# AI Tools Can't See Half Your Data. Here's the Fix > Your AI tools can only use data they can reach. Five practical steps mid-market private markets firms can take now, with tools they already have. - Published: Oct 16, 2026 - Topic: AI & Automation - Source: https://aquata.ai/articles/what-to-do-when-ai-tools-cant-see-your-data --- You deployed Copilot, Claude, or ChatGPT across your team. You expected faster diligence, smarter portfolio monitoring, and instant answers to questions that used to take a junior analyst all afternoon. Then reality set in. You asked the AI to pull together [your exposure across three funds](/use-case-total-portfolio-view), and it came back with a confident answer based on half the picture. You asked it to summarize the latest capital call notices, and it had no idea where to find them. You asked it to reconcile portfolio company financials, and it pointed you to data that was three months old. The tool isn't broken. The data is. For [mid-market private markets firms](/segment-private-markets), the gap between what AI *could* do and what it *actually* does comes down to one stubborn fact: your AI tools can't pull from data they can't reach. Capital call notices sit in PDFs buried in shared drives. Portfolio company financials arrive in Excel templates that look different from every company. Valuation reports live in your fund administrator's portal behind a login wall. The AI sees the tip of the iceberg and confidently reports on that, while the bulk of your investment data remains invisible. This is the AI access gap, and it's the single biggest reason AI underdelivers for private markets firms today. The good news is there are practical steps you can take right now, with tools you already have, to close much of that gap. The better news is when you're ready to solve it permanently, there's a clear path. ## The Real Problem Isn't the AI. It's the Data Beneath It As we explored in [our previous article](/articles/why-ai-tools-cant-see-half-your-data), private markets data doesn't arrive clean. It arrives as capital call notices, financials in Excel, and valuation reports in PDF. The data is fragmented across dozens of sources, each with its own format, its own cadence, and its own definition of what a given number even means. That fragmentation is the root cause behind a broader set of operational pain. McKinsey's Global Private Markets Report 2026 notes that private equity is now a mature industry where alpha will increasingly be "made" through deliberate operational discipline rather than market dynamics, and that firms are actively looking for data and tools to accelerate decades-old processes[1](#source-1). The Financial Times reports that "Excel spreadsheets, email and PDFs are still the most-used tools in private markets," a state of affairs that "really hasn't changed in 10 years," according to Nasdaq's Chief Product Officer for Capital Access Platforms[2](#source-2). The consequence is straightforward. AI tools are trained to query structured, accessible, well-governed data. When you point them at fragmented, unstructured, ungoverned sources, they either fail silently or produce answers that look right but aren't. Research from A-Team Insight found that half of private equity GPs cite the fragmented and unstructured nature of target-company information as their biggest data challenge, and that most are not yet getting meaningful value from AI on core tasks like sourcing deals or tracking portfolio company performance[3](#source-3). In other words, the problem isn't that AI is immature. It's that the data foundation beneath it is broken. Fix the data access, and the AI starts delivering on its promise. ## Five Practical Steps to Close the AI Data Access Gap You don't need to wait for a platform rollout to start closing the gap. Here are five steps a mid-market private markets firm can implement with existing tools and a small team. None of these are permanent fixes, but each one moves you meaningfully closer to giving your AI tools the data they need. ### 1. Build a Lightweight Data Inventory Before you can give AI access to your data, you need to know where it lives. This sounds obvious, but most mid-market firms have never written it down. Start with a simple spreadsheet. List every data source your team touches in a typical quarter: fund administrator portals, custodian reports, capital call notices, portfolio company financials, valuation reports, LP correspondence, side letters, benchmark data, and anything else. For each source, capture four things: where it lives (portal, shared drive, email), what format it arrives in (PDF, Excel, CSV, API), how often it arrives, and who owns it. This inventory becomes the foundation for everything else. You can't connect AI to data you haven't mapped. The Chief Investment Officer reports that private markets data "come in different formats, at different frequencies, with inconsistent naming conventions and uneven levels of detail," and that teams spend disproportionate time "translating information instead of using it"[4](#source-4). A data inventory is the first step toward stopping that translation tax. - **Time investment:** One to two days for a firm with a few funds. - **Tools needed:** A spreadsheet. ### 2. Standardize File Naming and Folder Conventions This is the lowest-effort, highest-return step on the list. If your shared drives look like most firms', you have folders full of files named "Q3 financials FINAL v2 (1).xlsx" and "valuation report - signed.pdf" sitting next to "CCN_March.pdf." No AI tool, and no analyst, can reliably find what it needs in that mess. Agree on a naming convention and enforce it. A simple pattern works: [Fund]_[Entity]_[DocumentType]_[Date]. So a capital call notice becomes FundIII_AcmeCo_CapitalCallNotice_2026Q3.pdf. A portfolio company financial statement becomes FundIII_AcmeCo_Financials_2026Q3.xlsx. Then