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Beyond the hype: Unlocking mid-market value through AI
5-MINUTE READ
November 3, 2025
Mid-market deal making is gaining traction among larger private equity (PE) firms. We’ve seen multiple global players raise multi-billion-dollar funds in 2025 that are targeted at the mid-market segment. In the quest to actively transform these portfolio companies and drive outsized returns, artificial intelligence (AI) is clearly becoming a game-changer.
By lowering barriers to large-scale automation and innovation, AI is making advanced capabilities accessible to mid-sized firms that once lacked the scale to implement them. Reusable AI modules, no-code interfaces and industry-specific models are accelerating adoption. In Europe, AI usage among mid-sized businesses grew by 60% year-over-year.1
This creates both urgency and opportunity for pioneering mid-market portfolio companies. Agile mid-market firms that adopt AI early can move faster than larger competitors and realize transformation benefits sooner. But without the right foundations, many portfolio companies risk lagging behind.
Barriers to AI adoption in mid-sized firms
Despite its promise, AI adoption among mid-market firms still trails larger companies. Several structural barriers slow the path to value creation:
- Digital maturity gaps: Most small and mid-sized enterprises (SMEs) in Europe report very low (42%) or low (34%) adoption of key digital technologies.2 Without solid digital foundations, scaling AI is difficult.
- Readiness and trust deficits: Two-thirds of employees report AI is advancing faster than their organization can train and prepare the workforce, revealing a clear readiness gap. Beyond this, there is both a trust deficit and a communication gap. Only a third of employees trust their employer to act in their best interest when introducing new technologies. An equal percentage say leadership has clearly explained how AI agents will impact roles and required skills.
- Fragmented governance: Mid-sized firms are more likely to pursue opportunistic pilots without structured governance. This often results in fragmented, duplicated or non-ROI-driven initiatives.
- Bandwidth constraints: Unlike large enterprises with dedicated AI centers of excellence, mid-market firms must juggle transformation with day-to-day operations. This makes it harder to prioritize disruptive innovation over “business as usual.”
For PE investors, these challenges highlight the importance of disciplined AI adoption—anchoring investments in ROI while managing organizational readiness.
Where AI moves the needle most
Our analysis of close to 40 portfolio companies reveals that while AI experimentation is broad, real value capture clusters around specific sectors and capabilities.
GenAI-enabled fertility diagnostics improved embryo prediction accuracy, enhancing both patient outcomes and lab efficiency. AI also streamlines scheduling, transcription and claims management—unlocking new revenue models and reducing costs.
AI-driven routing engines cut cost-to-serve by up to 20% while predictive forecasting improves customer retention and pricing accuracy.3 Warehouse automation accelerates delivery speed and reduces operating costs.
Hyper-personalized campaigns and AI-generated creative assets increase conversion and retention, while reducing manual campaign costs. Dynamic creative optimization allows thousands of ad permutations at scale, driving measurable ROI uplift.
Across these industries, the capabilities that matter most are consistent: strong data foundations, AI-enabled product differentiation through personalized engagement and targeted marketing, back-office automation and—in tech firms—coding and IT. Nearly 85% of use cases fall into either product differentiation or back-office automation, areas where results can be both quick and scalable.
A disciplined approach: From exploration to scaling
Drawing from our client experiences, PE firms can accelerate AI adoption in mid-market portfolio companies by guiding them through three critical phases: explore and prioritize, implement and scale.
01
Explore and prioritize
AI must be framed as both a growth engine and an efficiency lever.
Too often, mid-market companies focus only on cost-cutting. By contrast, leading adopters balance revenue opportunities such as personalized offerings and enhanced experiences) with productivity gains like automation of manual processes.
Our experience shows that most value gains for mid-size companies reside within core business processes, not support functions. In pharma, for example, processes can be segmented into high-value categories like R&D, manufacturing and regulatory affairs. These labor- and data-intensive, repeatable processes are natural candidates for AI.
Given bandwidth constraints, ROI-driven prioritization is critical. PE sponsors should push management teams to target near- and mid-term wins, aligning transformation timelines with investment horizons. The goal is pragmatic ambition: initiatives that are bold enough to change the trajectory of the business, but achievable enough to deliver measurable EBITDA improvements during the hold period. In terms of build vs. buy, most portfolio companies (60%) currently rely on external providers like software-as-a-service, with the remaining 40% opting to build in-house.
02
Implement with strong foundations
Success hinges on execution discipline.
Our analysis of hundreds of portfolio company AI use cases revealed that nearly 90% of them never move beyond the pilot stage. Many pilots fail because companies overreach before shoring up their foundations. Our analysis shows that portfolio companies that have a sound governance structure and clear data strategy in place are better positioned to scale high-impact use cases and track return on investment.
At a minimum, portfolio companies should:
- Remediate legacy IT: Ensure baseline modernization of infrastructure and data architecture to support scalable AI.
- Embed privacy and compliance: Establish cybersecurity controls, enforce data protection training and take a cautious approach to external model use.
- Deploy minimum viable products (MVPs): Launch quick-hitting, ROI-oriented initiatives to build momentum and avoid bloated costs.
- Monitor model economics: Large language models vary significantly in cost and response times. Carefully selecting models can make the difference between positive and negative ROI.
This foundation-first approach helps contain risk, control costs and deliver early wins that reinforce adoption.
03
Scale and institutionalize
Scaling AI requires more than technology—it requires cultural adoption.
Only 28% of employees say their employer’s AI training is tailored to their role or tasks. In addition to investing in role-specific, in-flow upskilling of both business and technology teams, incentives like “AI Champion” programs and AI-related KPIs can foster grassroots engagement.
Change management is equally critical. Clear communication from leadership helps minimize resistance, while staged rollouts ensure that disruption is manageable. PE sponsors should encourage portfolio leadership to institutionalize AI as part of “how work gets done,” not just as an experimental add-on.
Governance is another key aspect. Without responsible AI, mid-market companies face compliance and reputational risk: only 25% of portfolio companies we analyzed currently have a responsible AI policy.
The companies that succeed are those that integrate AI holistically, embedding it across products, processes and decision-making.
The investment angle for private equity
For PE owners, the key opportunity lies in bridging the gap between ambition and value tracking. While more than 60% of the companies we analyzed claim to have an AI strategy, under 15% track the EBIT or revenue impact of their initiatives. This creates room for investors to enforce rigor.
By requiring explicit linkage between pilots and P&L outcomes, PE firms can ensure that AI investments directly translate into enterprise value. In our experience, every $1 invested in AI transformation can deliver an annualized EBITDA uplift of 2–4x—a powerful multiplier effect at exit.
Unlocking alpha with AI
AI is no longer optional for mid-market portfolio companies—it is the next frontier of value creation. By targeting sectors with outsized potential, focusing on capabilities that deliver measurable results and adopting a disciplined three-phase approach, PE sponsors can transform their portfolio companies into faster-growing, more profitable and more resilient businesses.
The winners will be those who combine strategic focus with operational discipline—moving beyond experimentation to embed AI as a driver of reinvention. For private equity, this means both capturing efficiency and unlocking growth, ultimately amplifying valuations at exit.
The authors would like to thank Viktoriia Oshvintseva and Conrad Devin for their contributions to this article.
References
1 Accenture analysis of Eurostat data, 2025.
2 Ibid.
3 Unless otherwise mentioned, all stats in this article are derived from our analysis of hundreds of AI use cases at nearly 40 client portfolio companies, conducted in the third quarter of 2025.
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