AI Readiness for GTM Teams
Stop experimenting with AI in silos and start applying it with a practical, measurable system that ties AI initiatives to GTM outcomes, readiness, and execution.
Most teams want AI, but lack the clarity, data trust, and operational structure to deploy it without creating noise or risk. This certification helps you move from AI curiosity to AI readiness by grounding everything in business value, validating what your organization can realistically support today, and turning real GTM leaks into a prioritized roadmap.
Through hands-on modules, templates, and knowledge checks, you will learn how to map AI solutions end-to-end and execute them with clear ownership, guardrails, and measurable success metrics.
Course takeaways include:
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AI Foundations for GTM Leaders
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The AI Readiness Blueprint
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AI Readiness Audit and GTM Visibility
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Use Case Backlog and Roadmap Generation
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Solution Mapping and Execution Framework
$299
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theaters
~2-3 hours of video
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4 classes
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12 chapters
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12 practical assessments
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strategy
RPX Certification
Move from AI curiosity to AI readiness.
AI does not create advantage on its own, execution does. This course gives you a repeatable system to adopt AI with clarity, guardrails, and measurable outcomes, so your team drives real GTM impact instead of noise.
Class 1: AI Foundations and RPX POV
AI Foundations
3 videos | 3 Knowledge Checks
Time: ~1 hours
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Where is AI Today & Where is it Going
15 minutes -
The Age of Gen AI
10 minutes -
RPX & AI: POV & Next Steps
5 minutes
Class 1 sets the foundation for the entire certification. We start by grounding on where AI is today and where it’s going, then we zoom in on Generative AI and what GTM teams should do to adapt. Finally, we close with the RPX point of view and the “why” behind this certification: teams win by building AI maturity (clarity, clean signals, strong workflows, guardrails, and measurable outcomes).
By the end of this class, you will be able to:
- Explain what AI is, how it learns, and how modern AI differs from “traditional automation.”
- Describe how GenAI works in simple terms (tokens, probabilities, and the context window), and why output quality varies.
- Identify what high-quality “AI adoption” looks like in GTM (think before you act, avoid AI slop, measure outcomes, and build maturity across people/process/data/systems).
Class 2: AI Readiness Blueprint & Building Blocks
AI Readiness Blueprint
3 videos | 3 Knowledge Checks
Time: ~1 hours
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Business Values (Outcomes + Metrics)
10 minutes -
AI Capabilities & Pillars
10 minutes -
AI Solutions, Categories & More
10 minutes
Class 2 turns AI readiness into a practical blueprint you can apply to your own GTM org. We start with business value and success metrics (acquisition and retention), then map the core AI capabilities (pillars), and finally translate those into solution lanes, native platform AI, market ready tools, and custom solutions. You will also learn the prerequisites that make AI work reliably, especially data quality, access to internal knowledge, and the right infrastructure, so you can move from ideas to a roadmap with confidence.
By the end of this class, you will be able to:
- Define your AI Readiness Blueprint: Start from business outcomes, then break them into acquisition and retention levers with clear success metrics.
- Map capabilities to solutions: Use the AI pillars to categorize what AI can realistically do, then connect those pillars to solution types and use cases.
- Identify what is required to execute: Spot the prerequisites that drive quality and reliability, including clean data, access to internal knowledge sources, and model plus infrastructure readiness.
Class 3: AI Readiness Audit & Use-case Roadmap
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AI Readiness Audit
4 videos | 4 Knowledge Checks
Time: ~1 hours
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Capture GTM visibility and Leaks
10 minutes -
Validate AI Readiness
10 minutes -
Build the Use-case Backlog and Roadmap
10 minutes -
Prep for Roadmap Execution & Hand-off
5 minutes
Class 3 captures GTM visibility, validates readiness, and builds your AI roadmap. In this class, we move from blueprint to action. We start by capturing go-to-market visibility and leakage across acquisition and retention, then validate whether your organization is actually ready to execute AI (so you know where to say “yes” and where to say “not yet”). From there, we build and score a use case backlog, auto-generate a prioritized AI roadmap, and finish with a clean developer handoff template for any custom solutions.
By the end of this class, you will be able to:
- Capture GTM visibility and leaks: Map acquisition and retention performance end to end, pinpoint where metrics live, and document the biggest drop-offs and friction points across the buyer journey.
- Validate AI readiness with confidence: Assess what is truly ready for AI today, identify non-negotiable gates, and create a clear artifact to justify “no” when stakeholders push for AI execution prematurely.
- Build a use case backlog and roadmap: Brainstorm use cases from real leakage evidence, score them using a consistent framework, and auto-generate a prioritized roadmap with clear phases.
- Prep for execution and developer handoff: Turn top roadmap items into build-ready briefs using a requirement intake template, including inputs, outputs, constraints, decision rules, and launch plan.
Class 4: AI Solutions Mapping & Roadmap Execution
AI Solutions Mapping
2 videos | 2 Knowledge Checks
Time: ~30 minutes
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Map Roadmap Items to the Blueprint
10 minutes -
Execute and Track the Roadmap in your PM Tool
5 minutes
This is the final class of the course. You will take the roadmap you built in Class 3 and map each item back into the AI Readiness blueprint, so it is crystal clear what you are building, which inputs power it, which AI pillars are involved, what outputs you expect, and what human actions change as a result. Then you will convert the roadmap into a simple, repeatable execution plan using a sprint-based structure (in ClickUp or any PM tool), with clear phases like discovery, build, deliver, measure, and adoption. Finally, we will close with a full course recap so you leave with a clean end to end system.
By the end of this class, you will be able to:
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Map roadmap items into the blueprint: Visually connect business value, KPIs, inputs, AI pillars, outputs, and human behavior for each solution so the build is unambiguous.
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Define what “success” means upfront: Lock the KPI, expected behavior change, and validation approach before you build, so measurement is built into the plan.
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Turn the roadmap into execution: Organize roadmap items into a sprint-ready system with phases (discovery, build, deliver, measure, adoption) and clear ownership.
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Build an epic and task structure that scales: Break each roadmap item into phase-based tasks with clear end states, inputs, and outputs, and reuse the task templates across projects.
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Wrap the course with a full recap: Review the full journey from AI fundamentals through blueprint, audit, use cases, mapping, and execution so you can repeat this process in your own org.
Meet your instructor
Shadab works at the intersection of RevOps strategy and hands on execution. At RevPartners, he supports go to market teams by designing the systems that power predictable growth, including clean pipeline architecture, reliable reporting, data governance, and automation that teams actually adopt. He is also a HubSpot expert, known for turning complex requirements into scalable, measurable workflows.
Shadab has recently focused heavily on practical AI for GTM. He builds frameworks, playbooks, and operating models that help teams apply AI responsibly, with strong inputs, clear guardrails, and measurement loops. His work helps organizations move from experimentation to real outcomes, without sacrificing accuracy or trust.
Shadab Khan
Principal Product Manager, RevPartners
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