Volume 1 - 2026
The Visible AI Sentiment Report
Introduction
AI is transforming the way we work. At least we think it is.
AI is transforming the way we work. At least we think it is transforming the way we work. The AI hype has transformed into full-on staying power. Teams, companies, and industries are doing their best to stay ahead of the game, but most have yet to cut through the noise to find what truly has staying power versus what is merely a flash in the pan.
The AI Sentiment Report gives VC teams an up-to-date read on hype versus signal. Each edition features perspectives from fellow investors, product insights from across our portfolio, and market data worth paying attention to.
The Disconnect
Confidence is high. Execution is lagging.
100% of survey respondents are using AI at their firm in some capacity. The last 12 months have felt like Moore's Law on steroids. Deciphering what to do with the new tools, workflows, and power has been a struggle for most.
Very confident that AI will transform their firm in the next 12 months
Built zero new workflows in the past 90 days — despite strong confidence in AI's impact
Of those same confident respondents adopted zero new tools in the past 90 days
How is AI adopted in your operational workflows?
Core to
multiple
workflows
Used regularly
by most
members
Used by
specific teams
only
Being tested or
piloted
Not meaningfully
adopted
Confidence Score by Adoption Depth
Average 1-10 score: Will AI transform firm ops in 12 months?
AI is core to multiple workflows
AI is used by specific teams only
Depth of adoption drives confidence.
What is your biggest barrier to deeper AI adoption?
Teams are stalling, not sprinting, on AI
"New shiny AI tool every month. Tough to decipher who has staying power and where we should be investing time."
Survey Participant
"Before adopting new tools, new processes and workflows should be sketched out and clear use cases defined and tested out."
Pedro Garcia of Zacua Ventures
Early AI Implementation
Progress is real, but it's uneven.
57% of firms report AI is used regularly or is core to their operational workflows. Yet when it comes to impact, only 14% say their operational efficiency has drastically improved, while 47% say it's moderately improved. Firms are in motion, but most haven't crossed the threshold from hype to improvement.
How has AI impacted your firm's overall operational efficiency?
Only 14% say efficiency drastically improved. 47% say moderately. Most firms are in motion but haven't crossed the threshold.
Where has AI impacted your firm ops the most?
AI Use Cases — Portfolio Firm Survey
Where firms are applying AI today
Internal ops
Cap table, legal drafting, modeling
Data Collection
Sourcing, research, aggregation
Marketing & Comms
Content, outreach, newsletters
LP Reporting
Updates, performance summaries
Portfolio & Analysis
Cross-portfolio insights
Benchmark Performance
Cohort comparisons
The most valuable use cases — LP reporting, trends, benchmarking — are barely touched. A data problem, not an AI problem.
Reduced time on repetitive tasks
Improved quality of output
Faster decision making
Do work they couldn't before
The firms breaking through this friction share a common thread: they've stopped treating AI as a collection of point tools and started treating it as infrastructure, connecting their data, centralizing their workflows, and building on top of a foundation that compounds over time.
Early AI Implementation
The gap isn't an AI problem. It's a data problem.
The survey results point to something worth sitting with. Internal ops is the runaway leader for where AI is being applied. Yet the use cases that arguably matter most for a VC firm, LP reporting, identifying trends across portfolio data, and benchmarking portfolio performance, are the least utilized.
General-purpose AI tasks like drafting emails, summarizing documents, and automating admin work don't require your firm's data, they run on the model's existing knowledge. But the moment you try to ask meaningful questions about your portfolio, your LPs, your deal history, or your performance over time, the AI is only as good as the data you've given it to work with.
"Public data layers like Harmonic, Crunchbase, and PitchBook are increasingly commoditized. What can't be bought is a firm's internal data — years of portfolio updates, investment memos, IC voting records, and meeting transcripts. Most firms haven't started to systematically capture it, and those that do rarely leverage it."
Survey Participant
This is why 41% of survey respondents cite data quality as a top barrier. Not the AI itself but the foundation underneath it.
Top Barriers to AI Adoption
Worry about data exposure slows adoption even where value is clear
Too many disconnected tools, too little signal
AI can't answer portfolio questions without good data underneath
Individual use cases exist but firm-wide rollout remains elusive
Centralizing Data with AI Inbox
From scattered updates to structured data.
Helping firms centralize, structure, and make their internal data accessible is our bread and butter at Visible. We have seen firsthand how AI Inbox has transformed the way our customers centralize and parse their data.
AI Inbox Automation
From Q1 to Q4 2025, the average number of AI-extracted metrics per Visible customer grew by 771%, followed by an additional 29% increase in Q1 2026, representing more than a 10x increase over the past year. Across our customer base, the share of metrics in Visible extracted via AI Inbox has risen from 10% to over 40%+.
