Almost every PE and VC firm now uses AI somewhere in the deal process. One recent analysis puts that figure around 95%. The shift in the last 12-18 months is that firms are no longer “using ChatGPT for drafts”. They are wiring agentic AI into the sourcing stack and leveraging alternative investment management software that can run sourcing loops on its own: scan, enrich, write, log, repeat.
This is what many of the 500+ firms experimenting with agentic workflows are doing right now in practice.
What “Agentic AI” Means For A Deal Team
Agentic AI sounds like a buzzword, but for a deal team, it is simple:
You give the system a goal. It decides which tools to call. It keeps going until the job is either done or blocked.
Instead of “ask a chatbot for ideas”, you give an instruction such as:
- “Build a list of 50 bootstrapped vertical SaaS companies in DACH that match this thesis and clear these revenue and growth thresholds. Enrich each with the founder, HQ, pricing model, and latest product signals. Push the top 20 into our CRM.”
An agent then:
- Queries structured data sources (PitchBook, Grata, Extruct, Crunchbase, etc.).
- Scrapes or calls APIs on company sites, app stores, and public filings.
- Normalizes fields, scores fit against your thesis, and writes a short profile.
- Calls the CRM API to create or update records and logs what it did.
It is still “just software”, but it behaves more like a junior analyst who can read, click, cross-check, and loop, not like a single SQL query or dashboard.
Where Agents Sit in the Modern Deal-Sourcing Stack
Most firms that have gone beyond pilots keep their mental model very simple. The sourcing funnel still looks like:
- Thesis
- Universe building
- Screening
- Prioritization
- Outreach
- Tracking and reporting
Agentic AI sits inside that funnel, not outside it. It plugs into the tools you already run:
- Data platforms such as Grata, Extruct, or custom data lakes.
- Internal and third-party CRMs.
- Email and sequencing tools.
- Note systems and IC templates.
The agent does the glue work: it calls APIs, extracts text, enriches records, and keeps things in sync. A good mental image is “an always-on analyst that lives between your sourcing tools and your CRM”.
Use Case 1 – Always-On Signal Scanning Mapped To Live Theses
The biggest change agents bring is that sourcing is no longer tied only to human working hours.
A typical pattern looks like this:
- The partner defines a thesis: “Founder-owned industrial services in the US Midwest with asset-light models and recurring maintenance revenue.”
- The data or AI team encodes that into machine-readable rules: SIC/NAICS ranges, geographies, revenue bands, ownership flags, and a set of soft signals (site language, pricing hints, hiring patterns).
- An agent runs on a schedule (hourly, daily, weekly) and: Scans known databases and internal lists. Pulls in new names from alternative data such as job postings, patents, or niche directories. Re-scores existing targets as their metrics change.
What drops out is not a random “AI list”. It is a ranked watchlist tied to a specific thesis. Some providers report time savings of 80%+ on initial screening and many-fold speedups in how fast they can analyze thousands of opportunities.
Use Case 2 – Agentic Company Profiling And First-Pass Memos
Once a company pops up on the radar, the next grind is always the same. Someone has to:
- Pull financials or estimates from platforms.
- Read the website, product pages, and pricing.
- Check app stores, G2/Capterra, Glassdoor, and news.
- Work out ownership, funding, and recent moves.
- Write a short note so partners can decide if it is worth more time.
Agentic systems are very good at that “first 60–80%” of desk work. A typical workflow:
- Analyst drops a domain or company ID into a chat-like interface.
- The agent calls the firm’s standard data sources, scrapes relevant pages, and extracts key facts.
- It normalizes numbers into your internal schema (revenue, headcount, geography, segments).
- It drafts a one-pager in your IC note format: What the company does. Why it might fit the thesis. What is unknown and needs a human to confirm.
In some real deployments, agents prepare thousands of such snapshots in hours, with 8x faster memo prep and 12x faster visualization for IC decks compared to manual work.
Human judgment still decides if you engage. The difference is that you now see the top of the funnel as a structured queue of mini-memos, not a heap of half-filled spreadsheets.
Use Case 3 – Relationship and Warm-Intro Mapping At Scale
PE has always been about relationships, but most firms only see a thin slice of their own network. CRMs are patchy. People move. Warm intros get lost in inboxes.
Here, agentic AI acts as a memory layer across your communication exhaust. In firms that allow it, an agent can:
- Read CRM records, past call notes, and pipeline history.
- Index email metadata and calendar invites (e.g., “who met this founder in 2021?”).
- Blend that with external sources like LinkedIn or conference attendee lists.
For a specific target, the agent can then answer:
- “Who in our firm has had direct contact with this founder or company?”
- “Which LPs, executives, or advisors in our extended network share a strong connection we could ask for a warm intro?”
- “Are different teams in the firm already talking to related companies so we do not collide?”
No agent can replace relationship nuance. What it can do is stop you from starting cold when a perfectly good warm path already exists inside your own data.
Use Case 4 – Outreach Orchestration and Pipeline Hygiene
Most deal teams will admit that their biggest pain is not “finding names”. It is staying on top of outreach and keeping the pipeline clean:
- Drafting tailored emails.
- Following up without being annoying.
- Logging calls and notes.
- Updating stages when deals move – or die.
Agentic AI is now taking on the coordination work here:
For outreach
- Agents pull context from the thesis, company profile, and partner preferences.
- They draft short, specific emails (“we like you because of X, Y, Z”) instead of generic spam.
- They suggest follow-up sequences that reference news or product updates the agent has seen since the first contact.
For pipeline hygiene
- Agents watch the CRM for stale statuses.
- They nudge the deal owner (“no activity in 45 days – close, re-engage, or re-score?”).
- They log interactions by reading calendar entries or email threads and pushing structured notes into the right records.
The result is not “robo-spam”. It is a pipeline where data is more complete, and people spend more time on actual conversations rather than admin.
Implementation Checklist: Pilot Agentic AI in 90 Days
A good pilot is narrow, boring, and measurable. Something like this:
Weeks 1–2 – Pick one pod and one thesis
- Choose a small team that wants to experiment.
- Pick a live thesis (e.g., “profitable B2B SaaS in Benelux between €5–20m ARR”).
- Define “good lead” in concrete terms: revenue range, margin, owner type, tech stack hints.
Weeks 3–6 – Stand up one agent which owns a small loop
- Give the agent access to: Your main company datasets and a sandbox slice of the CRM.
- Ask it to do just two things: Maintain a ranked watchlist for that thesis. Generate short profiles for the top new names each week.
Weeks 7–10 – Wire into workflow and measure
- Push top targets into the real CRM, tagged as “AI-sourced”.
- Let the pod use agent-generated profiles in weekly deal meetings.
- Add basic outreach support (drafted emails, follow-up suggestions).
- Track a few simple metrics against a similar thesis that is still sourced manually.
If the pilot improves speed and quality without blowing up trust, you expand. If it does not, you still learned something in 90 days without betting the whole firm.
