Private equity firms may be racing to adopt artificial intelligence, but limited partners are signaling that AI alone is not enough to make a fund manager stand out. A new Gen II survey finds LPs place greater value on technology infrastructure and measurable outcomes—including faster reporting, transparency, actionable insights and customizable reporting—than on AI adoption itself.
The private equity industry’s enthusiasm for artificial intelligence is running into a more practical investor test: does the technology actually improve the experience of limited partners?
Findings from Gen II Fund Services’ 2026 GP/LP Technology Survey suggest that, for LPs, the answer depends less on whether a general partner has deployed AI and more on whether its technology produces better operational outcomes.
The survey found that 62% of LP respondents identified infrastructure as a high-value technology priority, while 61% pointed to outcomes such as reporting speed, transparency, actionable insights and reporting customization. By comparison, workflow digitization ranked at 53% and AI adoption at 41%.
That gap illustrates a broader shift in enterprise AI. As generative AI becomes easier to access, simply deploying an AI tool is becoming less distinctive. For private capital firms, the competitive question is increasingly whether AI can improve the infrastructure surrounding fundraising, investor reporting, fund administration and portfolio operations.
Gen II, a private capital fund administrator, says the survey reflects growing LP expectations for institutional-quality fund operations. Its research focused on how LPs evaluate GP technology and what separates firms perceived as technologically advanced from those that merely adopt individual tools.
One of the sharpest findings concerns the disconnect between general partners and their investors. GPs cited AI as a relevant source of technology differentiation at five times the rate of LPs—16% versus 3%.
That disparity is important because private equity firms increasingly use AI across investment research, due diligence, portfolio monitoring, document analysis and investor relations. Yet LPs ultimately encounter the technology through outcomes: the quality of information they receive, how quickly questions are answered, how easily data can be accessed and whether reporting can be adapted to their requirements.
In other words, the AI model may be invisible to the investor.
That does not mean AI is becoming irrelevant to private equity. Quite the opposite. The technology is becoming increasingly embedded in the infrastructure that supports investment firms.
A 2026 FTI Consulting survey of 200 private equity fund and operating leaders found that 95% of funds reported AI initiatives meeting or exceeding their original business-case criteria. At the same time, only 7% reported enterprise-scale AI deployment across portfolio companies, highlighting the distance between successful individual use cases and broad organizational adoption.
Another 2026 survey from Grant Thornton found that 46% of private equity leaders were scaling AI across functions, while 80% were exploring or piloting agentic AI.
Those findings help explain why Gen II’s infrastructure-first message matters.
Private equity firms are dealing with increasingly complex data environments. Investment teams, fund administrators and investor-relations departments must reconcile information from portfolio companies, accounting systems, CRM platforms, data rooms and reporting applications. AI can help extract, summarize and analyze that information, but its usefulness depends on the quality, accessibility and governance of the underlying data.
That makes infrastructure less visible but potentially more consequential than the AI interface layered on top.
McKinsey’s 2026 Global Private Markets Report reinforces the importance of operational capabilities to fundraising. Its research found that 53% of 300 LPs surveyed ranked a GP’s value-creation strategy among their top five manager-selection criteria, placing it third behind investment performance and the quality of the investment team and diligence.
For GPs, this creates a different technology playbook from the one often associated with generative AI.
Instead of leading an LP conversation with an AI copilot, chatbot or automated workflow, managers may need to demonstrate that technology improves four operational dimensions: speed, transparency, customization and infrastructure.
Custom reporting is particularly important. LPs have different investment mandates, reporting requirements, compliance obligations and internal data systems. A standardized PDF or dashboard may no longer be sufficient for sophisticated institutional investors.
This creates opportunities for fund-administration platforms, private-market software providers and AI infrastructure vendors to compete on interoperability and data quality as much as on machine-learning capabilities.
The broader private-equity software market is already moving in that direction. A 2026 industry software analysis identified hundreds of specialized vendors spanning portfolio monitoring, LP portals, CRM, accounting, document automation, fund performance and valuation. AI capabilities are increasingly being incorporated across those categories rather than existing as a separate software layer.
The competitive implication is straightforward: AI is becoming a feature of the private-capital technology stack, not necessarily the stack’s defining product.
For GPs, that could make technology differentiation harder. If multiple managers use similar large language models and automation platforms, the differentiator shifts toward proprietary data, integration quality, workflow design, security, governance and the ability to turn technology investments into measurable investor outcomes.
It also changes how firms should measure AI success. Instead of asking how many employees use an AI tool, managers may need to track whether investor reporting is faster, whether errors decline, whether LP requests are resolved more quickly and whether investors can obtain more actionable information without additional operational overhead.
Gen II’s survey ultimately points to a maturing enterprise AI market in private capital. The technology is moving into the operational layer, where its value will be judged by investors who may care little about which model powers a workflow.
For GPs competing for capital, that distinction could become increasingly important. AI may help create technological differentiation, but the differentiator LPs actually see is better infrastructure and better outcomes.
Market Landscape
Private equity is entering a more mature phase of AI adoption. The initial focus on experimentation and productivity is giving way to questions around ROI, governance, data infrastructure and measurable value creation.
AI is already being applied across investment research, diligence, portfolio operations and investor relations. L.E.K. Consulting found that private-equity professionals reported an average 28% productivity improvement from AI use, while only a minority considered their firms or portfolios advanced in AI adoption.
For fund managers, this creates a two-layer technology strategy: establish reliable infrastructure and data foundations first, then deploy AI where it produces measurable improvements.
The competitive landscape increasingly includes traditional fund administrators, private-markets software companies and major cloud and AI providers such as Microsoft, Google, Amazon and NVIDIA. The differentiator will increasingly be integration, governance and business outcomes rather than access to AI models alone.
Top Insights
- LPs prioritize outcomes: Reporting speed, transparency, actionable insights and customization matter more to investors than AI adoption as a standalone technology metric.
- GPs may overestimate AI differentiation: Managers cited AI as a differentiator five times more frequently than LPs, exposing a significant expectation gap.
- Infrastructure remains foundational: Reliable data, integrations and reporting systems provide the foundation on which effective generative AI and automation can operate.
- AI is becoming embedded: Private-equity software vendors increasingly integrate AI across reporting, diligence, portfolio monitoring, document processing and investor workflows.
- Technology must support fundraising: Better operational capabilities can strengthen the institutional-quality experience LPs increasingly expect when evaluating GPs.
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