The Challenge
A mid-market private equity firm managing six portfolio companies across manufacturing, distribution, and business services needed to determine where artificial intelligence could create measurable enterprise value. Portfolio leadership was receiving AI proposals from multiple vendors, but lacked a consistent framework for determining which opportunities were commercially meaningful, technically feasible, and appropriate for each company's level of maturity. With approximately 18 months until its next fundraise, the firm also needed a credible AI value creation strategy that could support both portfolio company investment decisions and its broader LP narrative. Our mandate was to answer three critical questions:
- Where could AI materially improve EBITDA?
- Which portfolio companies were ready to implement AI immediately?
- How should the firm prioritize capital across competing AI opportunities?
Our Approach
We conducted a company-level operational assessment across all six portfolio companies, evaluating opportunities within sales, supply chain, procurement, finance, reporting, workforce productivity, and customer delivery. We identified more than 40 AI opportunities and evaluated each using a structured prioritization methodology based on:
- Potential EBITDA impact
- Implementation feasibility
- Data maturity
- Technology readiness
- Time to value
- Required investment
Cross-Portfolio Analysis
We then analyzed the opportunities across the portfolio to identify patterns, shared use cases, and areas where common platforms, vendors, or capabilities could be deployed across multiple companies. Our analysis was translated into a portfolio-level AI investment thesis, sequenced implementation roadmap, and capital allocation framework.
Results
We also identified a cross-portfolio procurement and spend analytics opportunity that could support coordinated vendor selection across four portfolio companies.
- 40+ AI opportunities identified across six portfolio companies
- 12 priority initiatives recommended for near-term investment
- $30M+ in potential annual EBITDA improvement
- Three portfolio companies ready to begin AI pilots within 90 days
- Two companies requiring foundational data-readiness work before significant AI deployment
Business Impact
We helped the firm move from fragmented, vendor-driven AI experimentation to a structured investment strategy grounded in measurable business value. Our work gave leadership a clear view of where AI could create value, where the organization was ready to invest, and where foundational capabilities needed to be strengthened first. We also developed an LP-ready AI value creation narrative and a repeatable framework that the firm could apply to future acquisitions and portfolio companies.