Executive Summary

Madison Dearborn Partners has built one of the most respected track records in private equity over more than three decades—approximately $36 billion in aggregate capital raised, more than 160 platform investments, and a sector focus on Financial Services, Healthcare, and Technology & Government that is increasingly synonymous with the industries where AI is generating the most measurable value. The firm's single-office culture, deeply experienced team, and operational partnership model position it well to build a differentiated AI strategy. The question is not whether to act, but how to do so with the discipline and conviction that defines MDP's investment approach.

The private equity industry is at an inflection point. According to EY's November 2025 analysis, 50% of PE professionals believe generative AI and agentic AI will have the most transformative impact on the industry within three years. Competitors including Blackstone, KKR, EQT, and Thoma Bravo have established multi-year head starts. Industry benchmarks now show 35–85% productivity gains in due diligence workflows and 15–30% cost reductions in targeted portfolio company operations when AI is applied with rigor. The window for competitive differentiation is real, but it is narrowing.

Key Findings

• Deal sourcing coverage is structurally limited without AI. Traditional methods capture an estimated 16–18% of relevant deal opportunities. AI-powered market intelligence platforms can substantially expand that coverage, enabling more selective, higher-conviction investment decisions across MDP's three sector verticals.
• Due diligence productivity gains of 35–85% are achievable. AI applied to document review, financial modeling, contract analysis, and competitive assessment compresses evaluation timelines from weeks to days—freeing deal teams to focus on the strategic judgment that differentiates MDP.
• Portfolio company AI deployment drives direct EBITDA impact. Across Financial Services, Healthcare, and Technology & Government, targeted AI applications are generating 15–30% cost reductions in specific processes, improving margins and strengthening exit multiples during a period when average hold periods are lengthening.
• MDP's portfolio is a natural AI deployment network. Technology & Government holdings—T2S Solutions, Harmonia Holdings, Omni Federal—are already operating at the frontier of AI application. Proven use cases from these companies can be adapted and deployed across the broader portfolio, creating a repeatable value creation playbook.
• Governance and data readiness are the differentiating foundations. Over half of U.S. PE firms expect regulatory restrictions on AI within 12–18 months. Firms that build structured governance frameworks now will outpace those managing scattered pilots—and will be better positioned with LP audiences that increasingly expect demonstrated AI literacy.
• Competitor urgency is high. EQT's Motherbrain, Blackstone's internal screening platform, and Thoma Bravo's AI-first operating model are not hypothetical advantages—they are compounding data assets. MDP's mid-market competitive set remains largely in exploratory phases, creating a meaningful window to establish sector-level leadership.

Competitive Analysis

MDP operates within a competitive environment spanning mega-cap PE firms with dedicated AI budgets and specialized mid-market peers competing directly for deal flow. Understanding where competitors stand—and where they are headed—is essential context for building MDP's own AI thesis.

Three Tiers of AI Adoption

The competitive picture resolves into three distinct tiers with direct implications for MDP's positioning:

• Tier 1 — AI-Native Operators: EQT and Blackstone have built proprietary platforms deeply embedded in daily investment operations. Their multi-year data advantages compound over time. Thoma Bravo extends this model to systematically deploying AI across every portfolio company they own.
• Tier 2 — Active Adopters: KKR, TPG, and Carlyle are making significant technology investments with dedicated team capacity across deal sourcing, competitive intelligence, and LP management—capabilities that are meaningful but still less integrated than Tier 1 platforms.
• Tier 3 — Exploratory: Many mid-market PE firms—MDP's most direct competitive set—remain in early stages, using off-the-shelf tools for basic automation. This is where the near-term opportunity is sharpest for MDP.

MDP has a credible path to establish AI leadership within the mid-market while closing the capability gap with larger firms. MDP's deep sector expertise in exactly the industries where AI is most active—government technology, health IT, financial services—concentrates the institutional knowledge that AI systems can most effectively build on.

Portfolio Company Applications

This section is the operational core of this brief, addressed directly to operating partners and portfolio company leadership at MDP: where does AI generate measurable value across your holdings, which use cases should you prioritize, and what does repeatable deployment look like across a diversified portfolio?

The organizing principle is the portfolio playbook. Individual pilots at individual companies have limited strategic value. What matters is identifying AI applications that work in one company and can be adapted and deployed across the next—creating a compounding capability that makes every holding stronger as the portfolio AI knowledge base grows.

