In today’s data-saturated market, the challenge for private equity firms isn’t access to metrics, it’s knowing which ones truly drive value. The most effective operators start with the end in mind: aligning data with the equity story and the value creation plan. Metrics should serve as the connective tissue between investors (validating the investment thesis), management (tracking and steering value creation initiatives), and operators (informing day-to-day decisions). When done well, this alignment creates a unified narrative that not only drives operational improvement but also strengthens the equity story for exit, ultimately supporting a higher multiple.

The Problem with Too Much Data 

Private equity-backed businesses often suffer from a paradox: they’re drowning in dashboards, yet starved of insight. Legacy reporting systems tend to prioritise completeness over clarity, surfacing hundreds of KPIs that offer little strategic or forward-looking insight. 

What’s missing is a framework that ties metrics to three critical layers: investor expectations, management priorities, and operational execution. Without this linkage, metrics become noise rather than a strategic asset. What’s needed is metric discipline—a ruthless focus on the few indicators that validate the investment thesis, guide management decisions, and empower operators to deliver value. 

A Six-Step Framework for Identifying the Metrics That Matter

1. Start with the Equity Story 

Every investment begins with a set of key narratives (e.g., margin expansion, recurring revenue growth, geographic scale, operational efficiency). Define this story clearly, because it will anchor every metric you track. Ask: What are the stories we are looking to tell that we must evidence at Exit?  Your metrics should validate that thesis.

2. Map Metrics to Equity Narratives

Translate the equity story into measurable indicators across key domains: 

• Customer & Revenue: e.g., retention (GRR, NRR), organic growth, lifetime value 
• Operations: e.g., gross margin, throughput per FTE, cost-to-serve 
• Sales & Pipeline: e.g., pipeline velocity, conversion rates 
• Human Capital: e.g., attrition rates, productivity metrics 
• Finance: e.g., EBITDA margin, cash conversion cycle 

Consider both value creation impact (how metrics drive commercial and/or operational improvement) and exit multiple impact (how they influence valuation).

3. Prioritize Metrics Using a 2×2 Matrix 

Define reporting use cases and plot metrics on a Time-to-Value vs. Impact matrix: 

• High Impact / Quick Wins: Prioritise these first for immediate ROI 
• High Impact / Longer Horizon: Plan for these in roadmap 
• Low Impact / Quick Wins: Consider if they add incremental value 
• Low Impact / Longer Horizon: Deprioritise 

This ensures resources focus on metrics that matter most.

4. Conduct a Gap Analysis for Priority Metrics

For the highest-priority metrics, assess data across systems of record: 

• Availability: Does the data exist? 
• Quality: Is it accurate and consistent? 
• Granularity: Can it be sliced by dimensions (e.g., customer cohort, geography, product line) to uncover trends? 

It’s key to consider the required data history at this stage, e.g., how many years of data are available across current and legacy systems. This will determine what systems are in-scope for the Gap Analysis. For a transaction, three years is generally the minimum, but more years will be required for deeper historical analysis such as customer journeys, lifetime value and cohort evolution.

This step reveals feasibility and highlights where enrichment is needed.

5. Define Data Remediation Needs

Where gaps exist, determine what’s required: 

1. One-time Improvements 
   1. Data clean-up: e.g., customer de-duplication 
   2. Data enrichment: e.g., adding industry, sector, or location via third-party sources 
2. Ongoing Process Changes 
   1. E.g., adding new fields and/or replacing free-text with mandatory inputs to systems to improve structure 
   2. Where new fields are created and therefore historical data is not available, you may need to consider working with key users (channel partners, sales teams) to do a one-time data backfill

These actions ensure data integrity and unlock advanced analytics.

6. Define KPI Tree and End-User Groups

Design a KPI hierarchy aligned to user needs: 

• Board & Investor Level: Strategic KPIs tied to equity story 
• Management Level: Metrics for tracking value creation initiatives 
• Operational Teams: Granular KPIs for day-to-day execution 

This ensures visibility and accountability at every level, driving ROI and reinforcing the equity narrative. 

It’s imperative to align on the granularity of reporting required at each level, and the number of metrics displayed. For operational teams, you should ruthlessly prioritize the highest impact KPIs that will drive action and remove any secondary metrics that do not drive desired behavior. Additionally, key to supporting operational teams behavior is ensuring 1) Trust in the numbers through early engagement and effective change management, and 2) Full alignment in metric definitions. 

Actionable Takeaways for PE Leaders

• Audit your metrics: Eliminate noise and elevate the few that matter. 
• Align across layers: Ensure metrics connect investor expectations, management priorities, and operational execution. 
• Link to the exit: Every metric should reinforce the equity narrative and support valuation uplift. 
• Invest in granularity and build trust: Clean and enrich the highest value data, and further segment to uncover hidden value. 
• Champion forecasting: Move from hindsight to foresight. 
• Drive cultural adoption: Make data fluency a core competency. Coach and incentivise teams to trust and own their data – move away from ‘institutional knowledge and storytellers’ to ‘data-driven decision-makers’.  

