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Checklist: AI-Augmenting Your GTM

Use this checklist before deploying AI into your go-to-market operations. It covers the strategic, data, tooling, and measurement foundations required to avoid the eight documented GTM mistakes and to find the right balance between automation and augmentation. If you are evaluating a new AI-powered GTM tool, planning an AI-assisted outreach campaign, or auditing an existing AI deployment that is underperforming, start here.

Derived from the 8 GTM Mistakes with AI and Automation vs Augmentation --- Chapter 10: AI-Augmented Operations and GTM.

Strategy Assessment

  • Define whether each GTM process should be automated, augmented, or left human-only---don't default to "automate everything"
  • Score each task on the three complexity dimensions: component complexity (information inputs), coordinative complexity (process steps), and dynamic complexity (variability over time)
  • Confirm your AI GTM initiative solves a business problem, not a technology curiosity---articulate the specific outcome you expect before selecting tools
  • Audit current GTM AI initiatives against all eight documented mistakes (over-automating relationships, ignoring data quality, tool proliferation, chasing volume, vanity metrics, underinvesting in change management, disconnected systems, privacy violations)
  • Identify where you are putting technology before people and processes---most failures stem from this single error
  • Ensure AI outreach uses a hybrid approach: AI drafts, humans add genuine personalization referencing company-specific news and specific challenges

Data Foundation

  • Validate data quality before deploying any AI tool---80-88% of AI projects fail due to poor data quality, and AI amplifies data problems 10-100x
  • Implement real-time data validation to catch stale contacts, role changes, and outdated company information
  • Verify that AI lead lists are manually reviewed for junk (invalid emails, duplicates, people who have left companies)---target removing the typical 40% ghost rate
  • Ensure CRM data includes organizational changes, competitive intelligence, and real-time buying signals, not just static records
  • Build unified GTM data layers before deploying AI tools---ensure bidirectional data flow across all platforms
  • Conduct a Data Protection Impact Assessment before any AI deployment that processes customer or prospect data

Tool Selection

  • Audit the total number of AI tools in use across GTM teams---28% of enterprises use more than 10 different AI apps, creating sprawl
  • Verify all AI tools go through proper approval channels (only 35% currently do in enterprise settings)
  • Prioritize native integrations over point solutions to prevent siloed data and manual transfer between tools
  • Ensure selected tools support bidirectional sync (technical integration issues account for 40-60% of sales intelligence failures; HubSpot-Salesforce sync is a common culprit)
  • Consolidate redundant AI tools before adding new ones---30% of enterprises waste money on overlapping AI software
  • Confirm every automated process has a designed escalation path with clear triggers (confidence scores, sentiment, explicit requests) and seamless handoffs with full context

Human-AI Balance

  • Adopt the five-stage automation progression for new AI processes: Observe, Suggest, Execute with Approval, Execute with Audit, Full Automation---don't skip stages
  • Monitor for over-automation warning signs: customer experience degradation (NPS drops despite high deflection rates), quality problems (increasing false positives/negatives), and employee morale decline
  • Verify that AI-assisted outreach doesn't create "ghost leads" that never respond---if response rates are declining, over-automation is the likely cause
  • Ensure SDRs run hybrid verification on AI-generated leads: email verification and cross-checking titles on LinkedIn before outreach
  • Allocate 20-30% of AI implementation budget to training and change management---technology is the easy part
  • Address change resistance proactively: demonstrate value before full rollout, secure visible leadership backing, and invest in skills development before deploying technology

Measurement

  • Audit your GTM dashboard and remove vanity metrics: number of models deployed, AI-driven sessions, social shares, model accuracy alone, and volume of data processed
  • Replace vanity metrics with business outcome metrics: conversion rates, revenue influenced, customer acquisition cost, pipeline velocity, and retention impact
  • Measure flow efficiency (how fast work moves from idea to customer impact), not just activity volume or developer speed
  • Track both efficiency metrics and satisfaction metrics (customer NPS and employee engagement)---if efficiency goes up but satisfaction goes down, you have over-automated
  • Ask the diagnostic question for every AI tool: has it increased touchpoints without improving conversion?
  • Measure 12-month ROI holistically---hybrid human-AI approaches deliver 34% better ROI despite 15-20% higher initial costs

Privacy and Compliance

  • Audit all AI-powered GTM processes for GDPR compliance: lawful processing basis, transparency requirements, and data subject rights mechanisms
  • Verify that no AI system conducts behavioral profiling for targeted advertising without explicit user consent
  • Ensure AI data collection has valid legal basis---"the complexity of AI systems" doesn't justify non-compliance
  • Implement age verification where required for AI-powered customer-facing tools
  • Document transparency measures: prospects should be able to understand how their data is being used
  • Review vendor contracts for data processing terms, retention policies, and incident notification requirements