The private equity landscape is shifting from backward-looking financial modeling to real-time, predictive intelligence. A recent article in Forbes features Christian Davis (Partner at JMAN Group) as an expert witness on the strategic necessity of embedding AI across the entire investment lifecycle. From deal sourcing to exit planning, the integration of machine learning and natural language processing is cemented as a competitive requirement for the next generation of industry leadership.

In this article you will:

• Discover how predictive analytics models thousands of real-world variables to uncover patterns invisible to traditional analysis.
• Learn how live risk dashboards allow investors to dynamically assess exposure as macroeconomic and geopolitical conditions evolve.
• Understand why data integrity and ethical frameworks are the essential foundations for trusting AI-driven outputs.

Click below to read the full Forbes article.

The era of manual processes in private equity is over. In an exclusive feature for FinAI News, Molly Buxton, Principal at JMAN Group, joins other industry leaders to discuss the transformative power of AI in the sector. From automating manufacturing operations to accelerating due diligence, the deployment of specialized AI teams is the new standard for driving bottom-line growth.

Over recent months, the strategic use of partnerships and internal data teams has created a massive divide between those who embrace automation and those who remain tethered to traditional 100-day plans.

In this article, you will:

• Learn how AI experts are uncovering efficiencies in portfolio company pricing, supply chains and customer churn.
• Gain insights into using AI for deal sourcing and scanning for regulatory “red flags” during diligence.
• Find out why structured analytical and drafting tasks are seeing productivity boosts of 10% to 40%.

To read the full article, click below.

Driven by the rapidly-growing presence of JMAN Group in the US, industry publication Megabuyte caught up with Anush Newman and Richard Cowen, CEO and CFO respectively of JMAN Group, for an inside look at a landmark year.

In this exclusive update, the author ​​Jonty Lagden explores how the business has achieved a string of successes. Hitting a £30.1m revenue milestone, JMAN Group sits in the top quartiles for Organic Growth, Revenue CAGR, and EBITDA margin, reporting a Megabuyte Scorecard rating of 61, against an IT Consulting peer group average of 47.

As Anush and Richard told Megabuyte, the growth is thanks to JMAN Group’s unique blended delivery model and deep-rooted partnerships within the Private Equity sector. This has enabled the company to meet the continued demand for data engineering, analytics and AI delivery across PE-backed portfolio companies. 

With a global team now at more than 600 experts across London, New York and Chennai, plus investment from Baird Capital, JMAN is outperforming peer group averages and setting a high bar for data engineering and machine learning consultancy.

The article also covers: 

• How a vendor-agnostic approach to data and AI is driving 35% like-for-like revenue growth.
• Insights into how JMAN Group maintains top-quartile margins while expanding its international footprint.
• Why long-term engagements with portfolio companies are the engine behind a projected £40m+ turnover for FY26.

To read the full article, click the link below.

In the latest feature for PitchBook News, experts across private equity argue that 2026 is the year of “necessity and survival” for artificial intelligence in the industry.

While many firms remain in the early stages of exploration, Anush Newman, CEO and founder at JMAN Group, highlights the urgent need for GPs to move beyond concept and into consistent, value-driven execution. From supporting portfolio companies to securing future reinvestments from LPs, this article outlines the essential roadmap for the year ahead.

Key learning outcomes:

• Portfolio foundations: Why GPs must be more insistent and consistent in helping portfolio companies lay the groundwork for data and AI.
• The cost of inaction: Understanding why delaying AI investment will erode firm value much faster than the upfront cost of experimentation.
• LP expectations: How transparency, speed and accuracy in data reporting are becoming as critical as financial returns for modern allocators.

To read the full article, click below.

The “triple-whammy” of high debt, energy costs and wage inflation is ending the era of low-productivity “zombie” firms. Featured in The Times, Anush Newman, co-founder and chief executive officer of JMAN Group, explains why data is now the ultimate tool for “creative destruction” and sustainable growth.

Whether you are navigating a new acquisition, seeking operational efficiency, or preparing for a high-stakes exit, this article outlines the roadmap for commercial success in a tougher landscape.

Key learning outcomes:

• Bridging the data gap: Why many firms struggle to replicate “shiny” investment figures post-acquisition and how to fix it.
• Driving organic growth: Using AI and data signals to acquire the right customers and reduce churn.
• Mastering the exit: How to use data-led storytelling to prove your success was deliberate to skeptical buyers.

Click below to read the full article.

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. 

For busy executives, the world of data can feel overwhelming. It often gets pushed down the priority list in favor of immediate sales and operational needs. However, the longer you wait, the more daunting it becomes.

But what if you could take simple, strategic steps today to get ahead?

In our new white paper, Vanessa Macalliver, Associate Partner at JMAN outlines a proactive, do-it-yourself approach to data strategy. You can deliver real impact fast, even with limited resources. This guide is for executives who know they can’t afford to ignore data but aren’t sure where to start.

Read this paper to learn:

• How to define a data vision that is business-led, not data-led, by aligning your executive team.
• How to mobilize your existing internal team to find quick wins and identify future stars without rushing to hire.
• How to translate your vision into a prioritizsed list of opportunities based on business impact, setting the stage for sustainable growth.

Complete the form to get your copy today.

In wealth management, firms are no longer just collecting data; they are using sophisticated AI and machine learning to analyze it, creating powerful predictive insights to guide an advisor’s ‘next-best-action’. 

In a new article from Citywire USA, Christian Davis, Associate Partner at JMAN Group, explains the reality behind the buzz. He details how bespoke ‘churn tools’ and predictive models are becoming essential for staying competitive, anticipating client needs and personalizing service at scale.

To stay ahead, Christian says, firms must build a nuanced data strategy. 

In this article you will learn: 

• Why a robust data strategy is the essential first step before you can effectively deploy AI and machine learning. 
• How predictive analytics can identify a client’s propensity to churn, accept a new fee structure or be interested in a cross-sell opportunity.
• The reason a one-size-fits-all AI product doesn’t exist for this challenge and why a bespoke approach is critical for success. 

Click below to read the full article.

Today’s private equity landscape is dealing with the challenges of geopolitical uncertainty, economic caution and longer holding periods. Simply showcasing impressive top-line growth is no longer sufficient: the most critical asset for businesses seeking a successful and lucrative exit is data. 

In an exclusive article for WealthBriefing, Anush Newman, CEO and co-founder at JMAN Group, outlines how investors are scrutinizing a much wider range of metrics to build a holistic picture of a company’s performance, from customer loyalty to operational efficiency. This shift requires a cultural change within businesses, where data is viewed as a strategic priority, not just an IT or finance function. 

In this article, you will discover: 

• Why the traditional “data cube” has evolved into a scalable “data asset” that works long after acquisition. 
• The three key pillars a firm must demonstrate through data to build a compelling value story: operational efficiency, scalability, and long-term viability. 
• How businesses can leverage their existing data to not only secure a successful exit but also to capitalize on the value that AI can bring. 

Click below to read the full article and transform your approach to data.