
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?
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?
01
Market foundation
Account data, market signals, content and the source quality behind them.
02
Company context
CRM history, ICP, messaging, segments and previous customer interactions.
03
Intelligence
Research, qualification, prioritisation, personalisation and evidence traceability.
04
Agents
What can work continuously, what needs a specialist product and what teams build internally.
05
Workflow and control
Approvals, brand voice, ownership, access and the human judgement teams retain.
The stack layers GTM teams bring into the conversation
We are mapping the handoffs between signal, context, decision, approval, execution and the outcome that returns to the record.
Signals and targeting
Account selection and external signals
Clay
LinkedIn Sales Navigator
Apollo
PredictLeads
Enrichment
Buyer and account evidence
Clay
Apollo
Harmonic
PredictLeads
CRM
Relationship history and system of record
HubSpot
Attio
Salesforce
Outreach
Approved delivery and provider events
Smartlead
Instantly
HeyReach
Unipile
Gmail
Automation glue
Workflow routing and internal tools
n8n
Zapier
Claude Code
Always-on agents
Persistent agents coordinating recurring GTM work
OpenClaw
Hermes
AgentLed
Conversations and outcomes
Replies, meetings and outcome evidence
Granola
Circleback
Notion
Decision and approval
Where the next action is chosen and held
GTM Agent
ChatGPT
In-house / custom
The study is ongoing. Aggregate signals will appear here when they are updated.
The adoption map will appear when its aggregate has been updated.
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.
References and sources
These external benchmarks shape the study lens. They are not AgentLed study responses or independent proof of a specific provider integration.
- The State of GTM Engineering — 2026 Benchmark Report
State of GTME · 2026 · 225+ respondents worldwide.
- RevOps in 2025: Data, Process, Leadership, and AI
Revenue Wizards · 2025 · 180 professionals across 28 countries.
- Sales Tech Stack Survey Results
Level Equity · 2025 · 28 participating portfolio companies.
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