100 Conversations on AI in Finance
After more than 100 conversations with private equity, venture capital, and asset management teams, the patterns are clear. Here's why AI adoption has stalled across investment workflows—and what teams actually need.
The teams most frustrated with AI in finance are the ones who've actually tried it. ChatGPT for deal screening. Copilot for memo drafting. Perplexity for market research. They've all fallen short.
After more than 100 conversations with private equity, venture capital, and asset management teams, the patterns are clear. And they explain why AI adoption has stalled across investment workflows.
The Workflow Gap is Real
Investment teams aren't skeptical of AI for private equity or AI for asset management. They're disappointed by it. The tools they've tried don't understand finance-specific context, can't maintain format consistency, and struggle with the document types that matter most.
Here's what we hear repeatedly:
1. Manual Memo Creation is Unsustainable
Investment memos take 8 to 30+ hours. IC packages consume days or weeks. Teams are actively searching for automated memo generation that actually works.
The time investment in creating comprehensive investment memos has become a critical bottleneck for firms looking to scale. Junior analysts spend days synthesizing information from multiple sources, formatting data, and ensuring consistency across documents. Senior partners spend additional hours reviewing and refining these materials. The delays cost firms competitive opportunities.
2. Scaling Means Hiring, or Finding Another Way
Teams of 3 to 6 people handling substantial deal flow face a binary choice: add headcount or leave deals on the table. AI for PE was supposed to solve this. For most, it hasn't.
Small teams managing high deal flow need AI that actually understands investment workflows, not another tool that creates more work than it saves.
The promise of AI was to provide leverage without the overhead of additional headcount. But when the AI tools require extensive prompt engineering, can't maintain institutional knowledge, and produce outputs that need significant rework, they become another task to manage rather than a solution to workload challenges.
3. Generic AI Tools Lack Finance-Specific Context
The feedback is consistent: too basic, not nuanced enough. General-purpose models don't understand investment workflows or financial document analysis.
ChatGPT might excel at summarizing news articles, but it struggles with the specialized terminology, analytical frameworks, and nuanced reasoning required for investment analysis. Understanding the difference between EBITDA and Adjusted EBITDA, recognizing red flags in financial statements, and applying fund-specific investment criteria requires domain expertise that general-purpose models simply don't have.
4. Format Consistency is Non-Negotiable
Firms have specific IC memo formats, LP reporting templates, and brand standards developed over decades. Generic AI outputs don't meet the bar.
Every investment firm has refined their documentation standards over years of practice. These formats aren't arbitrary—they reflect what works for decision-making, what satisfies LP requirements, and what maintains the firm's professional brand. When AI tools produce outputs that require extensive reformatting to match firm standards, they eliminate much of their value proposition.
5. Data Room Synthesis Remains Manual
Teams need AI data room analysis that connects CIMs, term sheets, Q of E reports, and financial statements into coherent analysis. Most still do this by hand.
The real value in due diligence comes from connecting insights across dozens of documents. A claim in the management presentation needs to be verified against the financial model. Revenue projections should be cross-referenced with customer concentration data. Legal terms need to be understood in the context of the company's operational realities. Generic AI tools process documents in isolation, missing the cross-document reasoning that drives investment decisions.
6. Excel Processing is a Gap
Financial model analysis, chart generation, and cash burn calculations require capabilities most AI tools lack. Many only scan the first tab.
Excel files are the lingua franca of finance, but they're notoriously difficult for AI to process effectively. Complex models with multiple tabs, formula dependencies, and scenario analyses require sophisticated parsing. Teams need tools that can understand financial model logic, extract meaningful metrics, and generate analysis that incorporates quantitative insights.
7. Deal Screening Needs Speed
High inbound volume demands AI deal screening that delivers fast pass/fail decisions before committing to deep-dive analysis.
When a firm receives hundreds of opportunities per quarter, the ability to quickly separate signal from noise becomes critical. Teams need AI that can rapidly assess whether a deal meets basic investment criteria, identify potential red flags, and provide enough context for an initial go/no-go decision. Generic tools that require extensive prompt engineering for each deal simply can't deliver the speed required.
8. Portfolio Reporting is a Quarterly Burden
Reports take 2 to 3 weeks per quarter with low utilization. Teams need portfolio reporting automation, not another chatbot.
Portfolio companies submit data in varying formats. Metrics need to be normalized, trends analyzed, and reports generated in firm-specific templates. This quarterly exercise consumes enormous resources but happens infrequently enough that teams can't justify full-time headcount. Most tools can't handle the format variations, institutional knowledge requirements, and quality standards involved.
9. Market Research Workflows are Complex
TAM analysis involves 15+ steps. Validation remains manual, and standardized logic is difficult to repeat across deals.
Total addressable market analysis requires synthesizing data from multiple sources, making assumptions about market dynamics, and validating conclusions against comparable situations. Teams need to repeat this process across dozens of deals while maintaining consistency in methodology. Generic AI tools can help with individual research tasks but can't encode the firm's specific approach to market sizing or maintain the logical consistency required across a portfolio.
10. Security Requirements Constrain Adoption
Many teams are blocked from using consumer AI tools entirely, and the approved alternatives are inadequate for AI in investment management.
Compliance, data security, and regulatory requirements mean that many investment firms can't use consumer AI tools with sensitive deal data. The enterprise alternatives they're offered often lack the capabilities that made consumer AI tools attractive in the first place. Teams need solutions that provide both the advanced AI capabilities they require and the security posture their compliance teams demand.
The Through-Line
Investment teams don't need another chatbot. They need AI infrastructure purpose-built for finance: one that understands financial documents, maintains firm-specific standards, and operates within enterprise security requirements.
The common thread across all these pain points is clear: general-purpose AI tools weren't designed for the specialized requirements of investment workflows. They lack the domain expertise, can't maintain institutional knowledge, and don't provide the security and compliance features that financial firms require.
The solution isn't to abandon AI. Finance-specific workflows need finance-specific tools.
That's what we're building at Deck. If any of this resonates, we'd welcome the conversation.
Ready to see how purpose-built AI can transform your investment workflows? Book a demo to learn how Deck helps private equity, venture capital, and asset management teams work faster while maintaining the quality standards your stakeholders expect.
