Nate Heeren is a senior managing director and the national private equity leader at Riveron.
In the race to create value, private equity firms face pressure on all fronts, especially in the middle market. There, portfolio companies (portcos) are operating with leaner teams, tighter budgets and fewer in-house resources—all while facing the same expectations for speed and insight as their larger rivals.
Artificial intelligence has become a clear differentiator for midsized companies seeking to gain an edge over their competitors. Beyond improving cash visibility, scenario planning and portfolio monitoring, AI is unlocking new dimensions of efficiency and strategic insight across the investment life cycle.
Despite its benefits, the journey to transform AI from hype to an everyday tool is easier said than done. Firms must develop a pragmatic road map for AI implementation. Drawing on my experience in middle-market private equity, I’ll outline practical guidance for sponsors and management teams, what to prioritize first, what to avoid and how to effectively balance building versus buying technology solutions.
Start Smart: A Prioritization Lens For Mid-Market PE
Typically, the hardest part of any project is getting started. The same rings true for AI investment. When advising private equity clients on any major undertaking, I always recommend starting with areas that are hemorrhaging cash or have a high readiness for innovation. Newly acquired portcos often present immediate opportunities for transforming the office of the CFO through technology. The following use cases could be a strong starting point for mid-market organizations:
• Rapid document intelligence for contracts, customer cohorts and churn drivers: AI can ingest and synthesize information in real time.
• Key performance indicator (KPI) normalization across targets: AI provides automated mapping of metrics, revenue buckets and data definitions, enabling deal teams to compare apples to apples from the outset.
• AI-assisted cash-flow forecasting: Use historical patterns and external signals to tighten visibility and reduce forecasting errors.
• Accounts payable and journal-entry anomaly flags: AI can catch duplicate invoices, approval bottlenecks and unusual entries before they appear in profit-and-loss statements.
Sponsors and management teams must thoughtfully assess where AI can deliver the greatest impact, recognizing that some functional areas require substantial data preparation before an AI solution can be both valuable and reliable. Many mid-market companies—particularly those backed by private equity—face structural data challenges that complicate this effort, most notably the reliance on multiple, disconnected enterprise resource planning (ERP) systems and databases that make building a unified dataset nearly impossible.
These issues are often compounded by inconsistent coding across business units, which undermines accurate financial grouping and analysis. In addition, an overdependence on super spreadsheets for critical business processes means that core logic and data validation reside outside any auditable system, creating instability and risk in the data foundation required for effective AI training.
Once a stable data foundation is established, there are many other reliable possibilities to create value using AI, including:
• Predicting and optimizing inventory: Use deterministic modeling to analyze historical sales, seasonality and external factors to predict exactly how much stock is needed.
• Achieving labor and scheduling efficiencies: Deploy automated tools to forecast staffing needs based on customer demand, allowing management to optimize shift schedules.
• Detecting fraud and errors: Implement AI to monitor all financial transactions and employee expense reports, flagging unusual patterns or duplicate invoices often missed during human review.
To Build Or To Buy?
Once firms have pinpointed where to start their AI journey, they must decide whether to buy via acquiring a company with an existing AI advantage or to build by mobilizing an operations-focused strategy. Whether CFOs and other business leaders are overseeing the rollout of traditional or cutting-edge technology solutions, the core build-or-buy analysis remains the same: Define the problem, evaluate implementers and conduct a cost-benefit analysis.
But finance leaders cannot ignore the significant complexity added by AI. Unlike a static software product, AI is a dynamic capability that requires continuous integration and data management. Success hinges on a company’s data readiness, the availability of specialized talent and the ability to manage rapid technological advancements.
Acquiring a portco with a proven AI solution offers a direct path to market and a rapid value-add. This strategy aligns with a deal-focused approach, which demands rigorous due diligence on the model’s performance, data quality and intellectual property (IP) ownership to avoid buying significant technological debt. Developing AI in-house allows for a custom-built solution precisely aligned with the firm’s value-creation thesis. While choosing to build is more capital and time-intensive, it can create a powerful, proprietary advantage that can be scaled across other portcos.
Governance That Makes AI Work
Risk and controls are arguably the most important part of the AI implementation process, whether a company chooses to build or buy the capability. Human checkpoints remain a vital step in the AI adoption process. This includes establishing decision thresholds where a person must review outputs, defining escalation paths for exceptions and continuously validating that the model is performing as intended. Without this governance backbone, even the best AI use case can introduce new risks or undermine trust across the organization. And, since the governance, risk, compliance (GRC) landscape for new AI technology is relatively immature, it is helpful to engage seasoned governance and risk advisors who have ample experience helping similar companies navigate high-velocity change. This will help the organization establish compliant guardrails at the necessary pace.
AI is no longer a nice-to-have for mid-market private equity firms. To reduce costs, enhance efficiency and remain competitive with larger peers, firms and their portfolio companies must embrace new technologies before the gap becomes too wide to close. With a clear and disciplined approach, mid-market firms can transform AI from a buzzword into a habit and from a habit into a true competitive edge. Success starts by focusing on the areas that drive the greatest financial impact, maintaining a targeted scope, managing inherent risks and demanding measurable, data-backed results.
Ultimately, the firms that win in this new era will be those that view AI not as a project, but as a continuous capability, integrated into decision-making, embedded in operations and aligned with value creation at every stage of the investment life cycle.
The information provided here is not investment, tax or financial advice. You should consult with a licensed professional for advice concerning your specific situation.
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