apply the same logic to your folder structure. Create a consistent hierarchy: Fund, then Portfolio Company, then Document Type, then Year. This gives both your analysts and your AI tools a predictable path to any document. Nasdaq reports that many organizations managing multi-billion-dollar alternative portfolios still rely on "quarterly PDFs, fragmented GP portals, and spreadsheets," leading to "a widening gap between the scale of opportunities and the information available to act on them"[5](#source-5). Standardizing how you name and store those documents is the cheapest way to start narrowing that gap. - **Time investment:** Half a day to agree on conventions, then ongoing discipline. - **Tools needed:** None. ### 3. Create a Central "AI-Ready" Data Store This is where you start turning fragmented data into something AI can actually query. The goal is to consolidate your most important datasets into a single, structured location that your AI tools can reach. For a mid-market firm, this doesn't need to be expensive. If you already use a cloud data warehouse, use it. If you don't, a well-structured setup in Microsoft Fabric, OneLake, or even a carefully organized set of tables in a tool like Airtable can work as a starting point. The key principle is to get core datasets out of individual files and into structured tables. That means extracting the key fields from capital call notices (fund, date, amount, type), portfolio company financials (revenue, EBITDA, cash position, period), and valuation reports (fund, company, valuation, date, methodology) into rows and columns. You don't need every field on day one. Start with the data your team references most often and expand from there. Protiviti's 2025 AI Pulse Survey found that data governance, including data integration and preparation of data for AI models, is a key lever for advancing AI maturity, and that even at advanced stages, issues related to "data reliability, completeness and accessibility persist, often stemming from unclear definitions, inconsistent taxonomies and fragmented aggregation"[6](#source-6). A central data store directly addresses that fragmentation. - **Time investment:** One to two weeks for an initial version covering your top datasets. - **Tools needed:** A cloud data warehouse or structured data environment you already have or can stand up cheaply. ### 4. Establish Basic Data Quality Checks Getting data into one place is only half the battle. If the data is wrong, your AI tools will confidently give you wrong answers. KPMG's research on [data quality for AI](/feature-data-quality) emphasizes that "clean, accurate, and reliable data is the fuel that powers effective, trustworthy AI," and that poor-quality data "erodes trust in insights, leading to flawed decisions" while inflating costs through rework and delays[7](#source-7). You don't need an enterprise data quality framework. You need a few practical checks: - **Reconciliation cadences:** Pick your most critical datasets (NAV, cash positions, capital calls) and reconcile them against source documents on a regular schedule. Monthly is a start. - **Validation rules:** Set up simple checks in your data store. Flag any capital call amount that exceeds the fund's total commitment. Flag any portfolio company financials where revenue dropped more than 50% quarter over quarter. These are basic sanity checks, not sophisticated anomaly detection. - **A single owner per dataset:** Assign one person as the owner of each core dataset. That person is responsible for its accuracy. When the data is wrong, everyone knows who to ask. Academic research on data quality in AI systems reinforces this. A 2025 study on data quality challenges in retrieval-augmented generation found that data quality issues "transform and propagate through the pipeline," meaning errors introduced early compound as they move through AI systems[8](#source-8). Front-loading quality checks is far cheaper than chasing errors downstream. - **Time investment:** A few days to set up initial rules, then a few hours per month to maintain. - **Tools needed:** Your data store's built-in validation features. ### 5. Set Up Governed AI Access With What You Have This is the step that ties it all together. You've mapped your data, standardized your files, built a central store, and added quality checks. Now you need to give your AI tools a controlled path to that data rather than letting them reach into chaos. The [Model Context Protocol](/feature-aquata-mcp) (MCP), open-sourced by Anthropic in November 2024, is an emerging standard for exactly this. Anthropic describes MCP as "a universal, open standard for connecting AI systems with data sources, replacing fragmented integrations with a single protocol"[9](#source-9). AWS notes that MCP "functions as a universal translator, enabling seamless dialogue between language models and the diverse systems where your valuable information resides"[10](#source-10). For a mid-market firm, the practical version of this looks like: connect your AI tools (Copilot, Claude, or others) to your central data store through an API or MCP-style connector rather than pointing them at individual files and folders. This gives the AI a single, governed entry point to your data. You control what it can see, and you get a more consistent result because the AI is querying structured, quality-checked data instead of scraping random PDFs. If MCP feels like too much to set up internally, start simpler. Many AI tools can connect to a SQL database or a structured API directly. The point is to give the AI a clean, governed path rather than a scattered one. - **Time investment:** One to two weeks depending on your stack. - **Tools needed:** Your AI tool's connector or API capabilities, plus your central data store. **How Private Markets Firms Can Get in Shape for AI** Take your quality checks