BOTTOM LINE UPFRONT
In episode two of Accordion’s podcast AI & PE: The Future of Value Creation, Miles Rowland and Kyle Roemer discuss how AI is evolving from a productivity tool into a core value creation engine for mid-market private equity — driven by embedded use in diligence and value creation planning, lightweight architectures, and natural language access to trusted data that accelerates decisions and ROI.
In this episode of AI & PE: The Future of Value Creation, Accordion’s U.S. Head of Data & AI Kyle Roemer speaks with Miles Rowland, leader of Data & AI initiatives within Searchlight Capital’s Value Creation team.
Their discussion hits on how AI is reshaping the data stack, operating models, and decision-making speed in mid-market private equity. Miles breaks down a practical AI maturity framework, explains why natural language interfaces to data are the biggest near-term unlock, and shares how PE firms can embed AI directly into value creation planning to drive real ROI.
Here are the takeaways:
1. AI maturity follows a clear progression
Miles outlines a four-level AI maturity model:
- Level 1: Standalone chat tools (ChatGPT, Claude, Gemini) for individual productivity
- Level 2: AI connected to internal tools and data warehouses
- Level 3: AI that can take action (agents executing tasks)
- Level 4: Ambient AI — agents running quietly in the background, triggered by events or schedules
The key inflection point is when AI no longer requires prompting and instead operates autonomously.
2. Advanced AI no longer requires heavy engineering
What once demanded large development teams can now be achieved with lightweight architectures, human-readable instructions, and minimal code. This shift makes sophisticated AI automation realistic for mid-market companies without deep technical benches.
3. AI works best when embedded in value creation planning
At Searchlight, data and AI are integrated into diligence, value creation planning, and the first year post-close. AI initiatives are explicitly tied to priority value levers — such as churn reduction or pricing optimization — rather than pursued as standalone experiments.
4. There is a natural path from analytics to automation
The most effective use cases follow a progression: diagnostics through BI, insight through data science, and execution through AI automation. For example, identifying churn drivers, predicting at-risk customers, and automating targeted interventions.
5. Speed and agility are the real advantage
AI dramatically compresses the time required to test, adjust, and scale operating changes. What once took months — updating pricing, changing workflows, retraining teams — can increasingly happen in days, materially increasing business agility.
6. Natural language interfaces will transform how data is used
Instead of relying on sprawling dashboards, business users will increasingly “talk” to their data. With structured data and proper context, natural language interfaces allow leaders to ask ad-hoc questions and get reliable answers — especially in board, diligence, and operating settings.
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