gtmskills.com

Reference file

GTM AI Use Case Catalog

gtm-ai-use-case-catalog.md

GTM AI Use Case Catalog

The full AI use case catalog by bowtie stage, with detailed requirements and KPIs per use case. The slim SKILL.md keeps the quick-reference table; this file holds the operational detail. Minimum AI maturity refers to the four-stage GTM AI maturity model (Stage 1 Ad-hoc, Stage 2 Programmatic, Stage 3 AI-Assisted, Stage 4 AI-Orchestrated).

Before recommending any use case, run the AI Readiness Checklist: clean data in the relevant object, metrics baselined, a review protocol defined (who reviews, who overrides), and a weekly audit built into the operating cadence. If three or more prerequisites are missing, the client is not ready for AI in that area.

Awareness

ICP scoring

  • What it does: scores accounts and leads against the defined ICP so reps work fit, not volume.
  • Requirements: a documented ICP with thresholds; firmographic and technographic data; closed-won and closed-lost history to train on.
  • KPIs: win rate on high-score vs low-score accounts; share of pipeline that is on-ICP; reduction in time spent on poor-fit deals.
  • Minimum maturity: Stage 2+.

Intent aggregation

  • What it does: consolidates first- and third-party intent signals to prioritise outreach.
  • Requirements: intent data sources connected; signal-to-action rules; a clear owner for acting on signals.
  • KPIs: response rate on intent-triggered outreach; pipeline sourced from intent signals.
  • Minimum maturity: Stage 2+.

Data enrichment

  • What it does: fills and refreshes account and contact records automatically.
  • Requirements: an enrichment provider; field governance so enrichment does not overwrite verified data; dedup discipline.
  • KPIs: record completeness; data freshness; routing accuracy downstream.
  • Minimum maturity: Stage 2+.

Education

Chat qualification

  • What it does: qualifies inbound via conversational AI and routes or books accordingly.
  • Requirements: defined qualification logic; routing rules; human handoff path.
  • KPIs: speed to lead; qualified-conversation rate; meetings booked from chat.
  • Minimum maturity: Stage 2+.

Email personalisation

  • What it does: drafts personalised outreach at scale from signals and context.
  • Requirements: clean contact data; brand and voice guardrails; human-in-the-loop on send for early phases.
  • KPIs: positive reply rate; meetings booked per sender; time saved per rep.
  • Minimum maturity: Stage 2+.

Lead routing

  • What it does: assigns leads to the right rep by territory, fit, or skill.
  • Requirements: routing logic; territory definitions; SLA on assignment time.
  • KPIs: speed to lead; routing accuracy; reduction in cherry-picking and orphaned leads.
  • Minimum maturity: Stage 2+.

Selection

Conversation intelligence

  • What it does: transcribes and analyses calls for risk signals, methodology adherence, and coaching targets.
  • Requirements: call recording in place; methodology defined (so the AI knows what good looks like); coaching cadence to act on output.
  • KPIs: methodology adherence; coaching actions taken; win-rate movement on coached skills.
  • Minimum maturity: Stage 2-3.

Pipeline scoring

  • What it does: scores open deals on health and win probability.
  • Requirements: consistent stage definitions; activity and multi-threading data tied to opportunities; baselined conversion rates.
  • KPIs: forecast accuracy; correlation of score to actual outcome; stalled-deal detection rate.
  • Minimum maturity: Stage 2-3.

Qualification copilot

  • What it does: prompts reps to complete qualification fields and flags gaps live.
  • Requirements: a qualification framework (SPICED or MEDDIC) embedded in the CRM; required-field governance.
  • KPIs: qualification completeness; reduction in qualification skip rate; deal-review efficiency.
  • Minimum maturity: Stage 2-3.

Mutual Commit

AI-assisted forecasting

  • What it does: produces a data-driven forecast alongside the rep-submitted call.
  • Requirements: clean pipeline data; historical close data; a forecast cadence that uses both numbers.
  • KPIs: forecast accuracy vs actuals; variance between AI and rep forecast; slippage rate.
  • Minimum maturity: Stage 3+.

CPQ

  • What it does: automates configure-price-quote to compress proposal time.
  • Requirements: product catalogue and pricing rules; approval workflow; integration to CRM.
  • KPIs: quote turnaround time; discount governance adherence; proposal-stage cycle time.
  • Minimum maturity: Stage 3+.

Legal acceleration

  • What it does: drafts and reviews standard contract language to speed the close.
  • Requirements: clause library; risk thresholds; legal sign-off path for non-standard terms.
  • KPIs: contract turnaround time; reduction in legal-stage stalls.
  • Minimum maturity: Stage 3+.

Onboarding

Success plan generation

  • What it does: drafts a customer success plan from the deal context at handoff.
  • Requirements: a clean sales-to-CS handoff with captured goals and success criteria; a plan template.
  • KPIs: time to first value; onboarding cycle time; handoff completeness.
  • Minimum maturity: Stage 2-3.

Early health signals

  • What it does: flags onboarding risk from usage and engagement data.
  • Requirements: product usage data; defined health thresholds; an owner for red flags.
  • KPIs: time to first value; early-churn rate; intervention rate on flagged accounts.
  • Minimum maturity: Stage 2-3.

Retention

Renewal risk

  • What it does: predicts renewal risk ahead of the renewal window.
  • Requirements: usage, support, and sentiment data; defined risk scoring; a save-play motion to act on it.
  • KPIs: gross revenue retention; renewal forecast accuracy; saved at-risk ARR.
  • Minimum maturity: Stage 3+.

Health scoring

  • What it does: maintains a live account health score across signals.
  • Requirements: multi-source data (product, support, relationship); validated scoring weights; review cadence.
  • KPIs: correlation of score to churn; net revenue retention; proactive intervention rate.
  • Minimum maturity: Stage 3+.

Sentiment analysis

  • What it does: reads support and conversation sentiment to surface relationship risk.
  • Requirements: support and communication data access; privacy and compliance review; an escalation path.
  • KPIs: detection of at-risk accounts before churn; CSAT or NPS movement.
  • Minimum maturity: Stage 3+.

Expansion

Upsell scoring

  • What it does: scores accounts for expansion fit and timing.
  • Requirements: usage and entitlement data; whitespace map; a CS-to-sales handback motion.
  • KPIs: expansion pipeline sourced; net revenue retention; upsell win rate.
  • Minimum maturity: Stage 3+.

Usage signals

  • What it does: triggers expansion plays from product usage thresholds.
  • Requirements: product usage warehouse; signal-to-play rules; ownership of the trigger.
  • KPIs: expansion revenue from usage triggers; time from signal to action.
  • Minimum maturity: Stage 3+.

Reference mining

  • What it does: identifies happy customers and advocacy and expansion candidates.
  • Requirements: health and advocacy data; an advocacy programme to feed; consent tracking.
  • KPIs: references generated; advocacy-sourced pipeline; expansion influenced by advocacy.
  • Minimum maturity: Stage 3+.

Investment rule

Apply the 90/10 rule: buy AI capability from vendors for the 90%, and build only the 10% where no vendor can do it well enough, where you hold proprietary data a vendor cannot access, or where a vendor's approach conflicts with your methodology. Most use cases above are buy, not build.