AI-Enabled GTM Capability Assessment

Where does your revenue org stand today?

This 7-9 minute diagnostic measures your company across six dimensions of AI-enabled GTM maturity: ICP clarity, data foundation, KPI discipline, forecasting, AI workflow adoption, and RevOps governance.

You'll get a personalized maturity report with your overall level, dimension scores, and recommended bootcamp focus area.

Section 1 of 6: Company & Revenue Context

A few quick questions about your business so we can tailor the report.

1. What best describes your company’s primary business model? *

2. Which THREE revenue motions are most important to your growth plan over the next 12 months? *

Select exactly 3.

3. Which function do you represent? *

4. What is your approximate annual revenue band? *

5. Which systems are currently part of your GTM / revenue stack? *

Select all that apply.

Section 2 of 6: ICP & Strategy

AI won’t fix an undefined ICP — it just helps teams chase the wrong accounts faster.

6. How clearly defined is your Ideal Customer Profile? *

We do not have a clearly defined ICP, or it varies by team. We have a general ICP, but it is not consistently applied in targeting, qualification, or prioritization. We have a documented ICP used by sales and marketing to prioritize accounts and opportunities. Our ICP is data-backed, regularly refreshed, and embedded into scoring, routing, forecasting, and expansion plays.

Section 3 of 6: Data Foundation

Without a single source of revenue truth, forecasting and accountability break down.

7. How unified is your revenue data across CRM, marketing, finance, customer success, and delivery systems? *

Data is siloed and often conflicts across systems. Some systems are connected, but inconsistencies require manual reconciliation. Most core revenue data is unified with clear ownership and standard definitions. We have a governed single source of revenue truth with trusted, integrated data across the customer lifecycle.

8. How would you rate the quality of your CRM data? *

Unreliable: duplicates, missing fields, outdated contacts, inconsistent opportunity data. Mixed: important accounts are mostly accurate, but long-tail data is messy. Generally accurate, with periodic clean-up and defined required fields. High quality, governed, automatically validated, and actively used for AI workflows and reporting.

Section 4 of 6: KPIs & Signals

KPIs aren’t just metrics — they connect strategy to cross-functional decisions.

9. Are your core revenue KPIs clearly defined and shared across functions? *

No. Teams use different definitions and debate the numbers. Some KPI definitions exist, but they are not consistently used. Most core KPIs are documented and used across leadership meetings. KPIs are standardized, governed, tied to business outcomes, and cascaded by role and cadence.

10. How well does your team distinguish between leading and lagging indicators? *

We mostly manage from lagging indicators like closed revenue, quota attainment, and historical reports. We track some leading indicators, but they are not consistently used to make decisions. We have defined leading indicators by funnel stage and use them in reviews. Leading indicators trigger actions, alerts, playbooks, and AI-supported recommendations.

11. Which predictive signals are currently used in GTM decision-making? *

Select all that apply (or “None of the above”).

Section 5 of 6: Forecasting & Pipeline

Forecasting is a capability, not a calculation — an early warning system, not a math exercise.

12. How accurate and useful is your revenue forecast today? *

Forecasting is mostly manual, spreadsheet-based, and often surprising. Forecasting is CRM-based, but still relies heavily on rep judgment and stage probability. Forecasting includes pipeline quality, stage movement, deal velocity, and risk indicators. Forecasting is AI-informed, scenario-based, and used weekly to guide resource allocation, coaching, and executive decisions.

13. How disciplined is your pipeline management? *

Pipeline stages are inconsistently used and deals are often entered late. Pipeline stages exist, but compliance and deal hygiene vary by team or rep. Pipeline stages, exit criteria, and opportunity quality standards are mostly followed. Pipeline movement is governed, signal-based, and monitored for quality, risk, velocity, and forecast impact.

Section 6 of 6: AI, Operating Rhythm & RevOps

The final stretch — how AI, coaching, RevOps, and governance show up in your day-to-day.

14. How is AI currently used in sales, marketing, or customer success? *

Select all that apply (or “We are not using AI in GTM”).

15. Where do AI insights or analytics show up for frontline teams? *

Separate dashboards or reports people must go find. Shared periodically by email, Slack, or RevOps updates. Visible inside core systems like CRM, CS tools, or BI dashboards. Embedded directly into daily workflows with recommended actions and ownership.

16. How are revenue insights used in your operating rhythm? *

Mostly reviewed after the fact in monthly or quarterly reporting. Discussed in recurring meetings, but actions are inconsistent. Used in weekly pipeline, forecast, campaign, and renewal reviews. Used continuously to trigger decisions, coaching, resource shifts, and playbook execution.

17. How data-driven is frontline coaching? *

Coaching is informal, inconsistent, or based mostly on manager instinct. Managers use some activity and outcome metrics, but coaching is not systematic. Coaching is tied to scorecards, leading indicators, and performance patterns. AI-assisted coaching briefs, call insights, KPI shifts, and signal alerts guide manager 1:1s and team performance improvement.

18. How mature is your RevOps function? *

RevOps does not exist or is primarily ad hoc reporting / CRM administration. RevOps supports sales operations and reporting, but has limited cross-functional authority. RevOps owns key revenue processes, data quality, reporting, and operating cadences across functions. RevOps is the system owner for the revenue operating model, including data governance, AI workflows, forecasting, KPI design, and continuous improvement.

19. How strong is your revenue governance? *

Definitions, processes, and ownership vary widely across teams. Some rules exist, but compliance depends on individual managers. Definitions, roles, and process standards are documented and reviewed periodically. Governance is embedded into systems, workflows, dashboards, operating cadences, and AI adoption rules.

20. What is the biggest barrier to becoming more AI-enabled in GTM? *

Diagnostic only — not part of your maturity score.

21. What is your current forecast accuracy? *

What percentage of forecasted revenue typically closes in the expected period?

or

22. What percentage of opportunities are entered into CRM before proposal stage? *

Estimate the percentage entered before a proposal is requested or sent.

23. What percentage of your revenue comes from existing customers? *

Approximate percentage from renewals, repeat customers, upsell, or expansion.

Your Maturity Report

A snapshot of where your revenue org stands today across the seven dimensions of AI-enabled GTM maturity.

Calculating your results…