Artificial intelligence is becoming an increasingly important part of private equity operations. According to UK Private Capital’s September 2026 Private Capital Pulse survey, 78% of surveyed private equity and venture capital firms now use AI for portfolio monitoring, performance analysis and investor reporting. The research surveyed 76 senior decision-makers across private capital firms operating throughout the UK.
This figure suggests that AI is moving beyond investment research and deal sourcing into the ongoing management of portfolio businesses. However, using AI to monitor performance is not necessarily the same as embedding it into everyday operations. The distinction matters when assessing how much value private equity firms are actually generating from their AI investments.
So, how widespread is AI adoption across UK private equity, where is it making the biggest difference, and what is preventing firms from getting more value from the technology?
Jump to
- How Many UK Private Equity Firms Are Using AI?
- Where Are Private Equity Firms Applying AI?
- Are Portfolio Companies Actually Using AI?
- Why Is AI Adoption Not Always Delivering Results?
- What Are the Biggest Barriers to AI Implementation?
- What Should UK Private Equity Firms Do Next?
- How Fliweel Can Help Private Equity Firms Turn AI Opportunities into Operational Results
- Frequently Asked Questions
How Many UK Private Equity Firms Are Using AI?
The latest industry research suggests that AI adoption among UK private capital firms is already widespread. However, the level of adoption varies depending on whether the technology is being used within investment firms themselves or across the businesses they own.
UK Private Capital’s September 2026 survey provides one of the clearest pictures of adoption within the industry.
Its findings show:
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92% use AI for due diligence and document review.
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87% use AI for investment analysis and decision support.
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84% use AI for deal sourcing and market mapping.
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84% use AI for administrative and back-office tasks.
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78% use AI for portfolio monitoring, performance analysis and investor reporting.
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75% use AI for financial modelling and forecasting.
These findings demonstrate that AI is no longer limited to experimental applications within private capital. Firms are already using it across several important investment and operational activities.
However, the survey covered 76 private equity and venture capital investors, rather than every UK private equity firm. It should therefore be understood as an industry adoption benchmark, not a complete national census.
Does this mean most UK private equity firms have fully integrated AI?
No. Using AI in at least one business activity does not mean that a firm has integrated it across its entire operating model. Many organisations still use separate AI tools for individual tasks rather than connected systems that support complete workflows.
Where Are Private Equity Firms Applying AI?
The UK Private Capital research reveals that adoption is particularly strong in activities involving large volumes of information, repetitive administration and analytical work.
This is consistent with the areas where AI can reduce manual effort and help investment professionals make better-informed decisions.
1. Due Diligence and Document Review
With 92% of surveyed firms using AI for due diligence and document review, this is the most widely reported application.
Private equity transactions involve reviewing financial statements, contracts, compliance records, market research and operational documents. AI can help extract relevant information, summarise lengthy documents, identify inconsistencies and highlight potential risks for further investigation.
This allows investment teams to spend less time searching through documents and more time evaluating the findings.
2. Investment Analysis and Decision Support
UK Private Capital found that 87% of respondents use AI for investment analysis and decision support.
AI can help investment professionals process market information, compare company performance and identify patterns across financial and operational data.
However, investment decisions still require human judgement. AI-generated analysis should support professional decision-making rather than replace investment committees or experienced deal teams.
3. Portfolio Monitoring and Performance Reporting
Portfolio monitoring is particularly relevant to the question of AI adoption in portfolio operations.
The survey found that 78% of firms use AI for portfolio monitoring, performance analysis and investor reporting.
AI can help consolidate information from different portfolio businesses, identify unusual movements in performance indicators and prepare management reports.
For investment firms overseeing multiple companies, this can provide a more consistent view of performance and potential operational issues.
4. Administrative and Back-Office Automation
Administrative work is another significant area of adoption, with 84% of surveyed firms using AI for back-office tasks.
Potential applications include processing documents, managing internal requests, extracting information from emails and automating routine reporting activities.
These applications may appear less strategic than investment analysis, but they can free up valuable time across finance, operations and investment teams.
Are Portfolio Companies Actually Using AI?
This is where the adoption picture becomes more complicated.
Private equity firms may actively use AI within their investment teams while their portfolio companies remain at different stages of implementation.
