AI Consultant

Guide a seasoned AI consultant through structured client engagements — from initial research through deliverable generation — with emphasis on generative AI and AI agents.

Overview

This skill provides a repeatable engagement framework for AI consulting. It assumes you are an experienced AI/ML practitioner who needs structured guidance on the consulting process: who to talk to, what to ask, how to assess readiness, how to prioritize opportunities, and how to package findings into professional deliverables.

The skill does NOT teach you AI — it teaches you how to run an AI consulting engagement systematically so nothing falls through the cracks.

When to use

• The task is pre-implementation AI advisory work for a client or business unit.
• The user needs discovery artifacts such as interview guides, readiness assessments, opportunity maps, proposals, SOWs, or roadmaps.
• The core question is strategic: where AI fits, what to prioritize, what it should cost, or how to scope the engagement.
• The deliverable is consulting guidance, not production code or deployed systems.

Do NOT use when:

• The task is building, integrating, fine-tuning, or deploying an AI system.
• The user already has an approved scope and needs execution instead of advisory framing.
• The request is generic AI education with no client-engagement or strategy component.

Response format

Always structure the final response with these top-level sections, in this order:

1. Summary — state the task, scope, and main conclusion in 1-3 sentences.
2. Decision / Approach — state the key classification, assumptions, or chosen path.
3. Artifacts — provide the primary deliverable(s) for this skill. Use clear subheadings for multiple files, commands, JSON payloads, queries, or documents.
4. Validation — state checks performed, important risks, caveats, or unresolved questions.
5. Next steps — list concrete follow-up actions, or write None if nothing remains.

Rules:

• Do not omit a section; write None when a section does not apply.
• If files are produced, list each file path under Artifacts before its contents.
• If commands, JSON, SQL, YAML, or code are produced, put each artifact in fenced code blocks with the correct language tag when possible.
• Keep section names exactly as written above so output stays predictable across skills.

Workflow

Phase 1: Pre-Engagement Research

Before any client conversation, build a briefing. This prevents wasting discovery time on publicly available information.

1. Web search the company: recent news, earnings, press releases, leadership changes, industry position, competitors
2. Identify their tech stack from job postings, engineering blogs, conference talks, or public repos
3. Research existing AI tool adoption — look for signals of enterprise AI licensing (Microsoft Copilot, Google Gemini/Duet AI, ChatGPT Enterprise, Amazon Q, Salesforce Einstein, etc.) from partner announcements, job postings, or press releases. Knowing what they already pay for prevents recommending duplicate capabilities and reveals quick-win opportunities from underutilized licenses.
4. Research industry AI trends — what are their competitors doing with AI? What are analysts saying about AI in this vertical?
5. Check regulatory landscape — are they in a regulated industry? What compliance constraints apply to AI? (HIPAA, SOX, GDPR, etc.)
6. Draft a company briefing document with:
   • Company overview (size, revenue, industry, key products/services)
   • Likely pain points based on industry patterns
   • Known or suspected AI tools already in use (and estimated utilization)
   • Competitor AI initiatives
   • Regulatory considerations
   • Preliminary hypotheses about where AI could help

Save this as company-briefing.md. This becomes your conversation anchor.

Phase 2: AI Maturity Assessment

Score the client across 5 dimensions using the framework in references/ai-maturity-model.md. Each dimension is rated 1-5.

Dimension	What you're assessing
Data	Quality, accessibility, governance, cataloging
Infrastructure	Cloud readiness, compute, MLOps, API architecture
Talent	AI/ML skills, data literacy, willingness to upskill
Governance	AI policy, ethics framework, risk management, approval
Culture	Innovation appetite, change tolerance, exec sponsorship

Produce an ai-maturity-assessment.md with scores, evidence, and recommendations per dimension. The aggregate score determines engagement approach:

• Score 1-2: Foundation-building engagement (data strategy, infrastructure)
• Score 2-3: Pilot-ready (identify quick wins, build proof points)
• Score 3-4: Scale-ready (operationalize existing efforts, expand use cases)
• Score 4-5: Optimization (advanced capabilities, competitive differentiation)

Phase 3: Stakeholder Discovery

Use the question bank in references/question-bank.md to guide interviews. The order matters — start at the top for strategic context, then drill down.

