Private equity general partners (GPs) have evolved their playbooks. Digital transformation is no longer optional or merely a collection of bolt-on capabilities used to gain operational improvements but rather the essential foundation for AI deployment and driving outsized value creation.
In our latest survey, we asked 100 senior PE investors about digital value creation. Nearly 30% say they now integrate digital levers as part of the diligence phase, and an additional 57% say that digital levers are core to value creation planning (VCP). (See Exhibit 1.) Moreover, PE-backed companies that systematically build cutting-edge AI capabilities across functions have nearly twice the return on invested capital as companies that do not.
To deploy AI at scale, companies need strong and current digital platforms such as cloud infrastructure and application programming interfaces (APIs). Digital initiatives alone deliver a 15% to 20% return on investment (ROI), according to a recent IT buyers survey, but when AI is built on these foundations, total returns can reach 30% to 35%. What’s more, time to value accelerates by 40% when companies build AI on mature digital infrastructure rather than attempting to leapfrog foundational capabilities.
Digital initiatives alone deliver a 15% to 20% return on investment, but when AI is built on these foundations, total returns can reach 30% to 35%.
The industry is already racing to capture this opportunity. We found that more than 90% of investment professionals plan to expand portfolio-level digital budgets over the next three years—and one-third expect to do so significantly. With this in mind, we have codified a five-part digital-first, AI-powered playbook, which we will cover in detail later in this article.
Digital Priorities for AI Are Evolving
Despite broad recognition of the importance of strong digital platforms, execution remains challenging. Just 15% of portfolio companies claim “very mature” IT capabilities, while nearly 75% report “moderate maturity,” according to our digital value creation survey. When digital maturity lags or there is underinvestment, 40% of investors say they have experienced a valuation haircut of 5% or more, while only 8% said there was no impact on valuation.
To build a comprehensive foundation, PE teams are channeling digital efforts toward strategic decision support, process automation, and commercial excellence. Decision support systems built today become tomorrow’s AI-powered analytics platforms. Process automation initiatives create the workflows that intelligent automation will eventually enhance. Commercial excellence programs generate the customer data that feeds AI-driven personalization and prediction.
For example, enterprise resource planning (ERP) and customer relationship management (CRM) systems are being given greater priority. In the past, GPs considered these to be merely back-office necessities. But firms now recognize that ERP and CRM provide the structured data that is essential for AI.
It’s helpful that vendors are consolidating around this digital-to-AI continuum. Cloud providers, once focused purely on infrastructure, now offer comprehensive AI platforms. ERP vendors embed AI capabilities directly into core workflows. Even specialized digital boutiques are evolving their offerings to bridge the digital-to-AI gap.
Why Digital-to-AI Transformation Proves So Difficult
The boardroom, not the server room, emerges as the primary bottleneck to the digital-to-AI transformation. An overwhelming 90% of respondents cite competing priorities as the top blocker to digital transformation, while 76% point to unclear ROI. (See Exhibit 2.) Without a quantified value case that explicitly connects digital investments to AI potential and ultimately to exit multiples, digital initiatives fight for airtime against every other value creation workstream.
The measurement gap compounds these challenges. While 82% of firms track ROI and 72% track cost savings from digital initiatives, only 11% explicitly link digital progress to exit narratives, and a mere 40% use formal digital-maturity scores. This measurement weakness becomes critical when trying to justify ongoing digital investment to enable AI deployment.
Adding to the complexity, the vendor landscape continues to evolve at breakneck speed. GenAI capabilities that didn’t exist 18 months ago now dominate technology roadmaps. Portfolio companies find themselves caught between the imperative to modernize and the risk of investing in soon-to-be-obsolete platforms, which might lock them out of AI opportunities. The design must have AI evolution in mind from the start.
Critical Factors for the Digital-to-AI Journey
The most successful transformations assign C-level executives ownership of the entire digital-to-AI journey, ensuring the transformation is managed as a continuous journey rather than as distinct programs. By using integrated roadmaps that show the evolution from digital foundations through AI experimentation to scaled deployment, C-level executives can make budget decisions that further AI readiness and help portfolio companies maintain momentum toward AI implementation.
It’s hard to overstate the importance of executive ownership of the digital-to-AI journey—bolstered by accountability and compensation mechanisms—to elevate these initiatives from side projects to strategic imperatives. After all, technical teams can build digital infrastructure, but only business leaders can identify the high-value AI use cases that justify continued investment. Business leaders are also the ones most qualified to put talent strategies in place to capitalize on digital investments. These talent strategies should include broad digital literacy for all employees, deep digital expertise in IT and operations teams, and specialized AI capability in targeted value creation areas.
The Five-Part Playbook
Private equity leaders need to treat digital transformation as the essential foundation for AI deployment and value creation. Our digital value creation survey pinpoints five moves that separate the leaders from the laggards.
