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Key takeaways:
- Middle-market portfolio companies often struggle to realize AI value because of fragmented systems, inconsistent data, and manual workarounds.
- Leading private equity firms are expanding the traditional value creation playbook through increased focus on data, technology, and AI.
- A phased, repeatable approach can help portcos improve performance today while building a stronger, AI-ready foundation for future growth and exit.
While the typical middle-market, portfolio company1 has plenty of strengths, these portcos generally lag larger enterprises in the maturity of their core operating infrastructure (technology, data, and standardized processes). Underinvestment has resulted in fragmented systems, limited automation, inconsistent data, and a reliance on manual workarounds and internal knowledge. Further, management teams may struggle to execute on middle-market portco needs, and PE firms take too long to replace leadership, as needed. All of this results in significant delays, and when these companies finally get around to their AI projects, they may be unable to create the value they expected from a use case. The actual issue may not be the use case itself, but the core people, processes, systems, and quality of data it is pulling from to execute its function.
To complicate matters, market conditions (interest rates, valuations, uncertainty, etc.) are causing middle-market portcos to be held much longer than in the past, with median exit hold time stretched to six to seven years, putting pressure on liquidity, limited partnership (LP) expectations, and overall returns.2 In other words, the middle-market portfolio must evolve, but consensus has not emerged on how to drive durable growth.
Leading firms are finding that the answer isn’t to abandon the traditional PE playbook, but to expand it through increased focus on data, technology, and AI. By adding new, data-enabled insights to drive value faster and with more confidence, PE firms are giving middle-market portcos new ways to accelerate revenue growth, expand margins, surface insights, and improve their AI-enabled plans across the board. Further, the longer hold times are giving firms the opportunity to build up their portcos’ fundamentals that will make their portcos more attractive to buyers upon exit. Based on our experience, we see five data-driven opportunities firms can use to reshape portco operations, improve their value creation potential, and unlock AI’s potential.
1. We define a middle market portfolio company as businesses where revenues are typically between $10M and $1B.
2. PwC Analysis based on Pitchbook data.
Five data-driven trends to help your portcos create value
The easiest place to start building a data-driven portco is by taking an inventory of the data the portco takes in, (i.e., what activities are tracked, not tracked, and/or should be tracked), systems in place, and talent to execute. Finding those easy opportunities can create a small, early lift toward value creation insights. From that point, we see five ways portcos are creating value today.
1. Changing behavior from reactive to proactive
The level of technological maturity should not limit the possibilities for data-driven value creation. Even in highly commoditized businesses, data can reveal meaningful value without a large technology overhaul. Take a chain of parking garages. A full-stack transformation is neither necessary nor cost-effective. Instead, lightweight tools—such as AI-enabled dashboards, simple chat interfaces, and limited on-site hardware—can combine internal operating data with third-party inputs to forecast demand, anticipate event-driven spikes, and surface recurring customer issues. In turn, these tools can translate those insights into clear operating guidance for frontline teams.
These days, the challenge is rarely the tools or tech but changing the operating mindset. Many teams are managed reactively, waiting until problems surface to make a change. The goal is to shift this mindset into anticipating issues and acting before value is lost. The most effective starting point is often a small set of practical metrics, such as social sentiment analysis or third-party data on vehicle traffic patterns in surrounding areas that could impact the garage’s capacity. The result could be the creation of dynamic pricing models that can balance available supply and demand to drive targeted revenue growth. Early use cases can deliver fast, visible wins and build credibility, while establishing the basics that can be scaled across locations and refined over time.
2. Using supply chain and sales data to grow margins and find opportunities
How many of your portfolio company management teams can tell you, right now, the status of manufacturing capacity, in-transit inventory, and SKU-level availability? How clearly can they see what customers are buying, at what prices, and the margin impact? For most companies, this visibility exists only at period-end, if at all. For such firms, moving to real-time decision-making would be a revolutionary improvement simply by better managing inventory, SKUs and pricing.
Consider an apparel manufacturer entering spring with excess winter inventory. Their typical options are blunt: discount heavily or carry the inventory into the next season. But, with basic tools that integrate internal sales data, public market signals, and margin analytics, a more precise path can be taken. Adding these tools results in being able to use targeted promotions for specific customer segments outside the portco’s core customer base, resulting in improved sell-through, preserving margin, reducing carrying costs on last season's apparel, and strengthening inventory discipline. These capabilities deliver near-term results and signal a more sophisticated operating model that buyers value at exit.
