Growth

AgentLed research

AI agents in GTM teams: buy, build, or combine?

A short exploratory study of how go-to-market teams introduce AI into prospecting, content, outreach and revenue operations.

Which tools earn a place in the stack, what gets built internally, and where does human approval still matter?

Share your stack

Participate to receive the findings brief and practical industry insights from peer GTM teams.

Why this study

General models, specialist GTM products and in-house agents each solve part of the work. We want to understand the operating choices behind real adoption — including the tools teams stop using.

A five-layer research lens

We look beyond the model itself. At every layer, the question is the same: buy, build, or combine?

  1. 01

    Market foundation

    Account data, market signals, content and the source quality behind them.

  2. 02

    Company context

    CRM history, ICP, messaging, segments and previous customer interactions.

  3. 03

    Intelligence

    Research, qualification, prioritisation, personalisation and evidence traceability.

  4. 04

    Agents

    What can work continuously, what needs a specialist product and what teams build internally.

  5. 05

    Workflow and control

    Approvals, brand voice, ownership, access and the human judgement teams retain.

The tools GTM teams bring into the conversation

We are mapping how teams combine general AI, specialist GTM products and their existing operating stack.

General AI

Foundation models and agentic assistants

  • Claude
  • ChatGPT
  • Gemini

GTM intelligence

Prospecting, enrichment and account research

  • Apollo
  • Clay
  • Harmonic
  • Similarweb

CRM and engagement

Systems of record, sequencing and follow-up

  • HubSpot
  • Attio
  • Salesforce
  • Instantly

Company knowledge

Meeting intelligence, shared context and memory

  • Granola
  • Circleback
  • Notion
  • In-house / custom

Build and automation

Internal tools, agentic coding and workflow building

  • Claude Code
  • Codex
  • n8n

Always-on agents

Persistent agents that work across the GTM cadence

  • OpenClaw
  • Hermes
  • GTM Operator

Examples only — not a ranking, market-share claim or endorsement. Findings will report only de-identified patterns from the study.

Study response

What does your GTM team use today?

Select the tools in your active stack, add anything we missed, and help us map the combinations GTM teams are actually running.

We will use your domain only to identify the company behind this response and send the de-identified findings brief when it is published.

Select every tool your team actively uses

General AI

GTM intelligence

CRM and engagement

Company knowledge

Build and automation

Always-on agents

What best describes your in-house setup?
What technical capacity does the company have? Select all that apply.
Which recent work triggered this decision? Select all that apply.
What mattered most in the decision? Select all that apply.
How would you like to take part?

Your response will be analysed only in de-identified aggregate patterns. We will never publish anything beyond an opt-in company logo without separate approval.

We’ll email you the findings brief and practical patterns from peer GTM teams when the study closes.

Share a GTM workflow by email

Open to a short research conversation about one workflow, a stopped tool, or the evidence behind your next AI adoption decision?

Share a GTM workflow by email