Anchor digital excellence with AI potential in the investment thesis. Investment teams that combine digital and commercial diligence to size both IT prerequisites and AI upside avoid the capital expenditure surprises that plague late digital adopters. We found that 29% of firms integrate digital value creation in the pre-deal phase. Before a deal is signed, they hand lenders and investment committees a comprehensive day-zero roadmap that prices both digital transformation costs and AI opportunities into valuations. This integrated approach fundamentally changes deal dynamics.
Private equity leaders need to treat digital transformation as the essential foundation for AI deployment and value creation.
We found that 73% of firms run digital due diligence on most deals, and those that use an AI lens to assess digital foundations often find instances where modest digital investments can unlock significant AI opportunities. But our research also shows that most PE firms are not doing enough to avoid deals where the digital transformation required for AI readiness would consume excessive capital and management attention. Only 22% said that a company’s digital readiness influences go/no-go decisions.
Execute day-one digital sprints that build toward AI. The most successful firms launch 100-day digital squads immediately post acquisition that explicitly build toward AI endpoints. Rather than pursuing digital for digital’s sake, they sequence initiatives to create data flows, establish governance, and build the integration capabilities that AI will eventually require. Quick wins such as data-driven cross-selling and dynamic pricing come first, generating both immediate revenue and rich data sets for future machine-learning applications.
The digital plan needs to be a seamless part of the overall value creation strategy, interlocked with all business objectives and initiatives from the start—not as a later phase in a waterfall approach. This reflects a fundamental insight from our research: digital integration that starts within value creation planning delivers stronger results than reactive, post-close initiatives. PE investors say that a digital transformation’s most substantial impact is on business steering (61%) and customer experience (53%). (See Exhibit 3.) These operational improvements are the ambition that drives the management, infrastructure, and customer data foundations that make AI deployment not just possible but profitable.
Modernize core systems as the digital backbone for AI. As noted earlier, the surge in ERP and CRM prioritization—up by as much as five percentage points in our survey—reflects growing recognition that these systems provide more than operational efficiency. They create the structured data foundation that is essential for AI. (See Exhibit 4.) Forward-thinking PE firms now evaluate ERP and CRM vendors not just on current functionality but also on AI readiness: API architectures, data standardization capabilities, and embedded AI features.
The sequencing matters enormously. Firms that modernize core systems first report 40% faster AI deployment and significantly higher ROI. The reason is clear: AI algorithms require clean, structured, accessible data. Legacy systems, with their data silos, inconsistent formats, and limited integration capabilities, become insurmountable barriers to AI adoption. By prioritizing core system modernization in the first year post acquisition, PE firms create the digital backbone that makes everything else possible.
Blend digital expertise with AI specialists. The talent equation has evolved significantly. Portfolio companies with only moderate in-house technical maturity—and that describes 74% of the market—can leverage external partners to achieve outcomes that are on par with digitally advanced peers. The key lies in the blend: maintaining lean internal digital cores while spiking capability through specialized boutiques and strategy houses for specific initiatives. Our research reveals that 70% of successful firms tap specialized digital boutiques, while 59% engage strategy firms for transformation planning.
But here’s another critical finding: only 45% of successful firms systematically ensure knowledge transfer from external partners to internal teams. That knowledge transfer gap means many firms remain perpetually dependent on external support, unable to build the institutional capability needed for sustained digital and AI leadership. The most successful firms treat every external engagement as a capability-building opportunity, creating formal knowledge transfer targets and embedding internal team members in consultant-led initiatives.
Measure digital progress while building the AI story. Running a digital profit and loss statement that connects these investments to AI potential and exit narratives transforms how portfolio companies prioritize initiatives. Sponsors that benchmark digital maturity and link initiatives to business performance endure fewer exit discounts than their peers.
While ROI and cost savings remain important KPIs, forward-looking metrics such as data readiness scores, API coverage percentages, and AI pilot success rates are vital. These leading indicators predict which portfolio companies will successfully transition from digital foundations to AI leadership. Equally important, they provide the quantified narrative that exit buyers increasingly demand to demonstrate future AI potential, not just current digital capabilities.
The First 18 Months: A Critical Window
The path forward requires careful sequencing to build the digital foundation and accelerate AI. Over the first 6 to 12 months, portfolio companies need to complete core system modernization initiatives, establish enterprise-wide data governance, achieve cloud migration targets, and build comprehensive API and integration layers.
The subsequent 12 months can then include AI deployment on those mature digital foundations. Successful pilots will scale across digital infrastructure, using ROI from combined digital-AI initiatives to validate continued investment. This will allow portfolio companies to position themselves as digitally enabled AI leaders, commanding premium multiples from strategic acquirers who recognize the value of integrated digital-AI capabilities.
The message from our research is unequivocal: digital transformation isn’t being replaced by AI–it’s AI’s essential prerequisite. Portfolio companies must build the digital core first, expressly considering AI requirements from the start. Then they can layer AI capabilities onto mature digital foundations and continually measure progress holistically to connect digital investments to AI deployment and then to value creation at exit.
The firms that execute this integrated approach won’t just achieve incremental improvements. They’ll fundamentally transform portfolio company capabilities and command premium valuations.
