Artificial intelligence (AI) has quickly moved from boardroom discussion to business priority.
Across private markets, firms are exploring how AI can improve investment processes, streamline operations, strengthen investor reporting and unlock greater value from their data. Used well, AI can help firms assess opportunities faster, accelerate due diligence, improve portfolio oversight, enhance investor servicing and support growth without proportionately increasing operational complexity.
The goal isn’t to prove AI works. It’s to prove where it works for your organisation.
That is where many firms struggle.
Technology is evolving rapidly, yet organisations are progressing at very different rates. Some are still identifying the right opportunities, while others are embedding AI into everyday workflows or scaling it across the business.
In our conversations with private markets firms, we consistently see three broad stages of AI maturity:
- Assessing AI opportunities – Identifying where AI can create measurable business value.
- Driving AI adoption – Embedding successful use cases into everyday ways of working.
- Scaling AI capabilities – Integrating AI into the firm’s operating model and long-term strategy.
Most firms we work with are currently at Stages 1 or 2, while a smaller number are beginning to scale AI more strategically.
It’s also important to recognise that maturity isn’t always consistent across an organisation. A firm may be highly advanced in due diligence while still at an early stage in investor relations or operations. Likewise, organisations don’t always move neatly from one stage to the next. Many adopt AI through existing enterprise software before they’ve identified the business problems it should solve, making adoption more difficult and limiting long-term value.
Understanding where your organisation sits today is the first step towards deciding what to prioritise next.
Stage one: Assessing AI opportunities
The first stage is not about choosing technology.
It’s about identifying the business problems where AI can create the greatest value and determining whether the organisation has the data, governance and operating foundations needed to support it.
Rather than asking, “Where can we use AI?“, firms should ask:
- Where could AI help investment teams identify, assess and act on opportunities more quickly?
- How could AI improve due diligence, investment committee preparation and decision-making?
- Could AI provide earlier insight into portfolio company performance, risk or valuation movements?
- How could AI improve fundraising, investor reporting and investor servicing without increasing operational overhead?
- Which manual processes consume the most time or create the greatest operational risk?
- Is our data, technology architecture and governance ready to support AI securely?
These questions move the conversation beyond generic productivity gains and towards measurable commercial outcomes.
Where firms typically begin
Many successful initiatives start with practical, lower-risk use cases, such as:
Investment research, due diligence and investment committee preparation.
Investor reporting, communications and compliance documentation.
Unlocking insight from fragmented internal data and knowledge.
These use cases allow firms to demonstrate value, build confidence and establish good governance before expanding into more complex initiatives.
Moving beyond experimentation
The output of this stage should be a clear AI strategy, a prioritised roadmap and agreed ownership for delivery.
One of the biggest mistakes organisations make is trying to deploy AI everywhere at once.
A stronger approach is to focus on a small number of well-defined use cases with clear business outcomes.
Leading firms typically:
Prioritise repetitive, high-volume or decision-critical activities.
Define success measures before launching pilots.
Involve business, technology, risk and compliance teams from the outset.
Establish governance around data access, use-case approval and success criteria before scaling.
Measure outcomes such as time saved, quality improved, reduced risk or increased capacity.
Not every pilot should progress. Some organisations discover that certain use cases simply don’t deliver sufficient value, and that’s a worthwhile outcome in itself. The objective is to identify where AI creates meaningful commercial impact before investing further.
Stage two: Driving AI adoption
Once valuable use cases have been identified, the focus shifts from experimentation to execution.
This stage is about delivering the roadmap created during the assessment phase and embedding AI into day-to-day ways of working. For many organisations, this is where the real challenge begins.
Technology alone doesn’t create value. People do.
It’s common to see enthusiastic early adopters sitting alongside colleagues who are unsure where AI fits into their role. Different teams may develop their own prompts, workflows or ways of working, resulting in isolated successes rather than a consistent organisational capability.
What this stage looks like
Typically, firms at this stage experience some or all of the following:
- AI is available, but adoption varies significantly between teams and functions.
- Successful use cases exist, but knowledge and best practice aren’t shared consistently across the organisation.
- Employees need clearer guidance on which AI tools they can use, what information can be shared and where human review is required.
- Leadership recognises the value of AI, but benefits are not yet measured consistently.
