Private equity embraces automation and AI as core strategies for lasting industrial value

For much of private equity’s history, automation was treated as a largely tactical exercise: replace old kit, lift output, trim costs and move on. That mindset is increasingly out of step with how industrial businesses are being valued and managed. According to MiddleGround Capital’s John Stewart, automation is now becoming a core lever of value creation, one that can shape a portfolio company’s performance long after the initial investment has been made.

The shift matter...

s because the upside from automation is no longer confined to a narrow set of factory upgrades. In addition to machinery replacement and line-speed improvements, sponsors are now looking at customised equipment, machine-vision systems, safety enhancements and AI-enabled analytics. The broader objective is not simply to spend capital more efficiently, but to build operating advantages that endure through the hold period and remain in place at exit.

That durability is one of automation’s most compelling features. Unlike management-led process changes, which can fade if discipline weakens, embedded automation tends to stay in production. Once installed, the savings and productivity gains often outweigh maintenance and upgrade costs, making the benefits more persistent than many other operational initiatives. In a market where labour remains expensive and skilled workers are hard to retain, that permanence has real financial value.

MiddleGround says it has tried to apply that thinking across its industrial holdings. At Race Winning Brands, the firm completed the first phase of an automated forging-press installation at a facility in Ohio, a project it expects to contribute roughly $9 million in equity value creation. The press had become a bottleneck because of lengthy die changeovers. Automating that stage raised throughput, improved working conditions and reduced heat and heavy lifting for operators. The work forms part of a wider modernisation effort aimed at making the operation safer and more efficient.

A similar approach was taken at a Detroit-area site, where engineers built a robotic system with a hopper capable of handling hundreds of parts at once. The process replaced manual handling of push rods during heat treatment, removing workers from a repetitive and potentially hazardous task while lifting output. The example illustrates a wider point: automation can do more than lower headcount-related costs. It can also improve quality, reduce risk and redeploy labour towards higher-value work.

The argument for a more systematic approach is strengthened by the rise of AI. Industry groups and specialist advisers across private equity are increasingly promoting AI not as a separate theme, but as an operating layer that sits alongside automation. HatchWorks AI, for example, says embedding AI within portfolio companies can modernise operations and create defensible intellectual property. Digital Alpha has argued that AI-driven transformation can expose hidden EBITDA and accelerate returns. Brownloop, meanwhile, focuses on unifying portfolio data so leadership teams can spot trends earlier and make decisions faster.

MiddleGround’s own view is similar. Stewart describes an internal AI application designed to pull operational data from portfolio systems and turn it into usable insight. The aim is to improve visibility into performance trends and identify emerging pockets of opportunity, while reducing the time management teams spend assembling reports. That matters because traditional reporting cycles can be too slow to catch problems when they first appear. If labour efficiency weakens, utilisation drops or inventory drifts out of balance, a monthly pack may arrive too late to support quick intervention.

Private equity firms increasingly want their operating models to move at a much faster pace. If portfolio companies can connect their systems to AI tools, sponsors may be able to identify anomalies in near real time, compare performance across facilities and arrive at site visits with sharper questions. The idea is not to replace hands-on operational work, but to make it more precise. Better data should, in theory, lead to better site priorities, quicker root-cause analysis and stronger accountability.

What separates the best approaches is planning. Stewart argues that automation should be built into the investment thesis from the start, rather than bolted on after acquisition. That means underwriting automation gains alongside procurement savings, pricing actions and other operational levers. It also means running process improvement and automation work in parallel, rather than treating them as separate tracks. If a sponsor automates a broken process, the technology may simply entrench inefficiency. If both are designed together, the benefits can compound.

There is also a growing case for repeatable playbooks. Vision systems are a good example. A camera placed over a line can be trained to detect small defects before goods leave the plant, helping to prevent costly customer complaints and returns. Compared with heavy equipment upgrades, these systems can be relatively modest investments, yet they can have an outsized impact on quality control, predictive maintenance and throughput.

The broader message from sponsors and advisers alike is that the window is narrowing. Labour shortages remain a constraint, manufacturing wages are still elevated and automation tools have matured to the point where they are increasingly accessible to middle-market businesses. AI is no longer a theoretical overlay. For firms that manage industrial portfolios, it is becoming part of the infrastructure of value creation.

At exit, that can matter as much as the operational gains themselves. A business with a documented automation roadmap, a clearer data environment and a credible pipeline of follow-on initiatives may look more scalable to buyers. In a competitive sale process, that can help signal that improvement is systematic rather than opportunistic, and that the next owner has room to continue the journey.

For private equity, then, automation is moving from a capital allocation decision to a discipline in its own right. The firms that treat it that way, and that pair it with AI-enabled oversight, are likely to be better placed to extract durable gains from their industrial holdings.

Source: Noah Wire Services