AI GTM Readiness & Compliance Framework

AI governance doesn’t end inside the model but it extends into how the product is positioned, sold, and understood.
I built this framework after watching teams rush AI features to market without the guardrails, alignment, or clarity needed to protect customers and the business.

This project reflects my belief that responsible AI is just as much a GTM function as it is a technical one. Before a model ever reaches a user, there should be a strategy for how it’s explained, tested, supported, and governed across product, engineering, marketing, sales, legal, and security.

This framework was developed to address a recurring failure pattern: AI features reaching market before governance, risk, and customer narratives were aligned.

This is my playbook for releasing AI features with confidence, transparency, and intention , whether you’re an SMB building your first LLM-powered workflow or an enterprise preparing a public launch.

  • This framework provides a structured GTM readiness and compliance process for AI-powered features.
    It ensures teams align before launch, risks are documented early, and customer-facing narratives match internal governance decisions.

    It includes readiness criteria across:

    • Product

    • Engineering

    • Legal & Privacy

    • Security & GRC

    • Marketing

    • Sales Enablement

    • Support & Monitoring

  • This work is informed by my experience supporting sales and client negotiations directly, where security requirements often became a gating factor for revenue. By defining engagement tiers, decision thresholds, and performance metrics, the goal is to enable faster go-to-market motion while maintaining a defensible security and risk posture.

    In practice, this lens reduces last-minute escalations, shortens security review cycles, and creates clearer yes/no decision paths for sales teams.

  • This work is informed by my experience supporting security and contract transitions during multiple business divestitures, where existing agreements, obligations, and review processes needed to be reassessed under tight timelines. In those environments, security decisions directly affected customer confidence, deal continuity, and revenue stability, reinforcing the need for governance models that scale during organizational change rather than break under pressure.

  • AI features often get pushed to market before:

    • governance and GTM alignment

    • risk and messaging alignment

    • customer expectation mapping

    • compliance narratives

    • safety evaluations

    • lifecycle monitoring strategies

    This creates:

    • Internal misalignment (governance, risk, lifecycle)

    • External misrepresentation (marketing, sales, customer expectations)

    • Operational fragility (support, monitoring, incident response)

    Companies need a repeatable, cross-functional AI launch process that connects risk to product to GTM.

  • 1. Product Readiness

    • Clear definition of AI functionality

    • Intended use vs prohibited use

    • UX expectations

    • Human-in-the-loop checkpoints

    • Failure mode identification

    • Model card availability

    2. Engineering Readiness

    • Performance thresholds

    • Error handling

    • Red-team results reviewed

    • Logging & observability

    • Drift detection plan

    3. Legal & Privacy Review

    • PII handling

    • Consent mechanisms

    • Data retention

    • Model transparency wording

    • Liability boundaries

    • Terms of use updates

    4. Security & GRC

    • AI risk register

    • Compliance mapping

    • Deployment controls

    • Incident escalation flow

    • Model misuse detection

    5. Marketing Alignment

    • Risk-aware messaging

    • No overpromising model capability

    • Ethical marketing patterns

    • Transparency statements

    6. Sales Enablement

    • Honest capability breakdown

    • What the model cannot do

    • Customer FAQs

    • Use-case boundaries

    • Competitive positioning

    7. Support & Monitoring

    • Clear paths for AI-related tickets

    • Escalation to engineering/legal

    • Customer-facing incident responses

    • Ongoing evaluation cadence

  • These recommendations are designed to be lightweight, repeatable, and compatible with existing enterprise processes.

    Pre-Launch

    • Cross-functional review meeting

    • Final safety evaluation

    • Compliance documentation

    • Executive sign-off

    Launch

    • GTM alignment

    • Messaging discipline

    • Sales + support readiness

    • Usage monitoring begins

    Post-Launch

    • 30/60/90 day AI performance review

    • Drift & failure mode analysis

    • Legal + risk reevaluation

    • Customer feedback loop

    • AI GTM Readiness Checklist (PDF)

    • Model Card Template

    • AI Risk Register

    • Controls Mapping

    • Transparency Statement Template

    • Sales Enablement One-Pager

    • Governance Review Workflow Diagram

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