The goal is no longer to prove that AI works. It’s to ensure that proven use cases become repeatable, trusted and scalable.
Building capability across the organisation
Leading firms focus on helping people apply AI confidently within the workflows that matter most to their role.
That typically includes:
- Delivering role-based training aligned to the firm’s priority use cases, whether that’s due diligence, investor reporting, compliance or operations.
- Redesigning workflows so AI supports complete business processes rather than isolated tasks.
- Building shared, owned AI assets – such as output templates, configured agents and reusable skills – rather than personal prompt collections, so capability is versioned, reviewable and retained as the organisation grows.
- Establishing governance that defines approved tools, appropriate data sharing, human oversight and accountability.
- Measuring success through operational outcomes – such as due diligence turnaround times, DDQ response times, investor reporting timelines or operational capacity – rather than AI usage statistics alone.
At this stage, AI begins to move beyond individual productivity gains and becomes an organisational capability that supports consistent delivery across the business.
Stage three: Scaling AI capabilities
At the third stage, AI becomes part of the firm’s operating model rather than another technology initiative.
The objective is no longer simply helping individuals work faster. It is creating an environment where trusted data, integrated systems and governed AI capabilities support better decisions across the investment lifecycle.
For some firms, this stage is appropriate. For others, scaling every use case may not deliver sufficient commercial value. The important point is that scaling should be driven by proven business outcomes rather than technology for its own sake.
What scale looks like
Organisations operating at this level typically have:
- A governed enterprise data platform that provides trusted information across CRM, portfolio monitoring, fund accounting, document management and other core business systems.
- Maintained integrations across core business systems and fund administrators, ensuring AI works from consistent, trusted information rather than disconnected data sources.
- AI services that draw from consistent, high-quality data rather than creating separate integrations into individual applications.
- Automated workflows that span multiple business functions while maintaining clear human review and approval points.
- Centrally governed AI models, agents and reusable capabilities rather than isolated solutions built by individual teams.
- Enterprise governance covering model lifecycle management, permissions, data lineage, monitoring and ongoing performance measurement.
As AI becomes embedded across the organisation, governance evolves as well. The focus shifts from approving individual use cases to managing AI as an enterprise capability with clear ownership, transparency and control.
Ultimately, successful scaling depends on much more than technology. High-quality data, effective governance, well-designed processes and organisational adoption all play an equally important role in delivering sustainable value.
Why many firms stall
Most private markets firms don’t struggle because they lack access to AI technology. They struggle because the organisational foundations needed to adopt AI successfully aren’t yet in place.
The most common barriers we see include:
- Fragmented or poor-quality data that limits the value AI can deliver.
- Unclear ownership of AI strategy, priorities and business outcomes.
- Governance and security policies that don’t provide practical guidance for teams.
- Limited change management, role-specific training and user adoption.
- Difficulty identifying and prioritising the use cases that will deliver meaningful commercial value.
Technology can often be deployed quickly. Building organisational capability takes considerably longer.
The firms that realise the greatest return from AI won’t necessarily be those investing the most in technology. They’ll be those that connect AI to clear business priorities, trusted data, effective governance and a practical roadmap for delivery.
What’s next for your organisation?
Whether your organisation is just beginning to explore AI or looking to scale proven initiatives, understanding your current level of maturity is the first step towards making informed investment decisions.
Every firm’s starting point is different. Some need help identifying where AI will create the greatest value. Others are focused on improving adoption, strengthening governance or building the data foundations needed to scale successfully.
The important thing is having a clear understanding of where you are today and what should come next.
The it|venture AI Healthcheck
At it|venture, we help private markets firms assess their AI maturity and turn ambition into a practical plan for delivery.
Our AI Healthcheck is a focused engagement, typically delivered over two to four weeks (subject to client availability), providing an independent assessment of your organisation’s readiness and identifying the actions that will create the greatest business value.
It includes:
- A current-state AI maturity assessment.
- A prioritised portfolio of high-value AI use cases.
- A review of data, technology and governance readiness.
- Recommendations for security, operating model and organisational capability.
- A practical roadmap that supports successful adoption and long-term scale.
Whether your organisation is assessing opportunities, driving adoption or preparing to scale AI capabilities, the Healthcheck provides a structured starting point for making confident, informed decisions understand where you are today and focus investment on the areas most likely to create measurable value.
