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 world of data and AI, legal can’t be an after-the-fact function. As JMAN scales at the intersection of private capital and technical innovation, General Counsel, Sara Mackie is transforming legal from a fragmented effort into a robust architectural discipline.

In a Corporate Counsel Business Journal blog, Sara explains how she has built a legal function designed for velocity, accountability and durability, from managing cross-border data responsibilities to navigating the evolving landscape of AI regulation.

In this blog, you will learn:

• Legal as Architecture, Not Just Policy: In high-growth, tech-driven environments, the legal function must be “built” to scale alongside engineering and consulting teams.
• Velocity through Governance: Creating clear internal frameworks for legal means firms move faster with durable processes.
• AI Pragmatism: Human judgment and robust governance must keep pace with technical innovation to ensure accountability.

Click below to read the full blog.

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. 

After two years of difficult market conditions, PE exit activity is recovering, but the era of selling based purely on headline growth is over. Today, data underpins every credible value story, yet according to a recent study by EY, 72% of respondents cited access to comprehensive data and KPIs as their biggest exit challenge. 

In a must-read article for Funds Europe, Anush Newman, CEO and Co-founder of JMAN Group, outlines the cultural and infrastructure shift required to treat data as a strategic management asset, proving the long-term viability that turns cautious buyers into confident investors. 

In this article, you will learn: 

• Which granular metrics (such as CAC:LTV and support resolution times) are necessary to bring operational efficiency to life. 
• How to prove sustainable growth potential through customer cohort performance and cost-to-serve ratios. 
• Why building a robust data foundation is essential for integrating AI and securing a higher valuation post-acquisition. 

The path to a successful exit is increasingly paved with high-quality data – according to The Private Equity Exit Readiness Study by EY, highlighted in a recent Pitchbook article. Data now plays a critical role in meeting heightened buyer due diligence standards and securing favorable outcomes.

The article, featuring insights from industry leaders including Anush Newman, CEO and co-founder of JMAN Group, unpicks the reasons behind data-related hurdles and offers strategic guidance for private equity firms. Newman emphasizes that investors’ expectations for data have never been higher, as has become essential fuel for both business growth and the equity story.

Key Learning Outcomes:

• Why a robust, standardized dataset is the biggest challenge and most crucial factor for efficient PE exits.
• Why firms must evolve their perception of data, integrating it into value creation strategies rather than solely as an IT function.
• The importance of preparing your data early, aligning it with your “equity story” and engaging your management team in the process.

If you are ready to transform your PE exit strategy, click through to read the full Pitchbook article and gain deeper insights into leveraging data for success.

If data is king, why are so many PE firms overlooking the role that data engineers can play in pursuit of value?
Data engineers are not just technical support, pipeline builders, data cleaners and report writers: they have the potential to unlock the immense value hidden within vast data sets – if firms are ready to change their perspective.

A new article in Tech Monitor, by Leo Valan, Chief Technology Officer at JMAN Group, challenges conventional wisdom and argues that when data engineers are treated as commercial partners rather than just backend technicians, they possess the unique ability to uncover deep insights, bridge critical data gaps and directly drive value.

The most successful firms are already making space for their engineers to contribute to commercial thinking and this article can help you join them. You will learn:

• Why private equity firms must elevate data engineers to strategic, commercial partners to fully leverage their data assets.
• How direct involvement of data engineers in commercial insights, diligence and performance reviews leads to higher quality analysis, earlier issue detection and more confident, informed investment decisions.
• Why firms must foster an environment where curiosity and an open mind are as crucial as technical skills for driving innovation.

If you are ready to transform your firm’s approach to data and talent, click here to read the full article on Tech Monitor.