AI for Excel in Private Equity: The Complete Buyer's Guide

1. Why Excel Is Still The PE Operating System

Every two or three years, someone declares Excel dead in finance. They are always wrong.

An analyst at a $2B PE firm builds an LBO in Excel, sends it to the VP, who tweaks it in Excel, who sends it to the partner, who reviews it in Excel. The IC memo references Excel exhibits. The data room provides Excel files. The portfolio company sends monthly financials in Excel. The fund's quarterly LP letter is built from Excel.

According to Bain's 2025 Global Private Equity Report, the median PE deal team still spends 40 to 50% of its hours inside Excel. Replacing it would mean retraining every professional, rewriting every model, and rebuilding every reporting workflow. Nobody is doing that.

The right question is not "should we move off Excel?" The right question is "how do we make Excel faster?" That is what AI for Excel is about. Not replacing the spreadsheet, making the work inside it less mechanical.

The economics are obvious. If a deal team spends 50% of its hours in Excel and you can shave 20% off that time with AI, you have given each professional one full day a week back. At a 10-person deal team and $300K average loaded cost, that is $1.5M of recovered productivity per year. The math justifies the spend before you even count the quality improvements.

2. What "AI for Excel" Actually Means in 2026

"AI for Excel" gets used loosely. There are five distinct things it can do, and most tools only handle two or three of them.

1. Formula generation. You describe what you want in plain English ("calculate IRR assuming entry multiple of 8x and exit multiple of 11x with this cash flow"), AI writes the formula. Useful for the 70% of analysts who can read complex formulas but cannot write them quickly.

2. Data extraction into Excel. AI reads a CIM, an annual report, or a data room document and pulls the financial data into a structured Excel format. This is where Daloopa lives. Saves 60 to 80% of the spreading time on financial DD.

3. Model auditing and error detection. AI scans your existing model, finds formula inconsistencies, broken references, and circular logic. Useful for the universal panic of "did this model break when we made the change at 11pm last night?"

4. Narrative generation from Excel data. You have a fully-built model. AI reads it and writes the IC memo paragraphs that describe what the model says. Compresses 4 hours of writing into 30 minutes of editing.

5. Automated workflows on Excel files. AI agents that take action: open this file, update these inputs from the latest portfolio company report, refresh the model, save a new version, send to the analyst. This is the newest category and the highest-impact one.

Different tools cover different combinations. Microsoft Copilot is strong on 1 and 4. Daloopa owns 2 cold. Specialized add-ins like Numerous AI focus on 1. Custom agents are how you get all five integrated. Knowing which thing you need most is the start of any tool selection.

3. The Four Categories of AI Excel Tools

Tools fall into four buckets. Picking the wrong category is the most expensive mistake we see.

The four categories solve different problems. The right firm-wide approach typically combines two or three: M365 Copilot at the team level for general productivity, plus a PE-specific tool for the high-value workflows (financial modeling, LBO building), plus a custom agent for the firm-specific quirks no off-the-shelf tool can match.

4. Microsoft 365 Copilot

If your firm runs Microsoft 365, Copilot is the obvious starting point. The integration is native, the security model is consistent with your existing Microsoft tenancy, and the cost is in the seat license you already pay for, plus the Copilot add-on.

What Copilot does well. Natural language queries against an existing model ("what is the IRR if exit multiple drops to 9x?"), summarizing what is in a sheet, generating new formulas from descriptions, and creating charts from data. It also reads across your Word documents, emails, and Teams chats, which is useful for cross-functional work.

What Copilot does poorly. Anything that requires understanding finance-specific concepts. It does not know what an LBO model looks like, what a quality of earnings report should contain, or what an IC memo's standard sections are. When you ask it to "build me a model", you get a generic spreadsheet, not a PE model. When you ask it to read a CIM, it does not know to extract the things a deal team cares about.