UK Private Capital’s research found that 93% of surveyed investors encourage their portfolio companies to use AI for administrative and back-office tasks. Meanwhile, 92% encourage adoption for data analysis and business intelligence, and 91% for product development.
The figures demonstrate strong support from investors. However, encouraging adoption does not automatically mean that portfolio companies have deployed working AI solutions.
The same research highlights a considerable implementation gap.
| Area of Portfolio Company Support | Percentage of Firms |
|---|---|
| Knowledge sharing and best-practice exchange | 72% |
| Identifying high-value AI opportunities | 63% |
| Developing AI strategies and roadmaps | 61% |
| Management education and awareness | 50% |
| Bringing in external AI specialists | 50% |
| Supporting AI governance | 45% |
| Integrating AI into business processes | 32% |
| Providing access to AI tools and infrastructure | 32% |
| Providing dedicated AI funding | 20% |
The difference between encouraging AI adoption and integrating it into business processes is important. Only 32% reported helping portfolio companies integrate AI into their operations.
This suggests that identifying opportunities and developing strategies are more common forms of support than hands-on implementation.
Are private equity firms doing enough to support AI adoption across their portfolios?
Not necessarily. Encouraging experimentation and sharing knowledge are valuable starting points, but portfolio companies also need implementation support, suitable technology, employee training and measurable operational targets.
Without these elements, AI initiatives can remain isolated experiments rather than improvements that affect business performance.
Why Is AI Adoption Not Always Delivering Results?
The wider UK business environment provides useful context for understanding this challenge.
The UK Government’s AI Adoption Survey 2026, published on 29 October 2026, found that 32% of UK businesses were using at least one AI technology, double the 16% reported in 2025.
However, widespread access to AI tools does not necessarily translate into significant operational transformation.
The same survey identified several important findings:
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81% of businesses using or planning to use AI reported applications in administration.
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71% reported using AI in marketing.
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66% reported applications in IT functions.
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50% of AI adopters reported an overall increase in productivity.
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64% reported no immediate financial effect on operating costs.
These findings show that businesses are increasingly incorporating AI into everyday activities. However, measurable financial improvements are not guaranteed.
The distinction is particularly important for private equity firms, where AI investments are expected to contribute to a clear value-creation strategy.
For example, introducing an AI assistant to help employees draft emails may improve individual productivity. However, automating an entire reporting workflow could have a more direct effect on processing time, resource allocation and operating costs.
The objective should therefore be to identify processes where AI can improve measurable business outcomes rather than simply increase the number of AI tools being used.
What Are the Biggest Barriers to AI Implementation?
Research from FTI Consulting provides another perspective on the gap between AI ambition and operational adoption.
Its 2026 Private Equity AI Radar, based on a survey of 200 private equity decision-makers worldwide, found that only 36% reported their portfolio companies were using AI in day-to-day operations. Just 7% said AI was fully integrated across their portfolio companies.
Importantly, this is an international survey, not a UK-specific adoption estimate. Nevertheless, it highlights a challenge that UK investors should consider.
The report also found that 65% of respondents said AI programmes in their portfolio companies had exceeded their financial targets. A further 30% reported that targets had been met as planned.
These findings suggest that AI can deliver meaningful value when initiatives are implemented effectively, even though widespread day-to-day adoption remains difficult.
One of the biggest barriers identified was the shortage of AI and IT specialists, cited by 35% of respondents.
For portfolio companies, particularly those without dedicated technology teams, implementation can involve several challenges:
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Limited technical expertise: Employees may understand their operational problems but lack the skills to build and maintain AI solutions.
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Disconnected systems: Business information may be spread across CRMs, spreadsheets, finance platforms and other applications.
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Unclear return on investment: Companies may struggle to identify which AI projects will deliver measurable financial benefits.
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Employee resistance: Teams may be unfamiliar with AI or concerned about how automation will change their responsibilities.
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Governance and security: Businesses need appropriate controls around sensitive financial, customer and operational information.
These barriers explain why purchasing AI subscriptions is rarely enough to transform a portfolio company’s operations.
What Should UK Private Equity Firms Do Next?
The research points towards an important shift in private equity AI strategy. The focus should move from encouraging adoption to implementing solutions that improve how portfolio companies operate.
