March 4, 2026
In many portfolios, AI has moved from a theoretical topic to a standing agenda item. Investment committees ask where AI shows up in the value creation plan, and management teams see a steady stream of vendor pitches and headlines. Yet behind the scenes, many leaders admit they are unsure where to begin in a way that is practical, measured, and repeatable.
At REEA Global, we work with private-equity-backed companies to modernize operational workflows and deploy AI in ways that show up in EBITDA and cash flow, not just in slide decks. In practice, the most effective initiatives rarely begin with a sweeping AI program. They begin with a proof of concept focused on a single workflow that can produce a measurable result.
The barrier to AI adoption is rarely the technology itself. It is operational complexity. Most portfolio companies run critical processes across ERP systems, spreadsheets, inboxes, and manual handoffs. Orders arrive by email and are keyed into systems. Vendor confirmations move between inboxes and shared drives. Finance teams reconcile data across tools that were never designed to work together. These processes consume thousands of hours each year while introducing delays and errors that quietly erode margins.
This is where AI-enabled workflow automation tends to have the most immediate impact. The opportunity is not to automate everything at once, but to identify the processes where automation removes friction from how the business actually runs. In many portfolio companies, the strongest candidates are familiar: manual data entry, repetitive document processing, or operational workflows that follow predictable rules. Even partial automation can materially improve speed, accuracy, and cost efficiency.
A practical starting point is a contained proof of concept designed to demonstrate measurable value in one workflow. The process begins by identifying a task that is repetitive, rules-based, and currently dependent on manual effort. Leadership should define success using metrics the business already tracks—hours of manual work removed, cycle-time improvements, or reductions in error rates. If the initiative cannot reasonably move one of those numbers, it is unlikely to justify broader investment.
The pilot itself should remain narrow and fast to evaluate. Many effective implementations move from concept to observable results within a few weeks. In portfolio companies operating under tight value creation timelines, this kind of focused experiment often fits naturally within the early phases of a broader operational improvement plan.
This pattern is already producing measurable results across mid-market portfolio companies.
One staffing company, for example, relied on staff to manually process hundreds of payroll forms each week. Employees opened each email, extracted compensation and withholding information, and entered the data into the accounting system. The work consumed thousands of hours annually and produced a double-digit error rate. An AI-enabled workflow that reads inbound forms, extracts the required data, and routes it into the accounting system with a simple verification step eliminated more than 2,000 hours of manual work per year while reducing errors by over 80 percent.
In another case, a private-equity-backed manufacturer employed a staff member whose primary role was reviewing hundreds of vendor emails each day and entering key data points into the ERP system. A focused automation pilot replaced that effort with an AI workflow that reads emails and attachments, extracts the required information, and sends it directly into the ERP. The result was roughly 2,000 hours of labor removed annually, about $75,000 in operating savings, and a meaningful reduction in processing errors.
Neither project began as an enterprise AI initiative. Both started with a narrow operational question: where is the business losing time to repetitive manual work?
The real value of the first proof of concept is not the single workflow it improves but the capability it creates. Once automation proves reliable in one process, leadership teams begin identifying similar opportunities across the organization—order entry, invoice processing, vendor confirmations, or customer onboarding. Each implementation becomes easier because the pattern is already understood.
For private equity portfolios, the leverage can extend even further. When a workflow pattern proves effective in one company, operating partners can test the same approach across others. Because the workflows are often similar across companies, the implementation can usually move much faster the second time. What begins as a small operational improvement can evolve into a repeatable value creation lever across the portfolio.
Operational improvements like these also translate directly into valuation impact. The financial effect can be meaningful. If automating a manual workflow removes $75,000 to $250,000 in annual operating costs, that improvement flows directly to EBITDA. For a company valued at an 8x multiple, the resulting increase in enterprise value ranges from roughly $600,000 to $2 million. When similar improvements accumulate across multiple processes, and eventually across multiple portfolio companies, the value creation compounds.
For most organizations, the practical path forward is straightforward: identify one manual workflow creating operational friction, automate the process in a contained proof of concept, measure the operational impact, and expand the pattern where results justify it.
AI adoption rarely begins with strategy. In practice, it begins with one workflow that works.