Where Copilot fits. The everyday productivity layer. Helping analysts move faster on routine tasks: writing formulas, formatting outputs, summarizing for partners, creating charts. Not the tool you would buy for the highest-value workflows, but the right baseline for a firm that already runs M365.

Security. Microsoft Copilot operates within your existing M365 tenancy. Your data stays in your tenant. It does not train Microsoft's foundation models on your content. This is the strongest security posture among the major options for general-purpose tools.

Practical recommendation: roll Copilot out to the deal team and the operating team. Cost is bounded, the security risk is low, and adoption tends to be high because the integration is invisible. Skip Copilot only if your firm is not on M365.

5. Specialized AI Add-Ins (Numerous, Formulabot, Excelly-AI)

Specialized AI add-ins are the cheapest tier. They install as Excel add-ins, focus on cell-level and formula-level help, and price at $10 to $30 per user per month.

Numerous AI. Probably the most-used tool in this category among finance professionals. Lets you write formulas in plain English and run AI prompts on cell ranges (e.g., "categorize each row in column A by sector"). Strong at the "I know what I want, just write the formula" use case.

Formulabot. Same general pitch, slightly different feature mix. Free tier exists, which is unusual in this market. Worth trying as a starting point before paying for any of the others.

Excelly-AI. Newer entrant focused on formula generation and explanation. Useful for analysts learning Excel deeply.

What this category does well. Cell-level and formula-level help. Faster than asking ChatGPT and pasting the result back into Excel. Cheap enough that you can roll it out to the entire deal team without an IC battle.

What this category does poorly. Anything workflow-level. They do not understand that you are building an LBO model. They do not extract data from a CIM into a structured spreadsheet. They do not generate IC memo paragraphs from your model output. They are productivity tools for individual cells, not workflow tools for the deal lifecycle.

Security caveat. Most of these tools route your prompts and the surrounding data through OpenAI or Anthropic via the vendor's API. That is fine for non-confidential work. For an LBO model with confidential deal data, read the vendor's data handling carefully. The cheapest tier of these tools is also the riskiest tier from a confidentiality standpoint.

Practical recommendation: useful as a complement to Microsoft Copilot, not a replacement. Pair them: Copilot for the integrated workflow stuff, Numerous AI (or Formulabot) for the cell-level formula generation. Total cost stays under $50 per user per month. Watch the data handling on confidential models.

6. PE-Specific Modeling Tools (Rogo, Daloopa, Tactyc, Mosaic)

This is where the real value lives for PE deal teams. PE-specific tools are built around the actual workflows: LBO modeling, financial spreading, comps analysis, fund-level performance.

Rogo. The most prominent PE-focused AI platform. Operates partly in and partly outside Excel. Their value proposition: AI agents that work the way a PE analyst works. Read CIMs, build comps, populate Excel models, draft memo paragraphs. Used by several mid-market PE firms as a primary deal team tool. Pricing is enterprise but the time savings are real if your team uses it.

Daloopa. The category leader for financial data extraction into Excel. Pulls financials from public filings, earnings transcripts, and press releases into structured spreadsheets. Saves 60 to 80% of the time analysts spend on financial spreading. Stronger for public-comp work and less useful for private deal financials, but the public data is what you need for valuation work anyway.

Tactyc. Originally built for venture portfolio modeling and LP reporting, but increasingly used by PE firms for fund-level performance analytics. Strong at the IRR/MOIC/DPI/TVPI calculations and scenario modeling that GPs do for LPs.

Mosaic. FP&A-focused with strong analytics on top of Excel data. More relevant for portfolio company CFOs than for the GP-level deal team. Worth flagging for the operating team.

Causal. A modeling environment that sits adjacent to Excel and pitches itself as a faster way to build sensitivity-driven models. Some PE shops use it for scenario analysis, but most stick with Excel for the actual model and use Causal for reporting layers.

