility into key business trends and where there might be pockets of opportunity, allowing portfolio company leadership teams to spend more time on business strategy.
Automation as a Repeatable Value Creation Strategy
A critical component that determines success in planning and implementing automation systems is the utilization of a roadmap-based approach. Although it sounds simple, a longer-range view gives sponsors visibility across the entire hold period and encourages focus on initiatives that can be scaled over time. And if built properly, meaning that the end date extends beyond just your ownership, it creates a more durable operating framework and positions the business for continued success after exit. Building these capabilities at the firm level requires changes across the entire investment lifecycle.
First, automation must become part of the investment thesis at deal entry. Expected automation gains should be evaluated alongside procurement savings, pricing initiatives, and other operational improvements as part of the core value-creation plan. For many sponsors, this requires formalizing a capability that historically sat at the margins of the operating playbook.
Second, automation and process improvement teams must operate in parallel, rather than sequentially. In practice, that means conducting diligence together, optimizing the manufacturing process and automation solutions simultaneously, and implementing both through a coordinated effort. When lean manufacturing initiatives and automation programs are pursued independently, firms often end up automating broken processes. When integrated, the benefits can compound.
When sponsors empower portfolio companies to integrate their data systems with AI tools, it presents an opportunity to reduce one of the largest friction points in portfolio operations: reporting.
Third, firms should develop repeatable automation playbooks that can be deployed across a wide range of portfolio companies. Vision systems, in particular, represent a rapidly expanding opportunity set. A camera mounted above a production line, for example, can be trained to detect minute surface defects such as hairline cracks or small dents, before finished products leave the facility. Compared with major capital equipment upgrades, these systems require relatively modest investment while addressing one of the more costly failure modes in manufacturing: quality issues that go undetected until products reach customers. As technology has matured, applications across industrial environments have broadened significantly, enabling more advanced quality control, throughput optimization, predictive maintenance, and production monitoring capabilities than were previously available.
At exit, an automation roadmap supported by documented operational improvements and a pipeline of future initiatives may differentiate a business in a competitive sale process. It signals that value creation is systematic rather than opportunistic, with a clear runway for the next owner to continue scaling.
AI as the Operating Layer
Artificial intelligence is increasingly reshaping how sponsors monitor and manage portfolio company performance.
Historically, portfolio monitoring was labor-intensive and heavily dependent on management teams manually preparing reports. By the time operational trends emerge through monthly or quarterly reporting cycles, the window for early intervention is limited.
When sponsors empower portfolio companies to integrate their data systems with AI tools, it presents an opportunity to reduce one of the largest friction points in portfolio operations: reporting. Metrics, including labor efficiency, equipment utilization, scrap rates, and production throughput, no longer need to wait for month-end reporting cycles, and anomalies, such as labor inefficiency, utilization deterioration, or inventory dislocation, could be flagged automatically. Management teams are then able to spend less time assembling reports and more time thinking ahead, implementing strategic initiatives, and operating the business.
The objective is not to replace boots-on-the-ground operational engagement, but to make it more precise and effective. When sponsors and management teams operate from the same real-time data environment, they can align on priorities faster, identify root causes earlier, and work to intervene before operational issues materially impact performance.
What makes this especially powerful is the ability to identify trends across an entire portfolio simultaneously. PE sponsors can help detect emerging operational issues before they appear in quarterly reporting cycles, benchmark performance across facilities, and arrive at site visits with a far more targeted agenda.
The Window Is Narrowing
The conditions for embedding automation and AI capabilities into industrial PE operating models have never been more urgent.
Labor costs across manufacturing remain elevated, while skilled labor shortages continue to constrain throughput and capacity expansion. At the same time, automation technologies have matured, and implementation costs have become more accessible for middle market businesses. Meanwhile, AI-enabled operational monitoring has evolved from a theoretical concept into a practical tool with the potential to meaningfully improve portfolio oversight and decision-making.
Most sponsors are still in the early stages of treating automation as a true operational discipline. Firms that invest now in building centralized automation expertise, developing repeatable implementation playbooks, and integrating AI-driven operational monitoring may be advantaged as these capabilities become baseline expectations across PE.
For private equity firms managing industrial portfolios, the path forward is clear: automation must move from a line-item capital decision to a repeatable operational discipline, embedded into diligence from day one and deployed systematically across the portfolio.
About the Author
John Stewart is the Founding and Managing Partner of MiddleGround, which he established in 2018, where he oversees the overall management of the firm and serves on its investment committee.
John began his career as an hourly line worker at Toyota Motor Corporation, holding numerous management and executive positions over an 18-year career. He moved into private equity in 2007, joining Monomoy Capital Partners as a principal and head of the firm’s operating group. Over the next decade he was steadily promoted, becoming a partner in 2016. Across his career, John has worked on numerous transactions and served on the boards of more than 25 businesses, spanning both middle-market and Fortune 100 companies.
John is frequently sought after by fellow investors, limited partners, and business leaders for his vision and leadership in industrial manufacturing. His “blue collar” roots are the DNA of MiddleGround and set the tone for the firm’s culture.
This article is provided for informational purposes only and reflects the views of the author as of the date of publication. It does not constitute investment advice, an offer to sell, or a solicitation of an offer to buy any securities or investment products. MiddleGround Capital is a registered investment adviser. The operational improvements and strategies discussed herein are based on general industry observations and the firm’s experience; past operational results are not indicative of future performance. Any references to specific technologies, tools, or capabilities reflect the firm’s current understanding and are subject to change. This material should not be relied upon as a guarantee of any particular outcome.
