The New PE Operating Model: How AI Executives Are Redefining Value Creation in Private Equity – CDO Magazine

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Written by: CDO Magazine
Updated 8:00 AM EDT, July 3, 2026
Private equity is entering a transformative new era. Traditional value creation levers are becoming less effective in high-multiple markets. Standard practices like leverage, multiple arbitrage, and SG&A cost reduction no longer yield the same results. Consequently, firms are turning to operational excellence and AI-enabled transformation.
In this CDO Magazine and Burtch Works webinar, industry leaders explored how private equity firms are leveraging AI and modern data infrastructure to accelerate enterprise value creation. The discussion examined practical approaches for scaling AI across portfolio companies, improving operational efficiency, and enabling measurable business outcomes.
The traditional private equity playbook is evolving. The panel discussed how AI can help organizations scale operations by shifting repetitive work from manual processes toward data-driven automation. Rather than relying solely on financial engineering, firms are increasingly using AI to improve productivity, optimize operations, and support EBITDA growth across portfolio companies.
Should organizations build a perfect data foundation before implementing AI, or begin with high-value use cases? The panel advocated for a modular, business-first approach: identify high-impact, repeatable workflows, define the supporting data requirements, build fit-for-purpose data foundations that are “good enough” to enable the initial solution, and continue maturing the data environment as value is demonstrated.
The panel highlighted the growing importance of hybrid professionals who combine technical expertise with business and operational understanding. From forward-deployed engineers to T-shaped professionals, these roles bridge engineering, data, analytics, and business strategy, helping organizations accelerate AI adoption while ensuring solutions address real business needs.
The discussion distinguished between improving workforce productivity through automation and creating lasting business transformation. While automation focuses on reducing manual effort and increasing efficiency, transformation reshapes products, services, customer experiences, and business models—ultimately creating greater enterprise value.
Rather than implementing AI for its own sake, the panel emphasized beginning with business challenges and measurable outcomes. By identifying high-impact operational workflows first and then designing the supporting data and AI capabilities around them, organizations can demonstrate value more quickly while building a scalable foundation for future initiatives.
As enterprise AI adoption continues to expand, organizations are placing greater emphasis on governance and cost management. The panel discussed approaches such as Retrieval-Augmented Generation (RAG) and fit-for-purpose small language models (SLMs) to optimize token usage, improve efficiency, and help protect proprietary enterprise data while maintaining performance.

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