Interview sequence:

1. Executive sponsor (CEO/COO/CTO) — strategic vision, budget authority, success metrics, timeline expectations
2. Department heads (operations, sales, customer service, finance) — pain points, process bottlenecks, data sources, team readiness
3. IT/Data team (CTO, VP Eng, data engineers) — infrastructure, data architecture, integration constraints, security requirements
4. Frontline workers — daily workflows, manual processes, friction points, tool frustrations

For each interview, capture:

• Current-state process description
• Pain points (ranked by business impact)
• Data sources and accessibility
• Existing AI tools in use (licensed, shadow AI, utilization rates)
• Appetite for change (1-5)
• Specific AI ideas they've already considered

Produce a stakeholder-discovery.md summarizing findings across all interviews.

Phase 4: Red Flag Detection

Before investing time in opportunity mapping, check for engagement killers. These are conditions that make AI initiatives likely to fail regardless of technical approach.

Red Flag	Severity	Mitigation
No executive sponsor	Critical	Pause engagement until sponsor identified
No data strategy or data is siloed/inaccessible	High	Recommend data foundation work first
Unrealistic expectations ("AI will 10x revenue in 3 months")	High	Reset expectations with industry benchmarks
Political resistance from key stakeholders	High	Identify champions, propose change management
No budget allocated or "we'll figure out budget later"	Medium	Get budget range commitment before scoping
Previous failed AI initiative with unresolved blame	Medium	Address explicitly — diagnose what went wrong
Compliance/legal has not been consulted	Medium	Loop them in immediately
No plan for GenAI-specific risks (hallucination, data leakage, prompt injection)	Medium	Require GenAI risk assessment before any GenAI deployment
Paying for enterprise AI licenses nobody uses	Medium	Audit utilization — quick win may be adoption, not new tooling

Document flags found in red-flags.md with specific mitigation plans. Also include a "Positive Indicators" section documenting favorable conditions (e.g., strong exec sponsor, data team enthusiasm) — because knowing what IS working is as valuable as knowing what isn't. If critical red flags exist, address them before proceeding to Phase 5.

Phase 5: Opportunity Identification & Prioritization

Map every AI opportunity discovered during stakeholder interviews. For each opportunity, score on two axes:

Business Impact (1-5):

• Revenue potential or cost savings
• Strategic alignment
• Number of people/processes affected
• Customer experience improvement

Feasibility (1-5):

• Data readiness (exists, accessible, clean enough)
• Technical complexity
• Integration difficulty
• Organizational readiness (skills, change management)
• Time to first value

Plot opportunities on an impact-vs-feasibility matrix:

	High Feasibility	Low Feasibility
High Impact	Quick Wins — do these first	Strategic Bets — plan carefully
Low Impact	Fill-ins — only if resources allow	Deprioritize

For every opportunity in the matrix, include these three assessments (not just top priorities — all scored opportunities need them because they inform comparison):

• Build vs Buy vs Already Licensed — is there an off-the-shelf solution, or does the client already have a licensed tool that covers this? Check existing enterprise AI investments first. See references/pricing-guide.md for effort estimates
• GenAI vs Traditional ML — does this need generative AI, or would traditional ML/analytics suffice?
• Agent potential — could this be an autonomous AI agent workflow?

Produce opportunity-matrix.md with scored and prioritized opportunities.

Phase 6: Deliverable Generation

Generate the appropriate deliverables based on engagement scope. Use templates in references/deliverable-templates.md.

