Private Equity Firms Bet Big on AI for Faster Returns

What's New

According to Harvard Business Review, private equity firms are aggressively developing systematic AI implementation strategies to accelerate value creation in portfolio companies, despite industry-wide struggles with AI returns where only 10% of large companies report significant ROI from generative AI investments.

Why It Matters

PE firms' compressed 5-7 year investment timelines create unique pressure to prove AI's value quickly, making them laboratories for rapid AI deployment strategies that could provide blueprints for broader corporate adoption across industries.

Big Picture Drivers

• Talent Evolution: Firms are shifting from hiring scarce data scientists to full-stack AI engineers who can build and deploy solutions faster with modern development tools

• Due Diligence Integration: Leading firms now embed AI assessment into acquisition processes, with some deploying 25 general partners in AI evaluation workflows

• Operational Focus: Analytical AI applications for customer analysis and cross-selling are delivering faster returns than generative AI productivity tools

• Data Infrastructure: Firms are prioritizing selective data quality improvements over comprehensive overhauls to accelerate implementation timelines

• Exit Strategy Planning: AI initiatives must demonstrate measurable operational improvements to attract future buyers, not just proof-of-concept potential

Key Trends to Watch

• Systematic frameworks like "DANCE" (Discover, Automate, Novel creation, Customize, Enhance) are emerging for identifying high-value AI use cases across portfolio companies.

• Industry exposure assessments are becoming standard practice to identify sectors with highest AI opportunity and risk before making investments.

• Analytical AI applications are outperforming generative AI in delivering measurable business outcomes and faster returns on investment.

• Ecosystem partnerships with curated technology and implementation vendors are replacing internal AI development capabilities at most firms.

Memorable Quotes

• "This is a carpe diem moment for companies to see their data as an off-balance sheet item. Data can be both a latent asset and a latent liability." - Sajjad Jaffer, head of data and analytics at GrowthCurve Capital

• "Avoid the temptation to boil the ocean" - PE AI leader on selective data quality improvements rather than comprehensive overhauls

• "While the role of data scientists remains important for certain initiatives, advances in AI development and analytics tools now allow full-stack AI engineers, working closely with subject matter experts, to quickly build and deploy AI solutions at scale." - Misha Logvinov, operating partner at MGX

• "Underwriting value creation from data and AI at the outset significantly increases the likelihood of successful implementation during the ownership period." - Cory A. Eaves, partner and head of Portfolio Operations at BayPine

Key Insights

• Speed Over Perfection: PE firms focus on rapid deployment of proven AI use cases rather than perfect solutions, prioritizing measurable operational improvements that can be demonstrated to future buyers within compressed ownership timelines.

• Analytical AI Wins: Traditional analytical AI applications for customer segmentation and cross-selling opportunities are generating faster, more measurable returns than generative AI productivity tools across portfolio companies.

• Talent Strategy Shift: The industry is moving away from hiring expensive, hard-to-retain data scientists toward full-stack AI engineers and curated consultant ecosystems that can deliver results more efficiently.

• Data as Strategic Asset: Leading PE firms are helping portfolio companies recognize data quality as a critical value driver, but focusing improvements only on domains that directly support high-impact AI use cases rather than enterprise-wide data management initiatives.

The Wrap

Private equity's mandate for rapid value creation is forcing the development of proven, repeatable AI playbooks that prioritize measurable operational improvements over experimental applications. Their systematic approach to AI due diligence, talent deployment, and use case prioritization offers a pragmatic blueprint for any organization seeking to move beyond AI pilots to production-scale value creation.