JMAN Group

At JMAN Group, we help private equity firms and their portfolio companies turn data into commercial advantage. Our approach combines strategic clarity with technical depth—enabling clients to identify the metrics that matter, forecast with confidence, and tell compelling stories at exit. 

Let’s Talk If your firm is ready to move from reporting to forecasting and insight, we’d love to help. Reach out to explore how JMAN can support your next value creation journey. 

Your company’s network is not just a list of contacts, it is an intricate web of relationships – internal team members, external influencers, and revenue-generating opportunities. Without a clear map, crucial connections aren’t leveraged, and the true value of an individual’s or the firm’s network remains invisible. This leads to:

• High-value prospects that never enter your sales pipeline.
• Relationships that could open doors, but are never identified nor capitalized.
• Difficulty in quantifying the real return on your relationship investments.

With your prospects’ decision-makers under pressure, a cold call is still a cold call, however much research the sales team has done. Everyone knows that the best “in” is a genuine, authentic relationship. But how do you build that kind of relationship when the end of the quarter is approaching and you need results now?

Artificial Intelligence (AI) is transforming every industry it touches, but not every business is doing what it takes to seize the opportunities that exist right now.

The JMAN Group AI Readiness Assessment, developed in collaboration with industry leaders Pinsent Masons, Inference Group and Cyber DueDil, provides a comprehensive and insightful evaluation of your organization’s AI maturity. This self-service survey tool empowers you to:

• Identify your strengths and weaknesses across six critical dimensions of AI readiness.
• Uncover opportunities for growth and strategic AI implementation.
• Benchmark your organization against industry peers of similar size and geography.
• Gain a clear understanding of your current AI maturity level and the next steps to elevate it.

Our assessment is designed to meet the needs of:

• Independent Companies: We will give you a complete understanding of your current status and chart a course for AI integration.
• Portfolio Companies: Easily evaluate individual portfolio businesses and identify areas for AI-driven value creation.
• Private Equity Funds: You will gain a holistic view of AI readiness across your portfolio with our unique comparison feature.

The Six Dimensions of AI Readiness:

AI Readiness is assessed across the following key areas:

• Strategy and Value: To what extent is AI a strategic priority with clear alignment to business goals?
• Operating Model: Does your business have the cross-functional collaboration and agility for effective AI delivery?
• People and Culture: Does your organization possess the necessary technical talent and data-driven mindset?
• Risk, Legal and Ethics: Are you equipped to navigate the complexities of AI risk management and compliance?
• Infrastructure and Tooling: Does your technology foundation support your current and future AI ambitions?
• Data: Are your data governance, availability and pipelines optimized for AI applications?

Don’t get left behind. Whether you’re just starting your AI journey, facing a plateau in your progress, or want to be at the vanguard of this rapidly-evolving space, the JMAN Group AI Readiness Assessment provides the clarity and direction you need.

Take the first step towards enhancing your AI Readiness.

Artificial intelligence (AI) is reshaping how PE deals are identified, evaluated, and managed. It’s not just about algorithms crunching data; it’s about rethinking strategies, operations, and even the very essence of decision-making in the PE industry. With emerging technology, how it will impact the industry remains to be fully understood, but in this article we’ll be looking at how far some PE firms have already pushed the limits. Let’s unpack this transformation.

More Than Just Data: Why AI Matters in Private Equity

AI’s influence in PE isn’t limited to automating mundane tasks (though it’s great at that too). It’s about enabling firms to:

• Spot opportunities before they’re public knowledge.
• Predict market trends with uncanny accuracy.
• Streamline portfolio company management.

Embracing AI in private equity not only enhances decision-making but also helps firms stay ahead in a rapidly evolving market.

Finding Deals: From Gut Feelings to Data-Driven Insights

Sourcing deals used to depend entirely on intuition, personal networks or closed-door meetings. Those days are gone. AI-powered platforms now scan troves of unstructured data — social media chatter, news reports, industry forums — to identify potential targets. What’s more, machine learning models can analyze:

• Market positioning.
• Revenue trajectories.
• Competitive landscapes.

Take firms like Signal AI or Palantir. They sift through extensive datasets to provide insights that would take a human weeks to uncover. And while intuition remains crucial, it is now just one piece of the jigsaw.

The Diligence Dance: Smarter, Faster, Better

Due diligence used to be an exhaustive, manual process. Analysts poured over financial statements, compliance records, and market reports for weeks. Today? AI tools like AlphaSense or Clearfind can:

• Highlight inconsistencies in financial data.
• Evaluate the cultural fit of a target company through sentiment analysis of employee reviews.
• Identify hidden risks buried deep in legal contracts.

Sure, humans are still in the loop (and thank goodness for that). But with AI’s assistance, they can focus on asking the right questions rather than drowning in spreadsheets.

Portfolio Management: AI Steps In as a Silent Partner

Managing portfolio companies is where AI truly shines. Whether it’s optimizing supply chains or predicting customer churn, AI tools are invaluable. Let’s break it down:

• Operational Efficiency: Tools like Celonis help firms identify bottlenecks in processes and recommend fixes.
• Customer Insights: Platforms such as Amplitude analyze user behavior, offering actionable insights to drive growth.
• Financial Forecasting: Advanced analytics predict cash flow patterns, enabling better capital allocation.