further with six measurable data quality dimensions and AI use cases for NAV, cash and investor inquiries. ## The Limits of the DIY Approach These five steps will get you meaningfully closer to making your data AI-ready, but be honest about the limits. Every step above requires manual effort to maintain. Someone has to update the data inventory when you add a new fund. Someone has to enforce the naming convention when a new analyst joins. Someone has to extract fields from the latest round of PDF capital call notices and load them into your central store. Someone has to run the reconciliation checks and chase down the breaks. For a mid-market firm, that "someone" is usually a small team that would rather be doing deals, building investor relationships, and finding alpha. The Huble AI Data Readiness Report found that 45% of organizations report poor data structure as the leading barrier to AI success, with "fragmented, unstructured information stalling intelligent automation before it even begins"[11](#source-11). Your DIY data store helps, but it doesn't eliminate the structural problem: private markets data keeps arriving in unstructured, inconsistent forms, and someone has to keep taming it. State Street's 2025 Private Markets Study, as reported by Unlisted Intel, found that only 8% of institutions have actually implemented GenAI solutions in production for private markets, while 41% remain at the pre-investment planning stage[12](#source-12). The gap between intent and production is largely a data problem. The firms that close it will pull ahead. The firms that don't will keep spending their time on data wrangling instead of deal-making. > The gap between intent and production is largely a data problem. ## When You're Ready to Solve It for Good The steps above are makeshift solutions. They buy you time and incremental improvement. At some point, the maintenance burden of a DIY data infrastructure becomes its own problem, especially as your firm grows across funds and vintages and the data volume compounds. That's where you need Aquata. Aquata makes data AI-ready by centralizing, standardizing, and quality-assuring it. Instead of building and maintaining your own central data store, Aquata consolidates all your investment data, including the unstructured data that private markets firms deal with every day, into a [single source of truth](/use-case-central-data-layer). Capital call notices, borrower financials, valuation reports, and portfolio company data that used to live in PDFs, spreadsheets, and portals gets extracted, normalized, and validated into one governed connected data layer. That means AI tools are no longer working blind. They are working with clean, unified data. The Aquata MCP Server gives enterprise AI applications, including Copilot and Claude, standardized, governed access to Aquata data and catalog search. Instead of AI tools trying to reach fragmented, ungoverned data sources across your firm, they connect to a single, trusted data layer. This eliminates blind spots and closes access gaps. AI gets the full picture, not half of it. For teams that want AI built directly into their workflows, Aquata ships with in-app AI, Arcesium Intelligence. It provides domain-aware AI tied to Aquata's robust data governance, so you have a full audit trail to back up every result the AI generates. You can ask a question in plain language, get a grounded answer, and trace exactly where that answer came from. No guessing. No hallucinated numbers. No defending outputs you can’t explain. The promise of AI in private markets is real, but it only materializes when the data beneath it is accessible, standardized, and governed. You can build a makeshift bridge to get there. Alternatively, Aquata can do it for you and help you operationalize AI, so your team can spend time on deals and investor relationships, not data wrangling. For a mid-market firm looking to transform fragmented data into action, that's the difference between fighting the data problem every quarter and putting it behind you for good. ## Sources - Global Private Markets Report 2026, McKinsey: https://www.mckinsey.com/industries/private-capital/our-insights/global-private-markets-report - Are private markets ready for the data age?, Financial Times: https://www.ft.com/content/0352fb0f-66dd-4ff2-920b-861686f16576 - Private Market GPs Unhappy with Data Availability, Unsure About AI Value, A-Team Insight: https://a-teaminsight.com/blog/private-market-gps-unhappy-with-data-availability-unsure-about-ai-value/ - Private Markets Don't Need More Data; They Need Data That Connect, Chief Investment Officer: https://www.ai-cio.com/news/private-markets-dont-need-more-data-they-need-data-that-connect/ - Private Markets Data Expansion: What LPs and GPs Need to Know, Nasdaq: https://www.nasdaq.com/articles/analytics/private-markets-data - From Data Confusion to AI Confidence: 2025 AI Pulse Survey, Protiviti: https://www.protiviti.com/sites/default/files/2025-08/2025-ai-pulse-survey-v2-protiviti_global.pdf - The Data Quality Trifecta, KPMG: https://assets.kpmg.com/content/dam/kpmgsites/in/pdf/2025/10/data-quality-trifecta.pdf - Data Quality Challenges in Retrieval-Augmented Generation (arXiv:2510.00552), arXiv: https://arxiv.org/abs/2510.00552 - Introducing the Model Context Protocol, Anthropic: https://www.anthropic.com/research/model-context-protocol - Unlocking the power of Model Context Protocol (MCP) on AWS, AWS Machine Learning Blog: https://aws.amazon.com/blogs/machine-learning/unlocking-the-power-of-model-context-protocol-mcp-on-aws/ - The AI Data Readiness Report 2025, Huble: https://213255.fs1.hubspotusercontent-na1.net/hubfs/213255/The%20AI%20Data%20Readiness%20Report%202025.pdf - GenAI in private markets unlocks unstructured data, The Unlisted Intel: https://unlistedintel.com/blogs/genai-unstructured-data-and-the-next-divide-in-private-markets/