What is the biggest challenge investors face with AI adoption?
Most investment teams have tried AI. Few have made it part of how work actually gets done.
The challenge is not access to tools: it is knowing where to start. Most investment teams have experimented with AI, but few have embedded it into how work actually gets done. McKinsey’s 2024 Global Survey on AI found that 72% of organizations use AI in at least one business function, yet most struggle to move beyond isolated pilots. The firms seeing real results did not launch transformation programs. They picked one painful, recurring workflow, fixed it, measured the outcome, and expanded from there.
This guide is organized the same way: start with what works, build from there.
What is the most important prerequisite before using AI in investment workflows?
A structured data foundation. AI in private markets is only as useful as the data it can work with. If portfolio data is scattered across spreadsheets, email threads, and slide decks, AI amplifies the disorder rather than resolving it. The prerequisite is centralizing portfolio and fund data into a consistent source of truth, one that AI can query to generate drafts, flag inconsistencies, and surface risks reliably.
This requirement is reinforced by industry direction. Bodies like ILPA and Invest Europe are standardizing reporting templates and guidelines precisely because comparable, structured data is becoming a baseline expectation for GPs, not an advanced capability.
How should investment teams prioritize AI use cases?
By workflow maturity, not hype. There are three tiers:
Proven
reliable, repeatable value with low implementation complexity. Start here.
Gaining Ground
delivering value now, but the tooling is still evolving. Worth piloting in parallel.
Frontier
high potential, but dependent on data maturity and organizational readiness. Experiment selectively.
LP Reporting: Where AI delivers the fastest ROI in private markets
Quarterly LP reporting is one of the most operationally demanding cycles in private markets. It requires accuracy, consistency, narrative quality, and regulatory compliance: all under deadline pressure.
What can AI do for LP reporting today?
Draft narrative commentary. When grounded in clean portfolio data and prior report context, AI produces usable first-pass drafts for fund overviews and portfolio summaries. The value is not automation; it is compressing hours of blank-page drafting into review and refinement. Teams using Rundit’s AI-powered LP report features cut report compilation time by close to 50%.
Run consistency and QA checks. AI flags mismatched figures, inconsistent terminology, and formatting deviations across report sections. This matters more as LP reporting becomes standardized and auditable, particularly with ILPA’s Reporting Template v2.0 now in effect for Q1 2026.
What is improving in LP reporting AI?
Predictive fund performance and reserve modeling. This requires well-structured historical data, consistent valuation methodology, and external market signals. It is viable only for teams with mature data pipelines, because the quality of predictions is directly tied to the quality of inputs.
What is still emerging?
Predictive fund performance and reserve modeling. This requires well-structured historical data, consistent valuation methodology, and external market signals. It is viable only for teams with mature data pipelines, because the quality of predictions is directly tied to the quality of inputs.
Portfolio Data Collection: Eliminating the manual follow-up cycle
What is the core problem AI solves in portfolio data collection?
The standard cycle: data request, follow-up, version conflict, late submission, and manual consolidation, is one of the most time-consuming and error-prone workflows in venture capital and private equity operations. AI addresses each stage of this cycle.
Which data collection tasks are AI-ready now?
Automated requests and follow-ups. Structured workflows that send requests, track submissions, and reduce the friction of manual chasing are straightforward to deploy and easy to measure in time saved.
Metric extraction from unstructured updates. Founders submit updates as emails, PDFs, and attachments. AI extracts key figures and maps them to a standardized format, eliminating manual data entry. Rundit handles this at the portfolio level, pulling KPIs directly from founder communications and normalizing the format across all companies so portfolio-wide comparisons become meaningful. Read more: Email data extractor by Rundit.
Automated data quality checks. AI is becoming more reliable at detecting anomalies and definition drift: ARR vs. MRR inconsistencies, sudden margin swings, unit mismatches. Accuracy compounds as the model processes more historical submissions from each company.
Founder update summarization at scale. Instead of reading 30 long updates line by line, AI produces consistent summaries that surface deltas, risks, and follow-up items worth attention.
What is on the horizon?
Continuous data collection. The shift from quarterly snapshots to near real-time monitoring via integrations with financial systems and product analytics tools is underway but still early-stage.
Portfolio Monitoring: From spreadsheet maintenance to decision support
What does AI-powered portfolio monitoring actually mean in practice?
It means knowing which companies are drifting, which are compounding, and what the fund looks like against its objectives, without rebuilding the picture manually each week.
Centralized dashboards that stay current. A live, consolidated view of portfolio performance and exposures turns monitoring from a data assembly task into actual investment decision-making work.
Real-time company intelligence. Before IC meetings or follow-on decisions, teams typically spend hours compiling news, founder background, and recent developments from fragmented sources. Rundit’s AI surfaces real-time company news, founder profiles, and critical context automatically, so teams arrive informed without the manual prep work.
What risk signals can AI detect earlier?
AI can surface patterns before they become problems: growth deceleration, rising burn rates, declining engagement metrics. Earlier visibility creates more room for intervention.
What is still frontier territory?
AI-assisted portfolio strategy and scenario planning. The direction is clear: AI contributing to portfolio construction decisions and reserve modeling. But it is early, and it works only with rigorous governance and human accountability at every decision point.
How can investment teams implement AI in 30 days?
A four-week sprint works better than a roadmap.
Week 1: Choose one workflow. Define success. Pick the most painful recurring task: report drafting, data collection, or update summarization. Set a measurable target: hours saved, cycle time reduced, error rate cut.
Week 2: Standardize the inputs. Agree on metric definitions: ARR vs. MRR, runway, headcount. Build a simple data dictionary. This step determines whether AI compounds in value or stalls. Invest Europe’s Investor Reporting Guidelines are a practical reference for standardizing what gets tracked.
Week 3: Deploy AI with human review built in. Version history, approval steps, and human sign-off before anything goes out. AI drafts; humans approve. Non-negotiable.
Week 4: Make it repeatable. Convert the workflow into a checklist. Do not expand until the first workflow is fully routine.
What governance principles should investment teams follow when using AI?
Data privacy: define explicitly which data types are permitted into AI tools
Auditability: log what changed, when, and by whom
Human accountability: AI generates drafts; humans approve outputs
Policy documentation: specify allowed use cases and prohibited data categories
What separates investment teams that succeed with AI from those that do not?
They do not try to “adopt AI.” They fix one specific workflow and measure it.
McKinsey’s research is consistent on this point: organizations see better outcomes when AI is tied to a clear objective; efficiency plus quality, not novelty for its own sake. And when workflows are redesigned so AI outputs are reviewed, governed, and reused rather than recreated from scratch each cycle.
The three highest-return starting points for investment teams are LP report drafting and QA, founder update ingestion and structured summarization, and portfolio monitoring with anomaly flagging.
The underlying condition for all three is the same: structured, centralized portfolio data. That foundation determines whether AI delivers reliable, compounding value or just adds another layer of complexity.
Rundit is portfolio management software for VC and PE investors, designed to centralize portfolio data, automate reporting, and apply AI to the workflows that consume the most time. → Book a Rundit demoto see how it fits your team’s workflow.
