How clean data drives AI success in private equity 

Clean, well-governed data is the foundation of AI adoption in PE. Here’s how CFOs can turn data governance into a growth driver—and avoid the risks of fragmented systems. 

Key takeaways 

• Clean data gives CFOs the foundation to deploy AI with accuracy, speed, and confidence. 

• Agentic AI tools are helping CFOs with proactive decision-making across the full value chain, from diligence to forecasting and competitive analysis. 

• Strong data governance helps PE firms balance AI opportunity with risk, protecting enterprise value and investor trust. 

In today’s PE-backed businesses, the Office of the CFO (OCFO) sits at the center of every data and AI discussion. Data quality now underpins valuation, reporting integrity, and the success of advanced analytics. Yet analysts warn that roughly 80% to 90% of enterprise data goes unused. Gartner predicts poor data quality or unclear value causes roughly 30% of generative AI (GenAI) projects to fail—meaning that without clean, unified data, even advanced analytics and AI tools can produce misleading, biased, or simply incorrect insights.  

For portfolio CFOs, the mandate is no longer just financial stewardship. They’re also being tasked with data governance, automating data validation, and cataloging and classifying data by training machine learning (ML) tools. For example, ML algorithms can flag duplicates and anomalies and correct inconsistencies at scale, while natural language processing can parse contracts and unstructured financial notes to auto-tag and categorize records. These AI-enabled tools make data governance faster and more dynamic, helping PE teams build a stronger foundation for transformation. 

How CFOs leverage agentic AI across finance workflows 

AI adoption is shifting from a back-office efficiency play to front-line decision-making. Firms are adopting agentic AI to plan, take actions, and adapt toward goals with minimal human intervention to both streamline and improve operations. For CFOs, the opportunity spans the full finance value chain: 

• Automating data room reviews during diligence 

• Flagging anomalies in FP&A forecasts 

• Running real-time competitor and KPI analysis 

• Tracking regulatory shifts across jurisdictions 

Firms deploying AI-backed FP&A report faster cycle times, leaner teams, and more accurate forecasting. However, Gartner warns that 40% of early agentic AI projects could be scrapped by 2027 due to escalating costs and unclear ROI. The opportunity is clear, but execution discipline will ultimately determine whether these tools can deliver sustained value. 

How CFOs manage AI risk in PE portfolios 

Post-deal integration continues to expose the fragility of data infrastructures. PE firms often acquire mid-market portfolio companies with disparate ERPs, reporting systems, and unorganized data environments—where AI can amplify risk as much as it creates opportunity. AI agents require unified, clean, orchestrated data to be successful, and it’s on CFOs to manage this new AI risk. As Kalin Anev Janse, CFO and member of the Management Board of the European Stability Mechanism, notes in the World Economic Forum, “Every leader, including CFOs, must champion AI and understand the systemic risks of generative AI in finance.” Left unchecked, errors in AI-generated numbers could erode investor trust. Embedding controls for security, validation, and explainability into AI workflows helps ensure stability and oversight.   

Looking ahead, AI adoption in PE will increasingly determine competitive advantages at the firm level. Early movers are embedding AI, and strong data and AI infrastructure is no longer just an efficiency play—it’s a differentiator in fundraising and reputation. 

Data governance as a value creation lever 

AI is transforming both finance roles and the industry. For example, during a recent enterprise data assessment for a $2 billion PE-backed SaaS company, Highspring uncovered fragmented and inconsistent data practices across customer, product, contract, and financial systems. To address these challenges, we designed a unified governance model supported by ML-driven data cleansing and third-party enrichment. This approach reduced redundancy, strengthened trust in financial reporting, and established a scalable foundation for advanced analytics and AI adoption.  

Without clean, well-governed data, even the most sophisticated AI solutions can’t deliver accurate insights or meaningful business outcomes. AI, ML, and data analytics are all double-edged tools, but when implemented carefully and correctly, the payoff can be high—helping PE firms achieve the next level of growth and performance. 

Turn your data into AI-driven growth 

As PE firms race to embed AI, the OCFO is becoming both the guardian and accelerator of AI adoption. What was once a back-office efficiency play is now a strategic differentiator in fundraising, valuation, and reputation. CFOs who embrace this expanded role and balance governance with innovation will strengthen enterprise value and position their portfolio companies for sustained growth. 

Connect with our PE and AI experts to explore how strong data governance can power smarter AI adoption for long-term success.