Technology & Government

MDP's Technology & Government portfolio—T2S Solutions, Harmonia Holdings, AEVEX Aerospace, and Omni Federal—operates in a sector where AI is not a future trend but a current mission requirement. Government agency clients are mandating AI capabilities in contract competitions. This portfolio is MDP's natural AI laboratory, and the use cases developed here are directly transferable to technology-enabled businesses across the other two sector verticals.

Product Enhancement & Contract Competitiveness

For all four portfolio companies, AI capabilities embedded in product offerings directly affect the ability to win and retain government contracts. Harmonia Holdings has explicitly positioned digital transformation for the U.S. Federal Government as its core strategy; T2S Solutions lists AI/ML as a priority mission area. Both companies should be accelerating the integration of AI into their core delivery capabilities—not as a roadmap item, but as a competitive requirement.

Specific applications include automated anomaly detection in mission-critical data streams, AI-assisted software testing and quality assurance that reduces delivery cycle time, and predictive analytics for operational readiness. These capabilities improve both the quality of government deliverables and the cost structure of delivering them—a direct margin improvement story.

Estimated EBITDA impact: 10–20% improvement in delivery margins through AI-assisted development and QA workflows, based on benchmarks from comparable government IT firms that have implemented these capabilities.

Acquisition Target Identification

T2S Solutions has already demonstrated an acquisition-oriented growth strategy, adding Blue Marble Communications and Flexitech Aerospace. AI-powered market mapping—analyzing government contract award data, NAICS code adjacencies, teaming arrangements, and capability statements—can systematically identify bolt-on acquisition targets faster and more comprehensively than manual research.

This is a repeatable playbook: the same AI-driven market mapping methodology used to surface add-on targets for T2S can be adapted for Harmonia and Omni Federal, and eventually for platform company identification in adjacent government technology sub-sectors.

Operational Efficiency in Program Delivery

Government IT firms manage significant program management overhead—status reporting, compliance documentation, deliverable tracking, and contract modification processing. AI applied to these administrative workflows can reduce the time senior professionals spend on reporting by 30–50%, redirecting capacity toward billable work and business development. For firms where program manager utilization is a key profitability driver, this is a direct EBITDA lever.

Healthcare

NextGen Healthcare represents MDP's primary healthcare holding and one of the most strategically significant AI opportunities in the portfolio. As a leading provider of electronic health records and practice management software for ambulatory care, NextGen sits at the intersection of clinical workflow and the data infrastructure that makes AI-driven healthcare improvements possible. The ambulatory care market is early in its AI adoption curve—which means the competitive value of moving now is material.

AI-Enhanced Clinical Decision Support

The most impactful near-term AI application for NextGen is embedding clinical decision support directly into the EHR workflow. AI models trained on clinical documentation can flag potential drug interactions, suggest diagnostic codes, surface relevant clinical guidelines, and identify patients due for preventive care—all within the physician's existing workflow. This reduces documentation burden (a top driver of physician burnout and practice attrition) while improving care quality.

For NextGen, this is both a product enhancement story and a customer retention story. Practices using AI-enhanced workflows demonstrate measurably better clinical and operational outcomes. Thoma Bravo's experience with NLP contract analysis—which uncovered revenue opportunities leading to 15% customer retention improvements—is instructive here. The mechanism is different (clinical vs. commercial), but the value creation pattern is the same: AI surfaces what manual review misses, at a scale that changes the economic outcome.

Revenue Cycle Optimization

Medical billing is a highly structured, rules-intensive process where AI demonstrates consistent, measurable improvement. Automated coding accuracy, denial prediction and prevention, eligibility verification, and claims scrubbing before submission can meaningfully reduce the denial rate and accelerate the collection cycle for healthcare practices using NextGen's revenue cycle management capabilities.

The financial impact for practice customers is direct and quantifiable: a 1–2% improvement in clean claim rate can represent meaningful revenue recovery for a multi-physician practice. For NextGen, this translates into increased adoption of revenue cycle management modules—a higher-margin revenue stream than core EHR licensing.

Population Health & Patient Engagement

AI-driven patient outreach and population health analytics extend NextGen's value proposition beyond the point-of-care workflow. Predictive models can identify high-risk patients due for outreach, optimize appointment scheduling to reduce no-show rates, and generate personalized care gap communications. For practices operating under value-based care arrangements, these capabilities directly affect quality metrics and reimbursement.

Financial Services

MDP's financial services portfolio—with Wealthspire Advisors, Fiducient Advisors, and Newport Private Wealth forming a combined wealth management platform with over $90 billion in client assets—creates one of the most compelling AI value creation opportunities in the portfolio. Wealth management is a sector where AI is already demonstrating measurable commercial impact, and the platform scale MDP has assembled creates natural economies in AI deployment.

