AI has quickly moved from boardroom discussion to business priority.
Across private markets, firms are exploring how artificial intelligence can improve investment processes, streamline operations, strengthen investor reporting and unlock more value from their data. For private markets firms, however, the opportunity is not limited to making existing processes more efficient.
Used effectively, AI can help firms assess opportunities faster, accelerate due diligence and investment decision-making, improve portfolio oversight, scale investor servicing and support growth without proportionately increasing operational complexity.
The challenge is that firms are progressing at very different rates. In our conversations with private markets organisations, we consistently see three broad stages of AI maturity. Understanding where your firm sits is the first step towards deciding what to prioritise and how to create lasting business value.
Three stages of AI maturity
While every organisation’s journey is different, most private markets firms we work with fall into one of three broad stages:
- Assessing AI opportunities – choosing where AI can create the greatest value and proving the right use cases.
- Driving AI adoption – embedding proven use cases into everyday processes and building consistent capability.
- Scaling AI capabilities – integrating AI into the firm-wide operating model.
Each stage presents its own opportunities and challenges.
Stage one: assessing AI opportunities
At the first stage, the priority is not selecting technology. It is identifying the business problems where AI can make the greatest difference and determining whether the firm has the data, governance and operating foundations to support them.
The question firms should be asking:
- Where could AI help investment teams identify, assess and act on opportunities more quickly?
- How could AI improve the speed and quality of due diligence, investment committee preparation and decision-making?
- Could AI provide earlier insight into portfolio company performance, risk, valuation movements or emerging issues?
- How could AI help the firm scale fundraising, investor relations and investor servicing without proportionately increasing headcount?
- Which manual processes across the investment lifecycle consume the most time or create the greatest operational risk?
- Is the firm’s data, technology architecture and governance ready to support AI securely and at scale?
These questions move the conversation beyond generic productivity gains. They focus attention on where AI could help the firm make better decisions, handle greater scale and improve service to investors and portfolio companies.
Where private markets firms often start
Investment teams spend significant time reviewing documentation, researching markets and preparing investment committee materials. Operations and investor relations teams repeat reporting, DDQ and communication processes, while compliance teams manage growing documentation and oversight requirements.
Early AI initiatives frequently focus on:
- Investment research and market intelligence.
- Due diligence support and document review.
- Investment committee papers and meeting preparation.
- LP reporting and investor communications.
- Compliance documentation.
- Analysing data held in siloed sources, such as Excel spreadsheets and standalone reporting files.
- Knowledge management.
These are practical, low-risk opportunities that allow firms to demonstrate value before tackling more complex initiatives.
Moving beyond experimentation
One of the biggest mistakes firms make at this stage is attempting to deploy AI everywhere at once. A stronger approach is to select a small number of well-defined use cases that can demonstrate measurable value and build confidence across the organisation.
That means:
- Prioritising activities that are repetitive, high-volume or decision-critical.
- Defining the intended business outcome before launching a pilot.
- Involving investment, operations, technology, risk and compliance teams from the outset.
- Establishing governance alongside innovation rather than adding it later.
- Measuring time saved, quality improved, risk reduced or capacity created, not simply AI usage.
The goal isn’t to prove AI works. It’s to prove where it works for your organisation.
Stage two: driving AI adoption
Once a firm has proven valuable use cases, the focus shifts from experimentation to changing how people work. This is often the most difficult stage because value remains dependent on whether teams understand, trust and consistently use the capability.
It is common to see enthusiastic early adopters sitting alongside colleagues who remain uncertain about where AI fits into their role. Some teams may develop strong use cases, while others rely on ad hoc prompts or avoid the tools altogether. Without a coordinated approach, the firm creates isolated pockets of success rather than a repeatable organisational capability.
What this stage looks like in practice
- AI tools are available, but usage and confidence vary significantly between teams.
- Investment professionals use AI for selected tasks, but the approach depends heavily on individual initiative.
- Teams create separate prompt libraries, agents or workflows with limited visibility across the firm.
