Client Discovery & AI Readiness Assessment

Run a structured discovery process for a prospective consulting client. Gathers business context, assesses current technology, evaluates AI maturity, researches the competitive landscape, and produces a complete client profile with readiness scoring.

Step 1 — Intake Interview

Conduct a structured intake interview using AskUserQuestion. Questions are organized into three groups. If the user provided a <brief> after --, scan it first and skip any questions already answered. For skipped questions, confirm the extracted answer with the user.

Group A — Business Context (5 questions)

Ask these questions one group at a time. Present all questions in the group together so the user can answer in a single response.

Business Context — please answer the following:

1. What is the company's full legal name and any brand names?
2. What is the company size? (headcount and/or revenue range)
3. What industry or vertical does the company operate in? (e.g., fintech, healthcare, logistics, retail)
4. What is the approximate annual revenue range? (e.g., <$1M, $1-10M, $10-50M, $50-200M, $200M+)
5. What is the primary business model? (e.g., SaaS, marketplace, services, e-commerce, manufacturing)

Group B — Current Technology (4 questions)

Current Technology — please answer the following:

6. What is your current tech stack? (languages, frameworks, cloud providers, key SaaS tools)
7. Describe your data infrastructure. (databases, data warehouses, ETL pipelines, BI tools)
8. Are you currently using any AI/ML? If so, what? (e.g., chatbots, recommendation engines, predictive models, none)
9. What is the size and structure of your IT/engineering team? (e.g., 5 engineers, 2 data analysts, outsourced dev shop)

Group C — Pain Points & Goals (4 questions)

Pain Points & Goals — please answer the following:

10. What are your top 3 business pain points right now?
11. What are your goals for AI adoption? (e.g., reduce costs, improve CX, new revenue streams, competitive advantage)
12. What is your timeline expectation for seeing results? (e.g., 1-3 months, 3-6 months, 6-12 months)
13. What is your approximate budget range for an AI initiative? (e.g., <$50K, $50-150K, $150-500K, $500K+)

Record all answers verbatim. If a user declines to answer a question, record "Not disclosed" and move on.

Step 2 — Current State Analysis

Using the intake answers, produce the following analyses:

2a. Business Model Summary

Write a 2-3 paragraph narrative summary covering:

• What the company does and how it makes money
• Key customer segments and value propositions
• Market position and competitive dynamics

2b. Tech Stack Diagram

Generate a Mermaid architecture diagram showing:

• Frontend / client layer
• Backend / API layer
• Data stores and pipelines
• Third-party integrations
• Current AI/ML components (if any)

graph TB
    subgraph "Client Layer"
        A[Web App] --> B[API Gateway]
        C[Mobile App] --> B
    end
    subgraph "Backend"
        B --> D[Application Server]
        D --> E[Database]
        D --> F[Cache]
    end
    subgraph "Data"
        E --> G[ETL Pipeline]
        G --> H[Data Warehouse]
        H --> I[BI Dashboard]
    end

Customize this template based on the client's actual stack.

2c. Data Readiness Score

Score the client 1-5 on each of the following five dimensions. Use the intake answers and apply professional judgment.

Dimension	1 (Poor)	2 (Basic)	3 (Developing)	4 (Strong)	5 (Excellent)
Volume	Minimal data, mostly manual	Some digital records	Moderate structured data	Large structured datasets	Massive, diverse data assets
Quality	No standards, high error rates	Ad hoc cleaning	Some validation rules	Systematic quality processes	Automated quality pipelines
Accessibility	Data locked in silos	Some shared access	Central repository exists	Well-organized data catalog	Self-service data platform
Governance	No policies	Informal practices	Some documented policies	Formal governance program	Mature governance with automation
Integration	No integration	Manual exports/imports	Some API connections	Integrated data pipelines	Real-time unified data layer

Calculate composite Data Readiness Score = average of 5 dimensions (round to 1 decimal).

2d. Team Capability Matrix

Create a matrix of the client's team capabilities relevant to AI adoption:

Capability	Current Level	Gap to AI-Ready	Priority
Data Engineering	Low/Medium/High	Description of gap	High/Medium/Low
ML/AI Development	Low/Medium/High	Description of gap	High/Medium/Low
Data Science/Analytics	Low/Medium/High	Description of gap	High/Medium/Low
Product Management (AI)	Low/Medium/High	Description of gap	High/Medium/Low
Change Management	Low/Medium/High	Description of gap	High/Medium/Low

Step 3 — AI Maturity Assessment

Score the client 1-5 across five maturity dimensions using this rubric:

Maturity Scoring Rubric

Strategy (weight: 1x)

• 1: No AI strategy, no executive awareness
• 2: Executive curiosity, no formal plan
• 3: AI included in strategic discussions, informal roadmap
• 4: Formal AI strategy with budget and governance
• 5: AI-first culture, AI embedded in corporate strategy