Client Acquisition & Advisor Productivity

AI-driven prospect identification and lead scoring can systematically surface high-probability client acquisition opportunities for wealth advisors—identifying individuals with recent liquidity events (business sales, inheritances, executive compensation) or underserved segments within the addressable market. For a platform with hundreds of advisors, improving prospect identification efficiency by even a moderate amount has compounding revenue impact.

On the productivity side, AI applied to client reporting, portfolio commentary generation, and meeting preparation reduces the administrative burden that limits advisor capacity. Advisors who spend less time on documentation spend more time with clients—the core driver of both retention and referral-based growth in wealth management.

Investment Operations & Compliance

Portfolio rebalancing, tax-loss harvesting, and compliance monitoring are highly systematic processes well-suited to AI automation. For a platform at this scale, automating these workflows reduces operational headcount requirements and eliminates the error risk inherent in manual processes—both margin improvement and risk reduction in a single deployment.

AI-driven monitoring of client communications for suitability issues, automated KYC/AML workflows, and real-time surveillance of portfolio positions against client investment policy statements can reduce compliance staff requirements while improving the consistency and defensibility of the compliance program.

Cross-Platform AI Deployment Opportunity

The wealth management platform structure creates an opportunity that a single-entity firm would not have: deploying AI tools once and realizing the benefit across multiple entities simultaneously. AI vendor contracts negotiated at the platform level—covering Wealthspire, Fiducient, and Newport Private Wealth together—create pricing advantages and reduce implementation risk. This is the portfolio playbook in action.

Repeatable Patterns Across the Portfolio

The operating partner implication is direct: when an AI use case proves out at one portfolio company, the deployment cost and risk of replicating it at the next company in the same pattern category drops substantially. Building this institutional learning infrastructure—a Portfolio AI Center of Excellence—is the mechanism that turns individual company wins into portfolio-wide value creation.

Internal PE Firm Applications

Beyond portfolio company value creation, AI applies directly to how MDP operates as a firm. The investment lifecycle—from deal origination through diligence, portfolio management, and LP relations—has historically been labor-intensive and data-fragmented. AI creates meaningful leverage at each stage, and the cumulative effect on firm productivity and analytical quality is significant.

Deal Sourcing & Screening

Traditional PE deal sourcing captures an estimated 16–18% of relevant opportunities in a target market. The gap is not effort—it is the structural inability of manual processes to monitor millions of data signals simultaneously. AI-powered sourcing platforms address this directly by scanning company websites, news sources, financial databases, patent filings, hiring pattern shifts, and digital signals to surface opportunities that match MDP's investment criteria before they reach a broadly competitive process.

The recommended approach is deploying a combination of relationship intelligence platforms (Affinity or DealCloud) and sector-specific AI screening tools across all three verticals, integrated with MDP's existing CRM infrastructure to enrich contact and deal data automatically, map warm introduction paths, and score opportunities against historical investment parameters.

The sector specificity matters. Government technology contract awards are public data—AI that monitors USASpending.gov, SAM.gov, and FPDS in real time can identify companies with revenue inflection points or capability expansions that signal acquisition readiness before traditional deal flow sources surface them. In healthcare, regulatory filing activity, reimbursement changes, and physician practice consolidation patterns are similarly monitorable at scale. In financial services, RIA registration data, AUM growth trajectories, and advisor headcount changes tell a consistent story about platform acquisition candidates.

Expected impact: 40% or greater increase in centralized opportunity tracking, with materially faster identification of proprietary deal flow and early-stage sector signals that give MDP a timing advantage in competitive processes.

Due Diligence

Due diligence is where AI delivers the most immediately measurable productivity gains. EY's 2025 benchmarks demonstrate 35–85% productivity improvements when AI is applied to document review, financial analysis, competitive assessment, and management interview preparation—with the highest gains concentrated in the document-intensive portions of the process.

MDP's recent addition of Todd Novak as Managing Director and Head of Portfolio Finance strengthens the analytical foundation for AI-augmented diligence. Practical applications include AI-powered CIM and data room analysis that extracts key financial metrics, flags earnings quality anomalies, and summarizes competitive positioning within hours rather than days; contract analysis using natural language processing to identify customer concentration risks, renewal exposure, and revenue quality signals across thousands of documents; and automated drafting of investment memorandum sections based on structured data extraction from diligence materials.

