Marc Scheipe is CEO at Allvue Systems.
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In private markets, efficiency and precision are essential. Whether in private equity, credit, or venture capital, firms operate under pressure to move quickly, report accurately, and make informed decisions across increasingly complex workflows. In this environment, artificial intelligence isn’t a novelty, it’s becoming a practical tool that improves operations, supports teams, and creates long-term value.
AI’s impact won’t be instant or universal. But its role in simplifying work, accelerating tasks, and improving data access is already becoming clear. For firms managing fund operations, portfolio oversight, or investor communications, it’s time to consider where AI creates tangible results. From automating recurring workflows like capital call processing and valuation updates, to enabling real-time monitoring of fund performance and risk exposure, AI offers firms a powerful way to reduce latency and increase accuracy across day-to-day operations. In portfolio oversight, AI can analyse credit and equity positions against market conditions, compliance parameters, and internal thresholds.
Plus it’s able to flag exceptions before they become issues. In reporting, the new technology helps draft LP updates and is able to create dashboards tailored to specific investor queries. This new way of working cuts down the time and effort required to meet increasing transparency demands.
Artificial Intelligence can also assist in compliance and regulatory tracking, helping firms stay aligned with evolving mandates by identifying gaps in data or documentation. Perhaps most importantly, AI enables faster, more informed decision-making by synthesising data from across systems and surfacing relevant insights in context—whether that’s for a deal committee, a finance lead or an IR professional. These are not theoretical benefits; they’re already showing up in early deployments across private capital firms. The question is no longer whether AI can help, but where it can make the biggest difference first.
A Measured Approach to AI Adoption
Finance has always adopted technology when it adds clear value. That same lens should be applied to AI. A thoughtful approach, not rushed experimentation, will define the firms that gain an advantage without compromising accuracy, trust, or client service.
We recommend a simple framework:
- Pilot: Begin with straightforward, repetitive tasks. Document processing, meeting notes, and data lookups are ideal areas to test AI and give teams a low-risk way to build familiarity.
- Expand: As comfort grows, introduce AI into collaborative workflows. Marketing content development, investor summaries, or fund performance snapshots are areas where automation can support multiple teams.
- Accelerate: Over time, think about how entire processes could be redesigned with AI at the center. Rather than layering AI onto existing steps, consider how work could be structured differently, more integrated, less manual, and easier to manage across teams.
Why Agentic AI Matters for Private Markets
Alternative investments involve more than just complex math. They rely on coordination across departments, deep data visibility, and compliance at every stage of the investment lifecycle. In this context, AI agents—systems that can take action, not just provide information—offer real utility.
Agentic AI platforms can support tasks like document classification, basic compliance checks, or real-time performance queries. They reduce the time staff spend on routine tasks, help surface relevant insights, and improve consistency across the organisation.
This is particularly valuable in fund finance, where workflows are data-heavy and time-sensitive. By embedding AI into portfolio management, reporting, and middle-office operations, firms can remove friction without sacrificing control.
Building Operational Consistency
The most significant value of AI may be its ability to improve how teams work together. When AI is integrated across departments such as operations, client service, finance, compliance, it improves consistency and shortens the time it takes to make decisions.
In practical terms, this means:
- Reducing time spent on manual data collection
- Improving reporting accuracy and auditability
- Enabling teams to access relevant information more quickly
As firms adopt more data infrastructure and automation tools, the next step is to make those systems easier to use. AI can serve as the interface between the user and the data, providing role-specific insights, guiding next steps, and adapting based on feedback.
Final Thoughts
AI should not be seen as a replacement for people, but as an enhancement to the work they already do. When implemented thoughtfully, it improves decision-making, reduces repetitive work, and allows teams to focus more on areas where expertise matters most.
AI should support, not overtake, your operations. For firms navigating complex investment lifecycles, the real opportunity is to treat AI as part of the infrastructure: always available, increasingly useful, and quietly improving performance behind the scenes.