3. Making data your engine for commercial growth
How clearly do your portfolio companies understand their markets? How effectively are they using data to guide product development, pricing, customer acquisition, and sales execution? We’ve seen middle-market portcos use data in pockets, but it remains fragmented with separate data sets across sales, customer success, and finance. Limited visibility and inconsistent decision-making is typically the result, with leadership often lacking a single, trusted view of performance.
Data silos are an old problem. What’s new is how cheaply and quickly those silos can be broken down. Technology now makes it practical to connect disparate data sources and put usable insights directly into the hands of operators, improving execution, accountability, and performance.
Consider a portfolio company where bookings are rising, but revenue is flat. Too often, management will have to sift through competing explanations from sales and customer experience teams. By creating an integrated data foundation (combining all internal data sources), management can quickly see which customers are at risk, where usage is declining, and which segments are under pressure. Teams can then prioritize outreach, adjust pricing or packaging, and intervene earlier. The result? Proactively managing the at-risk customer’s churn rate, which protects the recurring revenue of the business. Plus, instead of reconciling conflicting reports, there is now one place to go for answers, enabling faster decisions and more consistent results.
4. Training employees to live and breathe data
Access to powerful technology is no longer a limit on analysis. The differentiator now is talent. Do teams have the skills, confidence, and authority to make judgments so they can effectively use data to make decisions and flag issues?
For portfolio companies, value creation increasingly depends on building a data-literate and tech-enabled pool of talent. Teams need to understand when to use data, how to interpret it, how to challenge outputs, and how to translate insight into action. Only human capabilities like these can fully engage with technology and wisely leverage it for the company’s benefit.
For example, sales, customer success, and operations teams often spend hours searching for information before they can act. With focused training and clear operating expectations, the same teams can use existing data to respond faster, prioritize the right actions, and improve outcomes in customer interactions. Follow-ups that once took days can happen in minutes—without adding complexity to the tech stack. Customer service is improved, enabling the business to either better retain customers and create real-time positive experiences (driving positive reviews, higher net promoter scores, etc.).
The call to action is clear: invest in people, not just platforms. Organizations that pair accessible technology with targeted training, role-specific guidance, and aligned incentives turn data into an everyday operating advantage. Those that don’t risk owning powerful tools with wasted potential and no impact on performance or return.
5. Moving toward a data end state with small, profitable steps
Some portfolio companies are ready to take the next step in how they operate: moving from fragmented reporting to an integrated, insight-led model where data informs decisions in real time. This shift does not require a major transformation or multi-year program. It starts by focusing on a single, high-impact area, proving value quickly, and then scaling systematically as the right people, processes, data, and technology are put in place.
Consider a multi-location retail business where regional leaders regularly travel to assess store performance. With a simple, connected view of real-time inventory trends, margins, and external signals, such as social sentiment, those leaders can prioritize which locations need attention and take targeted actions without being on-site. Once a data-driven approach is delivering measurable results for the company, it can be extended to other functions such as call centers, marketing, and finance. Further, the process is then ready to be enhanced with AI with limited disruption to current operations. The result is a repeatable model for sharing best practices with locations that may not perform as well. Building a business that can nimbly manage margin and costs, creates more informed decision-making across the business, and can lift the results of all locations.
Driving return through data: What to do now
Building data expertise across your portco can be a journey of a thousand steps, but there are some actions to take today to kick-start your data strategy.
- Start where the value is clear. Target a small number of portfolio companies and focus on use cases where accessible data, proven tools, and a clear economic case can generate results quickly. Prove impact first, then scale within the business and across the portfolio.
- Cross-functional first. There is often a lack of collaboration between commercial, operations, and the IT department building the tools. Building a cross-functional team before a project gets off the ground can be a path to success.
- Favor reliability over novelty. While some companies may benefit from leading-edge solutions, most will see stronger returns from simple, low-cost approaches such as basic monitoring, external data sources, and dashboards that operators actually use.
- Design for repeatability. Sustainable value comes from embedding data into everyday decisions: pricing at the right moment, intervening early in underperforming locations, or protecting high-performing teams from unnecessary disruption.
- Use early wins to build the foundation. As results accumulate, begin to connect initiatives into a coherent data backbone that supports future growth, whether through broader AI enablement, improved operating discipline, or selective digital expansion.
- Keep your data organized. When you approach an exit, the market (and future buyers) expects your portco to be AI-ready, if AI programs are not yet in place. Having data that is easily accessible and usable by that technology is an absolute must.
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