All five tools cost meaningfully more than Microsoft Copilot or specialized add-ins. They earn that cost on specific use cases. The question is which use cases your firm runs at high volume. If you spread financials on every deal, Daloopa pays for itself in a quarter. If your deal team writes IC memos on 10 to 20 deals a year, Rogo's memo-building features matter. If you obsess over fund-level performance reporting, Tactyc might be the one.

None of these are a substitute for Excel itself. They sit alongside Excel and compress specific workflows. The key buying decision: which workflow is your bottleneck? Pick the tool that addresses that specific bottleneck. Resist the temptation to buy three of them at once.

7. Custom Excel Agents

The fastest-growing category in 2026. Custom Excel agents are AI workflows built specifically for your firm's models, your firm's data sources, and your firm's reporting templates.

What they do. Anything you can describe as a deterministic Excel workflow. Pull this month's portfolio company financials from email, populate the monthly tracking model, refresh the formulas, generate the operating partner's review packet, save it to SharePoint. Or: read the latest CIM from the data room, populate the standardized screening model with extracted financials, score the deal against the firm's investment criteria, send a one-page summary to the deal team's Slack.

Why custom is now feasible. Frontier model APIs (OpenAI, Anthropic, Azure OpenAI) have matured to the point where building these workflows takes weeks, not months. The orchestration layer is the work, not the AI itself. A small focused team can deliver a working agent in 6 to 10 weeks, deployed in your own infrastructure.

When custom makes sense. Three triggers. First: you have a workflow you do at high volume (every deal, every week, every month) that is mechanical but the off-the-shelf tools cannot quite handle the specifics. Second: data sensitivity is high enough that vendor-hosted tools are uncomfortable for legal or compliance. Third: you want the workflow to integrate with internal systems (CRM, fund admin, portfolio monitoring) that vendors do not support natively.

When custom does not make sense. If you only run the workflow occasionally. If your firm does not have the IT or AI infrastructure to maintain a custom build. If an off-the-shelf tool covers 80% of what you need, the cost-benefit on building the last 20% rarely works out.

The economics: a custom agent typically costs $50K to $300K to build, plus 15 to 25% of that annually for maintenance and model updates. The ROI case is usually built on analyst hours saved and the specific reliability gains from a workflow that is tuned to your data shapes. We build these as Custom Build engagements, with the AI Deal Screener and Board Pack Automation being the most common requested workflows.

8. The PE-Specific Use Cases That Matter

Not all Excel work in PE is equal. Here is where AI moves the needle.

Financial spreading and EBITDA normalization. The grindiest part of any DD process. Spreading 3 to 5 years of historical financials, normalizing across periods, identifying and categorizing add-backs. AI compresses 4 to 6 hours per deal to 30 to 45 minutes. Daloopa or a custom build are the fits here.

LBO model construction. Building the base LBO from a target template, plugging in the deal-specific assumptions, running base/upside/downside cases. AI can scaffold the model from the screening data, but the deal team still drives the assumptions. Rogo handles parts of this; custom agents handle the firm-specific template.

Comps and valuation analysis. Pulling comparable transactions and trading comps, normalizing the metrics, building the valuation summary. Daloopa is strong here. Excel-native add-ins are weak.

Portfolio monitoring rollups. Consolidating monthly financials from 15 to 25 portfolio companies, each in slightly different formats, into a single fund-level view. The mechanical work is enormous. Custom agents that read incoming reports and populate the monitoring spreadsheet save 50 to 100 hours per month for a typical mid-market fund.

LP reporting calculations. IRR, MOIC, DPI, TVPI, J-curve projections, attribution analysis. The math is mechanical, but errors are unacceptable. Tactyc and similar tools deliver real value here. Or build it as a controlled custom agent with audit trails.

Scenario analysis. Sensitivity tables, base/upside/downside variants. Microsoft Copilot or Numerous AI handle this well. The work was always partially mechanical, AI just makes it faster.

