How can you build value with technology?
Effectively planning and integrating technology within private equity portfolios has become a key driver of value creation in today’s increasingly competitive market, a topic discussed at SuperReturn International. Sector specialism is gaining prominence as general partners (GPs) focus on sourcing, supporting, and scaling portfolio companies in less mature or fragmented markets. Alongside this, more firms are now adopting an 'AI-first' strategy to boost productivity and streamline operations.
The value proposition of digital transformation
To avoid technology initiatives being dismissed as 'vanity projects,' PE firms are establishing clear metrics that tightly link digitalization efforts within their portfolio companies to return on investment (ROI).
Consistent messaging from the top, especially from CEOs, is critical to embedding digital initiatives into core strategy and driving sustained change.
Successful digital transformation hinges on disciplined execution across three core priorities:
- Making technology adoption a CEO-level agenda
- Establishing robust data quality and governance foundations
- Prioritizing a few high-impact tools over spreading resources across multiple pilots.
Among the many enablers of impactful digital transformation, several stand out:
AI deployment at scale: Companies must move beyond pilot projects and embed AI into both customer-facing and operational processes - automating call centers or personalizing marketing content, for example. The focus should be on deploying commercially robust AI solutions, often available as modules within enterprise systems, rather than waiting for perfect data or custom-built tools.
Data quality and integration: Strong data foundations are a prerequisite for advanced analytics and AI. However, progress should be parallel; companies can implement AI while improving data quality, as many modern tools now handle imperfect data effectively.
Effective use of external advisors: Leveraging specialised advisors or boutique consultancies can accelerate digital progress without overwhelming smaller, growth-stage companies. These experts deliver targeted value and help scale proven digital initiatives.
Talent and organisational culture are also fundamental.
Progress is often driven by small, passionate teams whose enthusiasm and willingness to experiment spark innovation. Leadership should foster a culture that rewards experimentation, recognises contributions, and ensures visibility of innovative efforts.
Quantitative, data-driven tools for talent assessment, both pre- and post-investment, help ensure the right skills are in place and identify opportunities for capability upgrades. Breaking down silos and promoting collaboration across business units further amplifies the impact of digital initiatives by aligning objectives and maximising data leverage.
Over time, defensibility and sustainable value creation will increasingly depend on a company’s ability to leverage proprietary data and unique distribution channels—not on building foundational AI models, which are becoming commoditized. Companies that apply digital tools uniquely suited to their business context will unlock new growth and build resilient competitive advantages.
Data as A driver for smarter investments
Data and AI are transforming private equity and venture capital as GPs seek smarter investment approaches. The shift is clear: data is evolving from a supporting tool to a strategic driver of investment decisions, with emphasis on actionable insights and operational efficiency.
A key capability is structuring and understanding data within portfolio companies to make research actionable. Like Lego blocks, well-structured data can be deconstructed, analysed, and reassembled into practical research outputs. This allows investment teams to apply findings to real-world decisions, enabling repeatable, evidence-backed strategies.
End-to-end data-driven processes are increasingly used in venture capital, especially in early-stage deal sourcing. With thousands of opportunities to assess, AI helps identify, enrich, and screen investments to find the highest-potential candidates. Since sourcing and selection drive the most value in venture, these tools reduce the risk of missing standout deals.
Private market data, once sparse, is becoming more accessible despite challenges around latency and cleanliness. Machine learning is proving powerful in modelling liquidity, forecasting cash flows, and assessing manager performance, moving data usage from public to private markets.
Yet the full potential of data and AI remains constrained by one key factor: the human element.
Overcoming reliance on tribal knowledge
AI is no longer futuristic. It’s a practical tool that can drive major productivity gains at a fraction of traditional costs. Equipping employees with AI and fostering a culture of experimentation are critical steps for PE firms aiming to become “AI-first."
Many firms believe their processes are too bespoke for automation. Yet most back-office activities are commodity tasks ripe for AI. This challenges assumptions and highlights AI’s broad applicability. Once custom-built, many AI tools are now robust third-party solutions, enabling firms to experiment internally while also adopting tools that deliver fast value.
Routine requests and administrative tasks are already handled more efficiently by AI agents. In fact, 50 to 70% of back-office work could be automated in the next few years.
Cultural resistance, rather than technical capability, remains the main barrier to AI adoption. Teams should see AI as a powerful assistant that frees them for higher-value work. Firms investing in both the right tools and a supportive culture are already seeing substantial returns.
AI hasn’t re-written the M&A playbook…but it’s coming
Buy-and-build strategies in the tech sector are currently well-positioned for growth. Valuations have reset post-2021, liquidity remains constrained, and many founder-led businesses are open to acquisition. The result: a buyer’s market.
Well-capitalized investors can scale platforms through consolidation at attractive entry points. Technology businesses in fintech, insuretech, and cybersecurity are particularly well-suited for this strategy as innovation slows, product differentiation narrows, and competition intensifies. M&A becomes the route to expand reach or enter new geographies.
Execution remains paramount. LPs expect sponsors to not only identify the right targets but also integrate them effectively. This requires a proven playbook, an experienced value creation team, and strong founder alignment. Acquiring for the wrong reasons, such as access to cash, remains a common pitfall.
Today, AI enhances productivity but hasn't yet disrupted M&A. Portfolio companies report 20 to 30% gains in software development efficiency, but these haven't translated into EBITDA gains or structural headcount changes. Large-scale success stories remain anecdotal.
For now, AI augments M&A. It is yet to redefine it.
Specialists vs generalists: A smarter way to allocate in the age of compression
As private markets mature, the debate between sector specialism and generalist investing grows more relevant. Investors must demonstrate insight across market cycles, geographies, and portfolio construction. Specialism drives greatest impact in early to mid-stage growth. Later-stage investing prioritizes pricing discipline and investment judgment, both of which are core strengths of generalists.
Sector specialists tend to add more value in early and growth-stage investing, where complexity and volatility demand sharper underwriting. Their domain knowledge and pattern recognition help founders scale and drive post-deal outcomes.
Generalists, by contrast, can pivot focus dynamically and allocate capital rationally. Their broader mandate helps avoid sector-specific froth and pricing pressure; particularly useful in today’s uncertain macro environment.
Geographic dynamics also shape strategy.
In the U.S., market depth enables focused strategies. In Europe and Asia, a hybrid approach often prevails, blending sector expertise with local cultural fluency. Operating in Germany, for instance, requires fintech or enterprise knowledge plus native language skills and locally-networked investors. Similarly, in MENA, investors segment markets like Saudi Arabia, Egypt, and the UAE not just by sector but by growth model and market structure, reflecting a pragmatic, ecosystem-driven lens within their investment strategies.
As private equity matures and return compression sets in, large LPs are increasingly building in-house sector expertise to participate in co-investments alongside GPs.
Specialization is therefore no longer just a GP advantage; it’s a lever for LPs seeking to sustain outperformance in private markets.
Looking ahead, firms that combine sharp sector insight, operational discipline, and digital fluency will likely lead the next wave of technology-led value creation.
