Grok Delivery Playbook

AI Audit Delivery Playbook

Complete templates, questions, scoring, and process for delivering high-converting AI Readiness / Opportunity Audits

1. Core Pillars (Use These 7)

Strategy & Business Alignment
Data Readiness (quality, governance, access, lineage, privacy)
Technology & Infrastructure (compute, MLOps, security, integration)
Governance, Risk, Ethics & Compliance
Talent, Skills & Culture
Operating Model & Processes
Use Case Identification & Value Delivery

2. Interview Guide – Key Questions by Pillar

Opening (all interviews):

What are your top 3 business priorities for the next 12–24 months?
How do you currently see AI helping (or not helping) those priorities?
Have you run any AI pilots or experiments? What worked / didn’t?

Strategy & Business Alignment

Is there a documented AI vision or strategy? Who owns it?
Is there an executive sponsor and dedicated budget for AI?
How are AI initiatives currently prioritized and funded?
What would “winning” with AI look like for the business in 2–3 years?

Data Readiness

What are your most critical data sources for decision-making today?
How would you rate data quality, completeness, and accessibility (1–10)?
Who owns data governance? Is there a data dictionary or catalog?
What privacy, security, or compliance concerns exist around data used for AI?
Can we get sample access to key datasets during this audit?

Technology & Infrastructure

What is your current core tech stack (CRM, ERP, data warehouse, cloud)?
Do you have MLOps, model monitoring, or AI-specific tooling today?
How would you describe your infrastructure’s readiness for training/deploying models at scale?
What are the biggest technical bottlenecks you anticipate with AI adoption?

Governance, Risk, Ethics & Compliance

Do you have any AI-specific policies or governance frameworks today?
How do you currently assess and mitigate risks like bias, hallucinations, or data poisoning?
What regulatory requirements (GDPR, industry-specific) apply to AI use in your organization?
Who would be accountable if an AI system made a harmful or biased decision?

Talent, Skills & Culture

What AI/ML skills currently exist in-house (data science, engineering, prompt engineering, etc.)?
How do teams currently experiment with or adopt new AI tools?
What cultural or change-management challenges do you anticipate?
Are there any “AI champions” or resisters we should be aware of?

Operating Model & Processes

Which business processes are most manual or inefficient today?
How are cross-functional AI initiatives currently managed?
What would need to change in your operating model to scale AI successfully?

Use Case Identification & Value Delivery

What are the biggest pain points or opportunities where AI could have high impact?
Have any use cases already been identified or attempted?
How do you currently measure success of technology or transformation initiatives?
If we could only prioritize 3 AI opportunities, which ones would move the needle most?

Closing (all interviews):

Is there anything else we should know that we haven’t asked?
Who else should we speak with?
What would make this audit most valuable for you personally?

3. Document & System Review Checklist

Requested in Intake:

Organization chart / key stakeholder list
Current tech stack diagram or list
Data governance / privacy policies
Any existing AI strategy or roadmap documents
Security / compliance documentation
List of current data sources and systems (CRM, ERP, etc.)
Sample data access (anonymized where needed)

During Review:

Architecture & integration points
Data quality samples (completeness, freshness, lineage)
Existing AI pilots or shadow IT usage
Procurement / vendor management for AI tools

4. Scoring Rubric (1–5 Maturity Scale)

Level 1 – Initial / Ad-hoc: No formal approach. Reactive, inconsistent. Level 2 – Managed: Some processes exist but siloed and undocumented. Level 3 – Defined: Standardized processes and policies across the organization. Level 4 – Quantitatively Managed: Data-driven decision making and measurement in place. Level 5 – Optimizing: Continuous improvement, innovation, and automation embedded.

Score each of the 7 pillars based on interview evidence + document review. Use radar chart in final report.

5. Use Case Prioritization Framework

For each potential use case, rate:

Business Impact (1–5): Revenue, cost savings, risk reduction, customer experience
Feasibility (1–5): Data availability, tech fit, skills, effort, timeline
Strategic Alignment (1–5)
Risk Level (Low/Med/High)

Prioritization Score = (Impact × 0.4) + (Feasibility × 0.3) + (Alignment × 0.3)

Top 10–20 go into the roadmap. Quick wins (high feasibility + decent impact) highlighted first.

6. Report Template Outline (Core Tier)

Executive Summary (2 pages)

Key findings & maturity snapshot
Top 5 recommended opportunities with estimated ROI range
Recommended first 90-day actions

Current State Assessment

7-pillar maturity scorecard + radar chart
Evidence & quotes from stakeholders
Gap analysis

Prioritized Opportunity Portfolio

Table or cards: Use case name, description, impact, feasibility, data/tech needs, risks, rough ROI/effort

Implementation Roadmap

Phase 1: Quick Wins (0–3 months)
Phase 2: Foundations (3–12 months)
Phase 3: Scale & Optimize (12–36 months)
Owners, milestones, dependencies

Risk Register & Recommendations

Key risks + mitigation
Investment guidance
Next steps & support options

Appendices

Interview list & summaries
Data sources reviewed
Scoring methodology

7. Workshop Agenda (Half-Day Virtual – Core Tier)

Total: 3.5–4 hours

Opening & Objectives (15 min)
Maturity Snapshot Review (30 min) – Present scores + key evidence
Opportunity Deep Dive (60 min) – Walk through top prioritized use cases
Break (10 min)
Roadmap Co-Creation (60 min) – Prioritize phases, assign owners, identify quick wins
Risks & Dependencies (20 min)
Next Steps & Support (15 min)
Q&A & Close

Pre-work for client: Review draft report (sent 48h before)

8. Delivery Checklist (Before Handover)

All interviews completed & notes synthesized
Documents reviewed
Scores calculated & validated internally
Draft report sent to client 48h before workshop
Workshop scheduled & pre-work sent
Editable roadmap + backlog prepared
Presentation slides ready
30-day support calendar blocked

9. Post-Delivery Support (30 Days)

Two scheduled 30-min calls
Email support for questions
Help scoping first pilot if requested
Transition to implementation proposal (upsell)

10. Templates & Assets to Prepare

Intake form / scoping questionnaire
Interview guide (this document)
Scoring rubric spreadsheet
Report template (Google Doc or Word)
Roadmap template (Excel + Notion)
Workshop slide deck template
Client welcome packet
NDA template
Proposal / SOW template

Pro Tip for High Margins: Use Claude or similar to synthesize interview notes, draft report sections, and generate use case descriptions. One senior consultant + AI can deliver Core tier in ~25–35 hours of focused work.

This playbook + the accompanying sales page and business plan gives you everything needed to launch and deliver AI audits profitably at scale.

Customize branding, adjust pricing for your market, and start with 3–5 paid audits to refine delivery. Then systemize with templates and AI assistance.

Ready to execute.