Core deliverables (always produce):

1. Executive Summary — 1-2 page overview for leadership. Findings, top 3 recommendations, estimated ROI, proposed timeline
2. Discovery Report — comprehensive findings document. Company briefing, AI maturity assessment, stakeholder findings, opportunity matrix, red flags, recommendations
3. Roadmap — phased implementation plan (30/60/90 day or quarterly). Include dependencies, milestones, resource requirements

Engagement-dependent deliverables (produce when scoping specific projects):

4. Statement of Work (SOW) — must include: scope (in-scope AND out-of-scope), deliverables, timeline, pricing, assumptions, exclusions, and acceptance criteria. The assumptions and exclusions sections are critical for preventing scope creep — never skip them
5. Proposal — client-facing sales document. Problem statement, proposed solution, approach, team, timeline, investment, ROI case
6. ROI Analysis — per-initiative financial projections. Cost model, benefit model, payback period, sensitivity analysis

Stakeholder-specific packaging:

• Board/C-suite: Strategic alignment, ROI, competitive positioning
• Engineering: Architecture diagrams, integration points, technical approach
• Operations: Workflow changes, training plan, timeline, support model
• Finance: Cost breakdown, payment terms, ROI projections, risk factors

Phase 7: Industry-Specific Considerations

Check references/industry-playbooks.md for vertical-specific guidance. High-level playbooks are available for:

• Fintech — fraud detection, risk scoring, document processing, compliance automation, customer service agents
• Healthcare — clinical documentation, diagnostic support, patient engagement, operational efficiency, regulatory constraints (HIPAA)
• Manufacturing — predictive maintenance, quality control, supply chain optimization, demand forecasting, safety monitoring

For unlisted industries, apply the general framework and research industry-specific AI adoption patterns via web search.

Checklist

• Company briefing researched and documented
• Existing AI tools and licenses inventoried (including utilization)
• AI maturity assessment scored across all 5 dimensions
• Stakeholder interviews planned with role-specific questions
• Stakeholder discovery findings documented
• Red flags identified and mitigation plans created
• Opportunities identified, scored, and prioritized on matrix
• Build vs buy assessed for top opportunities
• Executive summary drafted
• Discovery report compiled
• Roadmap with phased milestones created
• SOW/Proposal generated (if scoping specific projects)
• Deliverables packaged for each stakeholder audience
• Industry-specific considerations applied

Common mistakes

Mistake	Fix
Skipping pre-engagement research and wasting discovery on public info	Always do Phase 1 first — clients respect prepared consultants
Jumping straight to solutions without understanding maturity	A company at maturity level 1 needs data foundations, not an AI agent fleet
Treating all stakeholders the same	CFO wants ROI, CTO wants architecture, ops wants workflow impact — tailor messaging
Ignoring red flags to keep the engagement moving	Address them early — failed projects destroy consulting relationships
Proposing GenAI for everything	Traditional ML or even simple automation may be more appropriate for many use cases
Scoping too many initiatives at once	Recommend 1-2 quick wins plus 1 strategic bet. Prove value, then expand
Not including change management in the SOW	AI projects fail more from adoption issues than technical ones
Vague SOW scope that invites scope creep	Be explicit about inclusions, exclusions, and assumptions
Scoping without understanding the client's capacity	Get budget and team signals early — don't propose 5,000 engineer-hours to a team with no AI staff
Ignoring existing AI tool investments	Always inventory what's already licensed — configuring an underused Copilot deployment is cheaper than building from scratch
Writing deliverables in AI jargon	Match the audience — execs want business outcomes, not model architectures

Key principles

1. Research before you ask — never waste a client's time asking questions you could have answered with 10 minutes of web research. It signals preparation and builds trust immediately.

2. Maturity determines strategy — the right AI recommendation depends entirely on where the client is today. A maturity-1 company needs data foundations; a maturity-4 company needs optimization and differentiation. Match your recommendations to their reality.

3. Quick wins fund strategic bets — always identify at least one high-impact, high-feasibility opportunity that can show value in 30-60 days. This builds credibility and budget for larger initiatives.

4. Deliverables are stakeholder-specific — the same findings need different packaging for different audiences. A board deck is not a technical architecture document. Write for your reader, not for yourself.

5. Red flags are gifts — discovering engagement risks early is valuable, not discouraging. Addressing them proactively differentiates good consultants from bad ones.