Garbage In, Garbage Out.
When it comes to private equity firms adopting artificial intelligence, it couldn’t be more relevant.
At our recent AI Pathfinder briefing, the message was clear: the journey to effective AI begins with strong data foundations. While the potential of AI is vast, without quality, structured, and accessible data, firms risk undermining their AI investments from the outset.
Here's what we learned and what you need to know about getting your data foundation right.
Where Private Equity Stands Today
Our audience survey revealed some clear signals from the market:
48% of participants said data is a significant value creation driver and they are already measuring its impact
50% have already developed a data foundation (including data management and platform), and another 42% plan to do so in 2025
The top reasons 'AI Ready Data' is critical for portfolio companies included Efficiency, Accuracy, Foundational, and Speed.
While progress is being made, these results also suggest many firms are still early on the journey and as one speaker put it:
“Every organisation I’ve worked in talks about having poor quality data, but nobody can explain what poor quality data is… I think you have to dumb down your aspiration to something achievable and start with a subset.”
This tendency to overcomplicate can result in fragmented strategies, missed opportunities, and a failure to scale successful pilots.
The Hidden Costs of Poor Data Foundations
Poor data isn't just an IT headache it’s a strategic disadvantage:
Delayed initiatives: Good ideas stall because teams spend more time managing data issues than using insights
Reduced competitive edge: Competitors with better data capabilities move faster, capitalise quicker, and gain market share
Limited scalability: Promising AI pilots rarely scale into meaningful operational changes
Underutilised assets: Valuable institutional knowledge is often hidden in unstructured or disconnected data silos lost or forgotten over time
Wasted investments: Firms risk spending time and money on tools and platforms that can’t perform effectively due to foundational data issues and have little ROI
Defining the AI-Ready Data Foundation
A robust AI data foundation isn't just clean data it's about structure, accessibility, and governance. Key pillars include:
Data Management: Clear standards, quality controls, and comprehensive documentation to ensure consistency
Cloud Data Platforms: Accessible, secure, scalable platforms that democratise data across teams
Structured Governance: Defined roles, responsibilities, and policies to ensure sustainable data usage and compliance
Your AI tools are only as effective as your data allows them to be. Get these foundations right, and you set the stage for meaningful, scalable AI outcomes.
“You can't do it unless the data’s there, and unless it’s clean, and unless it’s well structured.”
It’s also worth noting that a centralised data platform may not always be the best starting point. As one participant shared:
“I know it’s a contrarian view, but in many mid-cap businesses, building a big, centralised data platform can be a costly distraction. You can often get faster results by using agents to pull the data from internal and external sources without trying to redesign everything from scratch.”
This highlights the importance of aligning your data strategy to your firm’s capabilities and context.
Practical Steps: Creating Your AI Roadmap
Improving your data foundation doesn't happen overnight. It requires a clear, structured approach:
Conduct a Data Maturity Assessment: Know precisely where you stand today and identify immediate risks and opportunities
Prioritise AI Use-Cases Strategically: Align data investments directly with your strategic objectives and desired outcomes
Build Your AI Infrastructure: Establish robust, scalable data infrastructure that supports current and future use-cases
Implement Continuous Learning: Regularly measure outcomes, refine your approach, and steadily improve your data quality and utility
Why This Matters to Value Creation
Data is no longer a back-office concern it’s a strategic enabler. Poor data quality directly hampers the ability to derive measurable ROI from AI at the portfolio level. Inaccurate, incomplete, or inaccessible data can derail initiatives meant to enhance decision-making, slow down time-to-value, and erode confidence at the investment committee level. It’s central to value creation:
Nearly 9 in 10 participants at our session confirmed data quality and availability are either already driving measurable value or are recognised as strategically important.
92% of respondents either already have or are planning to develop a data foundation as part of their value creation strategy.
Source: Live participant survey from AI Pathfinder Breakfast Briefing, June 2025
“Pointing it at the right use case is really, really important and barrier of entry to access AI is incredibly low, what raises the barrier is actually how you're going to get ROI from it and how your defensible proprietary IP is there to be placed on that as a framework that will get that ROI”
Building and Evolving Your AI Strategy
On 16 September, we’ll be hosting a Private Equity AI Strategy Day at the Sea in London.
It will be a full-day experience allowing you to spend time exploring honest and accessible case studies as well as rolling up your sleeves on practical activities. The aim is to:
See what leading firms are doing to build AI advantage
Go deeper into practical use cases across the deal cycle and value creation
Explore what tools, teams and thinking are needed to drive progress
Expect a mix of keynotes, working sessions, and curated peer exchange.