AI-Native GTM
Source-grounded pack with AI-Native Buyer Journey Map, Agentic Sales Workflow Spec, and GTM Data Readiness Scan, plus 1 more.
Get this packGrounded in the complete AI-Native GTM series.
Open the pack, choose a workflow, and follow its setup and input instructions. You can also open each workflow below.
10 source essays · 4 usable skills · reviewed Jul 2026
Use this pack when
Choose this pack when the work touches AI-Native GTM and you need a bounded workflow with explicit inputs, a defined output, and human review gates.
Choose the job in front of you.
4 source-grounded skills. Load one workflow at a time.
AI-Native Buyer Journey Map
Use this workflow when mapping revenue as a cross-functional learning system from market signals through attention, gated action, lifecycle outcomes, and strategy updates.
Output specification
Return a revenue_learning_system_map with signals, interpretation_owners, decision_paths, attention_routes, human_gates, cross_functional_loops, learning_cadence, broken_loops, and one priority_repair with owner and evidence.
Agentic Sales Workflow Spec
Use this workflow when defining a bounded GTM agent that prepares, inspects, classifies, or queues revenue work without owning trust-heavy commercial decisions.
Output specification
Return a bounded_gtm_agent_spec with purpose, inputs, allowed_tools, allowed_actions, forbidden_actions, human_owner, review_gate, output_contract, quality_bar, permissions, audit_log, exceptions, stop_conditions, and feedback_loop.
GTM Data Readiness Scan
Use this workflow when checking whether revenue AI recommendations are grounded in owned, fresh, decision-specific sources rather than polished CRM blind spots.
Output specification
Return a revenue_source_of_truth_map with decision_sources, field_definitions, conflict_rules, quality_ratings, downstream_ai_dependencies, ownership, blocked_uses, and a prioritized repair_backlog.
AI-Native Campaign System
Use this workflow when designing attention, relevance, and pipeline-evidence gates so AI improves GTM selectivity instead of scaling synthetic activity.
Output specification
Return an ai_native_attention_system with attention_tiers, signal_combinations, account_hierarchy_rules, relevance_gate, pipeline_evidence_rubric, allowed_and_forbidden_actions, human_owners, feedback_events, and learning_metrics.
Keep consequential decisions explicit.
These workflows structure analysis and artifacts; they do not authorize autonomous external, personnel, financial, legal, security, or other high-impact action.