Thoma Bravo's documented experience provides a useful benchmark: their use of NLP to analyze over 50,000 customer contracts during diligence uncovered revenue improvement opportunities that led to 15% better customer retention outcomes post-acquisition. The value was not in automation for its own sake—it was in surfacing insights at a scale and speed that human review cannot match.

Governance requirement: all AI outputs in decision-critical workflows require mandatory human review. The goal is augmenting the judgment of MDP's deal teams, not replacing it.

Portfolio Monitoring & Early Warning

As average PE holding periods extend—BDO's 2025 survey reports 84% of fund managers experiencing longer holds—the value of continuous, real-time portfolio monitoring increases proportionally. Traditional quarterly reporting cycles create blind spots: issues that were manageable in month two become material in month five, and the information arrives at month three.

An AI-driven portfolio monitoring platform changes this dynamic fundamentally. By standardizing reporting formats across portfolio companies and automatically aggregating financial and operational KPIs into a continuous dashboard, MDP's deal teams and operating partners gain real-time visibility into performance trends. Predictive models can flag deviations—revenue run-rate changes, margin compression, working capital deterioration—before they become issues requiring reactive intervention.

External data integration is equally important. Monitoring competitor actions, regulatory changes, customer sentiment, and market trends against each portfolio company's performance context gives operating partners the context they need to distinguish company-specific issues from market-driven headwinds—the difference between reactive portfolio management and proactive value creation.

Value Creation Planning & Fund Operations

At acquisition, AI can significantly accelerate the identification of operational improvement opportunities that anchor the value creation plan. Benchmarking a newly acquired company against operational metrics from similar businesses in MDP's portfolio and the broader market provides an immediate evidence base for prioritizing improvement efforts. Over the hold period, AI tracking of value creation plan milestones provides accountability and early warning of plan deviations.

For fund operations, AI addresses a persistent inefficiency: LP reporting and investor communications. Leading PE firms including Ardian have deployed AI tools that generate individualized LP status reports, automatically surface relevant portfolio developments for specific investor interests, and maintain the consistency of communications at scale. For MDP—managing relationships across a diverse global LP base—personalized, timely LP engagement is a meaningful competitive factor in Fund IX fundraising and beyond.

Knowledge management represents a longer-term but strategically significant opportunity. MDP's 30+ year history of investment decisions, market analyses, and operational playbooks represents an institutional knowledge asset currently locked in documents and the memories of long-tenured professionals. Large language models deployed over this knowledge base can make decades of accumulated expertise instantly accessible to all team members—particularly valuable during the current leadership transition.

AI Advisory Services

The findings in this brief reflect what is possible when AI strategy is approached with rigor, sector depth, and a portfolio-wide lens. Compoze Labs works with PE firms and their portfolio companies to build that foundation—not through scattered pilots, but through structured programs that connect AI investment to value creation outcomes.

Three Outcomes, Framed for PE

Conclusion

Madison Dearborn Partners enters this AI moment with meaningful structural advantages: sector depth in three of the industries where AI is generating the most measurable commercial value, a portfolio increasingly weighted toward technology-enabled businesses, a collaborative single-office culture that reduces change management friction, and a team with the institutional knowledge base that makes AI systems most effective.

The competitive picture is clear. EQT's Motherbrain, Blackstone's internal screening platform, and Thoma Bravo's AI-first operating model represent multi-year head starts that are compounding. Within MDP's mid-market competitive set, most firms are still in exploratory phases. That gap is an opportunity—but it has a shelf life.

The recommended path forward rests on five priorities:

• Deploy AI-enhanced deal sourcing across all three sector verticals to expand market coverage and improve investment selection quality within 12 months.
• Implement AI-powered due diligence workflows to compress evaluation timelines and improve analytical depth—freeing MDP's deal teams to focus on the strategic judgment that defines the firm.
• Establish a Portfolio AI Center of Excellence to systematically deploy proven AI use cases across portfolio companies, creating measurable EBITDA impact and multiple expansion that compounds through the hold period.
• Modernize LP engagement through AI-driven personalization and real-time portfolio reporting, strengthening the relationships that will define Fund IX fundraising success.
• Build an AI governance framework that manages risk proactively across MDP's diverse regulatory environment while positioning the firm as a credible, responsible AI leader with LPs and portfolio company management teams.

The foundations built today—data infrastructure, governance frameworks, tested use cases, and team fluency—are what separate firms that capture AI-driven value from those that fund scattered experiments. For MDP, with its track record, sector expertise, and long-tenured team, the conditions for doing this well are already in place.

© 2026 Compoze Labs. Confidential. Prepared exclusively for Madison Dearborn Partners.