Rather than relying on guesswork, AI provides clear, logical insights that bring confidence and precision to decision-making. It’s not about magic; it’s about leveraging the power of data and advanced technology to make smarter choices.

Risk Management: Seeing Around Corners

With so many factors impacting whether a decision is the right one or not, risk management is essential. PE forms must ask: which risks will pay off? And which will jeopardize their multimillion-dollar investment? AI helps mitigate risks by monitoring early warning signs, such as:

• Market volatility.
• Shifts in consumer sentiment.
• Competitor movements.

For instance, natural language processing (NLP) tools can analyze public filings and earnings calls for subtle changes in tone or wording — hints that might suggest underlying issues. It’s nuanced work, but AI thrives on nuance.

Challenges and Ethical Quandaries

It’s not all smooth sailing. AI adoption comes with its fair share of challenges:

• Bias in Algorithms: Machines learn from historical data, which can embed existing biases into decision-making.
• Data Privacy: Navigating regulations like GDPR while leveraging data is a tightrope walk.
• Over-Reliance: There’s always a risk of letting machines call the shots instead of human judgment.

Ethics in AI isn’t just a checkbox; it’s an ongoing conversation. How do we ensure fairness? Transparency? Accountability? These questions are as critical as the answers AI provides. As AI continues to evolve, organizations should look to frameworks like the AI Ethics Guidelines by the EU or Partnership on AI for guidance on building responsible, ethical AI systems.

The Road Ahead: What’s Next for AI in Private Equity?

AI isn’t a passing fad; it’s the new normal. The next frontier? Predictive analytics that are so advanced, it anticipates market shifts years ahead. Or perhaps even AI-driven deal negotiations. Imagine bots haggling over terms — a little unnerving, but not impossible.
What’s clear is this: the firms that embrace AI today will set the standards for tomorrow. Those who think they don’t need to think about this, or that it doesn’t make a difference, or that their clients won’t care either way will quickly find they are wrong – and be left in the dust.

Final Thoughts: Humans and Machines, Together

Despite all the buzz around AI, it’s not about replacing humans but augmenting them. Think of AI as a co-pilot, guiding the journey but never taking full control. Private equity is, at its core, a people business. Relationships, trust, and judgment still reign supreme.

With AI in the mix, the possibilities are limitless. Making decisions with access to every possible insight ensures greater confidence and precision. AI doesn’t just help—it helps reduce the pressure, giving CEOs, CFOs, and stakeholders peace of mind.

The next step? If you’re ready to leverage AI to unlock new opportunities and drive smarter decisions in your portfolio, now is the time to start exploring the tools and technologies that can support your journey. Don’t wait for the future; start shaping it today.

Natalie Cramp, Partner at JMAN Group and panel chair, demonstrated the AI Readiness Assessment, a tool for businesses built in collaboration with JMAN, Cyber DueDil, Inference and Pinsent Masons. Using the tool, leaders can plot their readiness to take on the new technology against six key criteria, benchmarking against other businesses of similar size and across different industries and seeing their results in an interactive dashboard and a downloadable report to use within their business.

Natalie explained how going into this kind of detail breaks down the concept of AI from being an all-conquering external force and instead puts the emphasis on individual businesses to be prepared for the technology. This includes the readiness of the people within an organisation to start using it:

“There won’t be one silver bullet, but marginal changes will add up to transformation.”

Echoing the remarks of Lord Holmes, Cramp added that the human dimension was one that should not be neglected: “Sadly I see few Chief People Officers present when I talk to organisations about their data and AI transformation.”

Sam Brown, Managing Partner and Co-Founder at Cyber DueDil, said that the threats of AI need to be thought about in a strategic way:

“At present, people are gung ho – welcoming AI guests into their home without thinking about the full implications. Starting your AI journey has to be done with some degree of caution. Now we are playing catchup with how we educate people about how AI can be used and abused.”

On the subject of threats, Cerys Wyn Davies reminded the room that the law is changing and it is essential for businesses to keep up with the regulations: governance needs to be in place now, because sooner or later there will be a call for accountability. She said:

“The EU is not the gold standard; in France and Germany it is seen as too restrictive and there is fragmentation between countries trying to attract AI talent.”

Richard Davis, CEO and Co-Founder at Inference, added his thoughts on how to make AI work in a practical way within a business:

“Those who are doing well aren’t doing everything, they are investing in lighthouse projects. Set up, deploy and communicate benefits, then develop re-use capabilities. The key is to start small, talk up successes where they happen and stop projects that aren’t delivering.”

Closing the discussion, Natalie Cramp agreed that as leaders, “our job is to both encourage and kill experiments to avoid POC purgatory. And as a general principle, to remember that strategy, data, process and people come first, because technology is the icing – not the cake itself.”

To try the AI Readiness Assessment Tool for yourself, click on the button below.