If you are picking one workflow to start with, financial spreading is the highest ROI. The work is mechanical, the time savings are large, the accuracy is verifiable. If your team is doing more than 20 deals a year that involve spreading, the tooling pays for itself in the first quarter.

9. Security: Excel + AI = Your Most Sensitive Data

The Excel files in a PE firm hold the most sensitive data the firm produces. Deal economics, sponsor commitments, portfolio company financials, fund-level returns. Adding AI to Excel without thinking about security is asking for a confidentiality breach.

Three security layers matter.

Where the data goes. Microsoft Copilot processes data inside your M365 tenancy. The data stays in your tenant. PE-specific tools and most third-party add-ins route the data through their own infrastructure. Read the data flow diagram. Understand which servers your model touches.

Whether the data is used to train models. Microsoft does not train its foundation models on your tenant data. OpenAI and Anthropic do not train on enterprise API data either, as a contractual default. Some smaller add-in vendors are less clear. If a tool's terms allow training on your inputs, do not use it for confidential models.

Who has access during processing. SOC 2 Type II is the floor. Beyond that, ask about the access controls within the vendor's environment. Engineering teams routinely have access to production data for debugging at smaller vendors. At enterprise vendors with mature data governance, that access is locked down.

Practical guidance. For confidential deal models: use Microsoft Copilot natively in your tenant, or a custom agent in your own Azure/AWS environment. For non-confidential work (general analytics, internal modeling, market research): the broader tool set is fine. For the gray zone (signed NDA but not yet IC-approved): default to the more secure option.

Our broader take on this is in the AI Security and Data Governance for PE guide.

10. Evaluation Framework

Six questions to ask any AI Excel vendor before committing budget.

1. What is the exact use case I am buying for? Be specific. "Faster modeling" is too vague. "Cut financial spreading time on private DD from 5 hours to under 1 hour per deal" is the right specificity. The vendor should be able to demo on that exact use case with your data.

2. Can I run a 30-day pilot with two analysts on real workflows? A live pilot is the only way to evaluate. Vendor demos are optimized for showing well. Pilot data shows what the tool does on a Tuesday afternoon when the analyst is tired.

3. What does data flow look like for my Excel files? Where do they go? Who touches them? How is the data deleted after processing? Anything less than a clean answer here is a flag.

4. How does this integrate with my CRM, fund admin, and reporting workflow? Standalone tools are half a solution. The output needs to land where the deal team works.

5. What happens when Microsoft updates Excel? Excel evolves. Some add-ins break with each major release. Ask the vendor about their update cadence and how they handle breaking changes.

6. What is the total 3-year cost? License plus implementation plus internal time. Most pricing pages tell you only the first number. Ask about the rest before signing.

The vendors who pass all six questions are the ones worth piloting. The ones who hedge on three or more are not enterprise-ready, no matter how slick the demo is.

11. Where to Start

If you are starting from no AI for Excel today, here is the path that works for a typical mid-market PE firm.

Month 1. Roll out Microsoft 365 Copilot to the deal team and operating team. Cost is bounded. Adoption tends to be high. Establishes a baseline of AI-assisted productivity. Pair with Numerous AI or Formulabot for cell-level help.

Month 2-3. Pilot one PE-specific tool against your highest-volume workflow. If you spread financials on every deal, pilot Daloopa. If your bottleneck is memo writing, pilot Rogo. If you obsess over LP reporting, pilot Tactyc. Measure time saved on real deals, not on the demo set.

Month 4-6. Decide on the PE-specific tool. Roll out to the deal team. Train the analysts thoroughly. Build the new workflow into the team's standard operating procedure.

Month 7+. Look at custom agents for the workflows that off-the-shelf tools cannot quite cover. Most firms find one or two of these in their first year.

If you want help running this, our Discovery Sprint covers AI-for-Excel evaluation as part of a broader assessment of where AI delivers the most value across your firm. We have run this evaluation for PE firms ranging from $500M to $5B AUM. The right answer is rarely the same.