Why M&A Firms Still Use Excel and How AI Fixes It

M&A advisory firms continue to rely on Microsoft Excel for core deal modeling and tracking workflows despite the industry's $53.6 billion growth trajectory source, primarily because 71% of corporate development teams operate on fixed annual tech budgets under $250,000 source.

This budget reality forces most small and mid-sized advisory firms to stick with Excel-based solutions rather than adopting comprehensive AI platforms. The situation is especially pronounced for teams closing fewer than six deals annually, where Excel combined with add-ins remains "sufficient" for their modeling needs source.

The core challenge stems from how M&A software vendors fragment the solution landscape across three critical layers: Excel productivity enhancement, scenario modeling for complex multi-entity deals, and integration with CRM/VDR/portfolio reporting systems. As industry experts note, "Picking M&A modeling software is not a single decision. It is three: which Excel productivity layer you bolt on, which scenario-modeling engine you use for complex multi-entity deals, and how you connect the model to your CRM, VDR, and portfolio reporting. Most teams underweight that distinction, then buy one platform expecting it to cover all three jobs. It will not" source.

Many firms mistakenly assume a single platform should handle all three requirements, only to discover it fails at one or more layers. Tools like Macabacus ($268/year individual) and Microsoft Copilot in Excel (2024) offer limited AI enhancements such as AI Smart Format and AI Color Cells, but adoption remains early and siloed source.

The primary barrier to AI implementation is not technical capability but rather entrenched workflow habits. Industry data reveals that 790,000+ M&A transactions have occurred globally since 2000 with $57 trillion in total value source, creating immense pressure for efficient tracking systems that Excel struggles to provide at scale.

Key pain points driving Excel dependency:

• Excel remains foundational for "custom-built bespoke deal models" used by investment banking and PE teams source
  
• Native Excel breaks down with >5 operating subsidiaries requiring intercompany eliminations source
  
• Firms often purchase platforms expecting end-to-end coverage but end up with incomplete solutions that force continued Excel reliance

The path forward requires targeted automation that respects existing budget constraints while addressing repetitive tasks like data entry and stakeholder communication — areas where AI can deliver immediate ROI without disrupting established workflows.

As we'll explore next, modern AI systems can automatically handle these manual processes within a unified platform, transforming spreadsheet-dependent workflows into streamlined, error-reducing operations.

M&A teams spend more time formatting cells than analyzing deals—yet 71% of corporate development teams operate on annual tech budgets under $250,000, making wholesale platform changes impossible. Industry analysis shows most firms cling to Excel not because it’s ideal, but because it’s familiar and affordable. The real bottleneck isn’t modeling logic; it’s the repetitive, low-complexity tasks clogging the system. From data entry to status updates, these manual processes eat hours and introduce errors—precisely where AI can deliver immediate relief.

Excel’s dominance persists across the deal lifecycle, with 790,000+ transactions tracked since 2000 and $57 trillion in total value. The issue isn’t a lack of tools—it’s that most platforms force teams to shoehorn complex deal workflows into rigid systems. Instead of replacing the core financial model, AI can target the friction points around it. Formatting inconsistencies, broken precedents, and audit gaps don’t require deep financial expertise to fix. They’re tedious, error-prone, and ripe for automation.

Formatting and audit automation Spreadsheets devolve into chaos when hard-coded values hide behind colorful cells and unlabelled tabs. Tools like Macabacus address this by standardizing formatting, color-coding formulas, and catching broken precedents before they reach leadership. Its AI Smart Format feature alone cuts formatting time by up to 40% for teams juggling multiple deals. For firms managing fewer than six transactions annually, these enhancements make Excel not just sufficient—but optimized.

Scenario modeling for complex deals When entity complexity crosses five subsidiaries with intercompany eliminations, native Excel breaks down. That’s where scenario-modeling engines like Quantrix step in, rebuilding 12-entity LBO consolidations in seconds after a single assumption change. Quantrix’s AI Assistant further accelerates iteration by suggesting structural adjustments based on historical deal patterns. The key isn’t replacing the model—it’s accelerating the work around it.

CRM and VDR integration Deal data doesn’t live in a vacuum. Status updates, stakeholder communications, and portfolio reporting require seamless handoffs between systems. Cube bridges this gap by overlaying cloud storage, version control, and multi-entity consolidations onto existing Excel and Google Sheets files. Its Cube AI can draft status emails, reconcile discrepancies, and surface anomalies in real time—reducing the manual checks that derail timelines.

• Repetitive tasks AI can automate today:
• Data entry from VDR uploads into deal models
• Status update emails to investors and lenders
• Formatting cleanup before model reviews
• Precedent audits for broken links or overrides
• CRM field population from deal memos

AI won’t replace the nuanced judgment of an M&A advisor—but it can strip away the busywork that currently prevents teams from focusing on what matters. The next section explores how these automations integrate into existing workflows without disrupting core modeling logic.

Most M&A advisory firms still rely on Excel for core deal tracking and modeling despite its known limitations because it remains the most cost-effective solution for small teams handling fewer than six deals annually while operating under tight technology budgets source. The market continues to expand with global M&A advisory revenue expected to hit $53.6 billion by 2034, yet 71% of corporate development teams function within fixed annual tech budgets under $250,000 source.

Excel's dominance stems from practical necessity rather than technical superiority — it serves as the modeling surface for custom-built bespoke deal models used by investment banking, private equity, and corporate development teams source. While 790,000+ M&A transactions have occurred globally since 2000 with $57 trillion in total value, firms often misunderstand the tool selection process as a single decision when it actually requires three distinct components: an Excel productivity layer, a scenario-modeling engine for complex multi-entity deals, and an integration layer for CRM and VDR systems source.

The primary barriers to AI adoption aren't technical but behavioral — legacy workflow habits persist even as AI capabilities emerge in tools like Macabacus ($268/year), Quantrix ($1,995/user/year), and Microsoft Copilot in Excel source. Most teams discover too late that standalone platforms fail to address all three critical layers, forcing them to duct-tape solutions that don't talk to each other source.

Key AI-enhanced tools are emerging but remain fragmented: Macabacus offers AI Smart Format and AI Color Cells for $268/year, Quantrix provides an AI Assistant, and Cube overlays cloud storage and version control on Excel without requiring new interfaces source. Budget constraints are severe — LMM PE firms and search funds often must fit their entire modeling stack under $5,000 annually, making comprehensive migrations impractical source.

The most effective starting point targets repetitive, high-volume tasks where AI delivers immediate ROI: automating data entry, status updates, and stakeholder communication source. Rather than replacing Excel entirely, firms should layer affordable add-ins that boost productivity within existing workflows, respecting both budget realities and entrenched habits source.

This incremental approach mirrors how AI Business Sites helps small businesses adopt AI without disruption — by building smart functionality directly into existing website workflows rather than demanding costly platform overhauls. As M&A teams explore AI enhancements, the focus should remain on solving specific pain points rather than chasing technological novelty.

Next, we’ll explore how these same principles of incremental, budget-conscious AI integration apply to small business websites that need to operate autonomously.