Data (weight: 1x)

• 1: Siloed, inconsistent, no governance
• 2: Some centralization, basic quality standards
• 3: Data warehouse/lake in place, documented governance
• 4: Integrated data platform, active data quality programs
• 5: Real-time data fabric, self-service analytics, ML-ready pipelines

Technology (weight: 1x)

• 1: Legacy systems, no cloud, no ML infrastructure
• 2: Some cloud adoption, basic APIs
• 3: Cloud-native apps, CI/CD, some ML tooling
• 4: MLOps platform, feature stores, model registry
• 5: Full ML platform, automated retraining, edge deployment

People (weight: 1x)

• 1: No AI/ML skills in org
• 2: 1-2 data analysts, no ML engineers
• 3: Small data team, some ML experience
• 4: Dedicated AI team with ML engineers and data scientists
• 5: Distributed AI expertise, AI champions in every department

Process (weight: 1x)

• 1: No AI-related processes
• 2: Ad hoc experimentation
• 3: Defined ML development lifecycle
• 4: Standardized MLOps, model monitoring, A/B testing
• 5: Continuous AI improvement, automated model governance

Composite Score Calculation

Composite Score = Strategy + Data + Technology + People + Process
Range: 5 to 25

Readiness Level Mapping

Composite Score	Readiness Level	Description
1-5	Not Ready	Foundational gaps across all dimensions. Focus on data and strategy basics first.
6-10	Early Stage	Some awareness and early efforts. Needs structured roadmap and capability building.
11-15	Developing	Active efforts underway. Ready for targeted AI pilots with guided support.
16-20	Advancing	Strong foundation in place. Ready for scaled AI deployment and advanced use cases.
21-25	AI-Native Ready	Mature across all dimensions. Focus on optimization, innovation, and competitive moats.

Present scores as a radar chart description and a summary table:

AI Maturity Scorecard
=====================
Strategy:   [X] / 5  ████░░░░░░
Data:       [X] / 5  ██████░░░░
Technology: [X] / 5  ████████░░
People:     [X] / 5  ██░░░░░░░░
Process:    [X] / 5  ████░░░░░░

Composite:  [XX] / 25
Readiness:  [Level Name]

Step 4 — Competitive Landscape Research

Use WebSearch to research the competitive landscape. Execute the following searches:

1. "[industry] AI adoption trends 2025 2026" — identify industry-wide AI trends
2. "[top competitor 1] artificial intelligence" — specific competitor AI initiatives
3. "[top competitor 2] AI machine learning" — second competitor if known
4. "[industry] AI case studies ROI" — find relevant case studies with ROI data

From the search results, compile:

• Industry AI Adoption Rate: What percentage of peers are adopting AI?
• Competitor AI Initiatives: What are direct competitors doing with AI?
• Relevant Case Studies: 2-3 case studies from the industry with quantified results
• Emerging Opportunities: AI applications gaining traction in this vertical
• Threats: Risk of falling behind if AI is not adopted

Summarize findings in a competitive intelligence brief (max 500 words).

Step 5 — Output Files

Create the following files in the client's directory at consulting-toolkit/clients/<client-name>/:

5a. profile.md

Client profile summary containing:

• Company name, industry, size, revenue range
• Business model summary (from Step 2a)
• Key contacts and stakeholders (if provided)
• Engagement date
• AI Maturity composite score and readiness level

5b. discovery/intake.md

Full intake interview record:

• All 13 questions with verbatim answers
• Any additional context provided
• Date of intake

5c. discovery/current-state.md

Current state analysis containing:

• Business model narrative (from Step 2a)
• Tech stack Mermaid diagram (from Step 2b)
• Data readiness scores table (from Step 2c)
• Team capability matrix (from Step 2d)

5d. discovery/maturity-assessment.md

AI maturity assessment containing:

• Individual dimension scores with justification
• Composite score and readiness level
• ASCII scorecard visualization
• Key strengths and gaps identified
• Recommended focus areas for improvement

5e. discovery/stakeholder-map.md

Stakeholder analysis containing:

• Decision makers (who approves budget and strategy)
• Influencers (who shapes technical direction)
• Champions (who will drive adoption internally)
• End users (who will use AI-powered tools)
• Blockers (who may resist change and why)

If stakeholder information was not provided during intake, create the template with placeholder sections and note that this should be filled in during follow-up conversations.

Notes

• All output files use Markdown format
• Mermaid diagrams should be valid and renderable
• Scores should be justified with specific evidence from the intake
• If the client directory already exists, warn the user and ask whether to overwrite or create a versioned copy
• The entire discovery process should take 15-20 minutes with a cooperative client