For investment firms, this means identifying opportunities that can be repeated across different businesses while recognising that each portfolio company will have its own systems, processes and operational priorities.
There are several practical steps to consider.
1. Identify High-Value Operational Processes
Start by examining repetitive activities across finance, sales, customer service, HR and administration.
Examples include invoice processing, customer enquiry management, financial reporting, document extraction and sales follow-ups.
These processes often provide measurable opportunities to reduce manual work and improve consistency.
2. Assess Existing Technology and Data
Before introducing new AI platforms, review the systems already used by each portfolio company.
AI solutions should ideally work with existing CRMs, finance software, document management platforms and business applications.
Connecting these systems can be more valuable than introducing another standalone tool.
3. Prioritise Implementation Over Experimentation
Rather than launching several unrelated AI pilots, select a specific operational problem and build a solution around it.
Agree on measurable targets before implementation. These might include processing time, cost per transaction, response times, error rates or employee hours saved.
4. Establish Governance and Human Oversight
AI implementation should include appropriate access controls, data protection measures and clear accountability.
Human review should remain part of workflows involving financial decisions, compliance, sensitive customer information and other high-risk activities.
5. Measure Results Across the Portfolio
Private equity firms should establish a consistent way to assess AI performance across their portfolio companies.
Tracking implementation progress, adoption rates and financial outcomes can help investors identify successful solutions and opportunities to scale them elsewhere.
How Fliweel Can Help Private Equity Firms Turn AI Opportunities into Operational Results
For private equity firms, the challenge is not simply finding AI opportunities. It is turning those opportunities into working solutions that improve portfolio company performance.
Fliweel is a UK-based AI and automation consultancy that helps organisations implement practical AI solutions within their existing technology environments.
Its services include AI strategy and implementation, workflow automation, AI agents, document processing and systems integration.
For portfolio companies, this can mean automating repetitive administrative processes, connecting disconnected business applications, improving customer enquiry management and reducing manual document handling.
Fliweel’s approach focuses on understanding existing processes, identifying opportunities for improvement and implementing solutions that deliver measurable business outcomes.
Rather than treating AI as another software subscription, private equity firms can work with an implementation partner to identify where automation can support their value-creation plans.
Explore Fliweel’s AI and automation services to understand how practical AI implementation can support operational efficiency and business growth.
Frequently Asked Questions
How many UK private equity firms are using AI?
UK Private Capital’s September 2026 survey of 76 private equity and venture capital investors found that 78% use AI for portfolio monitoring, performance analysis and investor reporting. Adoption is higher in other activities, including due diligence and document review, at 92%.
What are the most common uses of AI in private equity?
The most common applications include due diligence, document review, investment analysis, deal sourcing, administrative automation, portfolio monitoring and financial forecasting.
Are private equity firms using AI across their portfolio companies?
Many are actively encouraging adoption. UK Private Capital found that 93% encourage AI use for administrative and back-office tasks. However, only 32% reported helping portfolio companies integrate AI into business processes and operations.
How does AI create value for private equity portfolio companies?
AI can help portfolio companies improve productivity, automate repetitive work, reduce processing times, improve customer service and strengthen business reporting. The financial impact depends on the processes being automated and how effectively solutions are implemented.
What prevents private equity portfolio companies from adopting AI?
Common barriers include limited technical expertise, disconnected business systems, unclear ROI, employee resistance and concerns about data security and governance.
Should private equity firms invest in AI implementation or AI tools?
Both can be important, but purchasing tools without a clear implementation plan can lead to limited results. Firms should prioritise solutions that address specific operational challenges, integrate with existing systems and deliver measurable improvements.
Final Thoughts
The latest research shows that AI has already become an important part of UK private capital operations. With 78% of surveyed firms using AI for portfolio monitoring and performance reporting, adoption is moving beyond dealmaking into portfolio management.
However, the figures also reveal that encouraging AI adoption is easier than integrating it into everyday business processes.
For private equity firms, the next opportunity is to close that gap. By focusing on practical implementation, connected workflows and measurable outcomes, investors can help portfolio companies move beyond experimentation and use AI as a genuine driver of operational efficiency and long-term value creation.
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