- Employees remain unsure which information can safely be shared with different tools.
- Leadership supports adoption, but benefits are not yet measured consistently.
- Governance policies exist or are being developed, but they are not fully embedded in day-to-day processes.
Building an AI-enabled organisation
Driving adoption is not about purchasing additional technology. It is about helping people apply AI confidently within the workflows that matter to their roles.
Leading firms focus on:
- Role-based training built around real investment, operations, investor relations and compliance activities.
- Redesigning workflows so AI supports an end-to-end process rather than a single isolated task.
- Creating a shared library of proven use cases, prompts and working practices.
- Developing AI champions who can support colleagues and identify further opportunities.
- Providing clear guidance on security, confidentiality, human review and accountability.
- Measuring outcomes such as turnaround time, quality, capacity and user adoption.
At this stage, AI starts to become a firm capability rather than simply another software platform. The objective is to make successful use cases repeatable, governed and accessible across the organisation.
Stage three: scaling AI capabilities
At the third stage, AI moves beyond individual tools and becomes embedded into the firm’s systems, data and operating model. The focus is no longer simply on helping people complete tasks faster. It is on redesigning processes so that people, data and AI work together across the investment lifecycle.
What scale looks like
- AI is integrated with core platforms such as CRM, portfolio monitoring, fund accounting, document management and enterprise data platforms.
- Users can securely access governed information across deals, funds, investors and portfolio companies without manually searching multiple systems.
- Automated workflows span several functions, with clear human review and escalation points.
- Reusable AI services, models and agents are governed centrally rather than rebuilt by individual teams.
- Performance, risk, adoption and return on investment are monitored continuously.
- Data ownership, lineage, permissions and quality controls support reliable firm-wide use.
What this can enable
When the right foundations are in place, scaling AI can help private markets firms:
- Assess a greater volume of opportunities without weakening investment discipline.
- Accelerate diligence and investment committee preparation while improving consistency.
- Identify portfolio risks, trends and value-creation opportunities earlier.
- Scale fundraising and investor servicing as the LP base grows.
- Launch new funds, strategies or geographies without replicating the same operational overhead.
- Preserve and apply institutional knowledge as teams and assets under management grow.
This stage depends on much more than technology. High-quality data, well-designed architecture, strong governance, process ownership and organisational change all become critical to maintaining trust and delivering value at scale.
Why most firms stall
Many firms successfully launch AI pilots, and many purchase enterprise tools. Far fewer create the foundations needed to move from isolated successes to a scalable capability.
The most common barriers are rarely a lack of AI technology. They include:
- Fragmented or poor-quality data across core platforms.
- Unclear ownership of AI strategy, use cases and outcomes.
- Security and governance policies that are either unclear or too difficult to apply.
- Limited change management and role-specific training.
- Difficulty prioritising use cases with meaningful commercial value.
- No consistent approach to measuring return on investment.
- Pilots that sit outside core workflows and cannot be operationalised.
Technology can often be deployed quickly. Building organisational capability takes longer. The firms that lead the market will not necessarily be those with the largest AI budgets. They will be those that connect business priorities with strong governance, quality data, effective adoption and a practical roadmap for delivery.
What’s next for your organisation?
Whether you’re just beginning to explore AI or looking to scale successful initiatives, understanding your organisation’s readiness is the first step towards long-term success.
At it|venture, we help private markets firms understand their current AI maturity and turn broad ambition into a practical plan. Our work connects business priorities with the data, technology, governance and organisational capabilities needed to deliver measurable results.
The it|venture AI Healthcheck
A focused assessment designed to give your firm a clear view of where it stands, which opportunities matter most and what needs to happen next.
- A current-state AI maturity assessment.
- A prioritised portfolio of high-value use cases.
- A review of data and technology readiness.
- Governance, security and operating-model recommendations.
- A practical roadmap from experimentation to execution.
Whether your firm is assessing opportunities, driving adoption or preparing to scale, the next step is to understand where you are today and focus investment on the areas most likely to create measurable value.