Metric Approval Workflows in Visible
| Metrics | Q1 2026 | Q2 2026 | Q3 2026 | Q4 2026 |
|---|---|---|---|---|
| Cash Burn | -$0K | -$0K | -$0K | -$0K |
| Cash Balance | $0M | $0K | $0K | $0K |
| Runway | 0 | 0 | 0 | 0 |
Most Common Metrics
of all metrics are extracted via AI
"AI needs more work before it really gets integrated into fund ops, unless you're managing hundreds of companies and just want the 30k foot view. A lot of the fund ops stuff is more about a platform/visibility across teams than it is about actual AI. And the AI isn't good enough to be trusted without a human in the loop yet."
Survey Participant
Workflow Approvals
Review, approve, or reject AI-extracted data points before they hit your dashboard. Human in the loop, by design. Full audit trail included.
We took that seriously. Approval Workflows, a human checkpoint between raw data and reported results, is the result. Your team can now approve or reject data points, add comments for internal visibility, and trace every metric back to its original source, all with a full audit trail.
That foundation is what unlocks the highest-value use cases, portfolio analytics, benchmarking, LP reporting, and everything in between.
Connecting Data Across Your Stack
Centralizing data is the foundation. Connecting it is where it compounds.
Since launching our MCP Server, we've seen teams push well beyond traditional portfolio monitoring, querying their Visible data directly inside Claude and other AI tools, alongside their CRMs, email threads, meeting notes, and deal pipelines. The question stops being "what does our portfolio look like" and starts being "what should we do next."
of all MCP tool calls are metric retrieval — the #1 use
"The MCP server reduces the time to surface portfolio insights from minutes to seconds, and to reduce ad hoc internal pings by an estimated 2-3 hours per week."
Survey Participant
Claude + Visible MCP: Most Common Questions
MCP Tool Call Breakdown
6,841 Total
49% of respondents struggle with tool overload. The firms cutting through aren't using fewer tools — they're connecting them to a central data layer that makes each one smarter.
Where Else Firms Are Implementing AI
The firms seeing the most impact got specific about the problem first.
Among firms that have adopted new AI tools in the last 90 days, Claude leads at 33% of all respondents, a general-purpose model, not a purpose-built VC tool. Granola (10%), Harmonic (7%), and ChatGPT/Gemini/Perplexity (7%) round out the top of the list.
New Tools Adopted
The Last 90 Days
Standout Use Cases
Legal & diligence
VDR review + cap table in 4 hrs vs. a lawyer's full week
Dealflow pipeline
AI-native CRMs scoring and prioritizing inbound deals
Financial & legal ops
Sub agreements, tax modeling, payroll — end to end
The firms seeing the most impact aren't necessarily the ones moving fastest. They're the ones who got specific about the problem first.
Standout Examples of Intentional AI Implementation:
Marketing & Comms
Historically an afterthought for lean VC teams, it emerged as a surprising standout among respondents at 30%. For many firms, AI hasn't just accelerated content and outreach. It's made consistent execution possible for the first time.
Compressing Legal and Diligence Timelines.
One respondent used AI to review a virtual data room and build a cap table for a complex stacked convertible instrument company, four hours of work that would have taken a lawyer a full week.
End-to-End Financial and Legal Operations.
One firm is using AI to run legal drafting for subscription agreements, shareholder agreements, and IP acquisitions, alongside financial statement automation, tax modeling, and payroll planning.
Dealflow and Pipeline Management.
Several firms have moved from traditional CRMs to AI-native pipeline tools, using AI to score, summarize, and prioritize inbound dealflow.
Build the Relationships That Matter
Every efficiency gain points to the same question: what do you do with the time you get back?
The easy answer is more output. More deals reviewed, more reports generated, more updates processed. And for many firms, that's where the AI story stops.
But the most important returns from AI may not show up in a dashboard at all.
At the end of the day, venture is a relationship business. The best founders choose their investors as much as investors choose them. LPs commit capital to people they trust. Teams perform best when they feel connected to a shared mission. None of that changes in the age of AI — if anything, it becomes more important as the operational and analytical gaps between firms continue to narrow.
The firms that will pull ahead aren't necessarily the ones that automate the most. They're the ones who use AI to get closer to the people they work with.
Imagine walking into a board meeting having already synthesized every update a founder has submitted, every metric trend, every commitment made and kept over the past two years, not because you spent three hours preparing, but because you asked a question and got an answer in minutes. Or knowing exactly which LPs haven't had a meaningful touchpoint in 90 days, and what portfolio news is most relevant to each of them before you pick up the phone.
That's not AI replacing relationships. That's AI making better ones possible.
The firms that will define what great VC operations look like in the next decade won't be known for how many tools they adopted. They'll be known for how well they showed up for their founders, their LPs, and each other. In a world where every firm has access to the same models, the same tools, and increasingly the same data, the competitive advantage is still the same one it has always been... the relationships.
2026 AI Sentiment
Build the Relationships That Matter
Give your team the data foundation to show up better for every founder and LP.