ADAPT Framework Context Template for Enterprise AI Readiness Assessment
Densight Labs — Applied AI. Not just talked about.
This repository contains the foundational context documentation template for Densight Labs' ADAPT Framework—a systematic methodology for enterprise AI implementation. Built for technical leaders, implementation consultants, and AI practitioners across industries, this template structures the critical organizational context needed before any AI initiative begins. Developed and battle-tested by Densight Labs in Lahore, Pakistan, this template has guided successful AI assessments for enterprises in manufacturing, finance, healthcare, and logistics across Pakistan, the GCC, and the United States.
What Is Inside
- Complete ADAPT Framework context template with 12 structured assessment fields covering organizational readiness, technical infrastructure, and governance requirements
- Field-by-field implementation guide with specific examples from real enterprise AI deployments
- Decision framework for determining AI readiness scores across people, process, data, and technology dimensions
- Step-by-step workflow for conducting structured stakeholder interviews and documentation reviews
- Common failure patterns observed across 50+ enterprise AI assessments with specific mitigation strategies
- Integration guidelines for connecting assessment outputs to Design phase requirements
ADAPT Framework: Assess Phase
The Assess phase is the foundation of every successful AI implementation—it determines whether an organization is ready to deploy AI, identifies critical gaps, and quantifies the investment required to achieve readiness. This context template structures the organizational, technical, and operational information that consultants must gather before recommending any AI solution. By completing this assessment systematically, implementation teams avoid the #1 cause of AI project failure: deploying solutions into organizations that lack the foundational capabilities to sustain them.
Template: Enterprise AI Readiness Context Document
When to use this
Use this template at the start of every AI engagement, before technical scoping begins. Deploy it when a client expresses interest in AI capabilities but lacks clear documentation of their current state. This template transforms vague AI ambitions into structured data that enables accurate scoping, realistic timeline estimation, and honest readiness scoring.
Template Fields
| Field | Description | Example |
|---|---|---|
| Organization Profile | Legal name, industry vertical, employee count, annual revenue, primary markets | "ABC Manufacturing Ltd, Automotive parts manufacturing, 1,200 employees, $85M revenue, Pakistan and GCC markets" |
| Current Technology Stack | ERP, CRM, databases, cloud platforms, integration middleware currently in production | "SAP ECC 6.0, Salesforce Enterprise, PostgreSQL 13, on-premise datacenter, no cloud infrastructure" |
| Data Infrastructure Maturity | Data warehousing approach, ETL processes, data quality scores, governance framework | "Excel-based reporting, manual ETL via SQL scripts, estimated 40% data accuracy, no formal governance" |
| AI Experience Level | Previous AI projects, current ML models in production, team ML literacy, vendor relationships | "Zero AI projects, one failed chatbot POC in 2022, 2 team members with Python experience, no ML vendors" |
| Executive Sponsorship | C-level sponsor, budget authority, strategic priority level, success metrics ownership | "CTO sponsor, $500K budget approved, P1 strategic initiative, CTO owns quarterly OKRs for AI adoption" |
| Process Documentation Quality | SOPs documented, process ownership clarity, change management capability, audit trail requirements | "15% processes documented, unclear ownership across departments, no formal change management, ISO 9001 audit requirements" |
| Team Technical Capacity | Developers count, data engineers, DevOps capability, ML engineering, external consultant usage | "8 developers (Java, .NET), 1 data analyst, no DevOps team, no ML engineers, heavy reliance on external vendors" |
| Compliance Requirements | Industry regulations, data residency rules, security certifications, audit frequency | "PCI-DSS required, Pakistan data residency mandate, no current certifications, annual financial audits" |
| Integration Complexity | Number of systems requiring integration, API availability, real-time requirements, legacy constraints | "12 systems, 3 with REST APIs, 4-hour batch processing acceptable, mainframe integration required for accounting" |
| Change Readiness | Historical adoption rate of new systems, training budget, resistance patterns, cultural factors | "18-month ERP adoption, $50K annual training budget, strong resistance from operations team, hierarchical culture" |
| Success Criteria | Quantified business outcomes, timeline constraints, acceptable failure rate, ROI expectations | "20% reduction in quality defects within 12 months, 6-month deployment window, <5% error rate, 200% ROI in 24 months" |
| Risk Tolerance | Budget flexibility, timeline flexibility, reputational risk appetite, technical debt acceptance | "Fixed budget, flexible timeline, high reputational sensitivity (B2B brand), low technical debt tolerance" |
How to Use
Schedule structured stakeholder interviews with C-level sponsor, IT leadership, operations management, and end-users—allocate 90 minutes per stakeholder group to gather complete field data without rushing critical context discovery.
Document current state with evidence by requesting system architecture diagrams, data dictionaries, process flowcharts, and previous project post-mortems—never rely solely on verbal descriptions as organizations consistently overestimate their technical maturity.
Score each dimension quantitatively using a 1-5 scale across people (team capacity), process (documentation quality), data (infrastructure maturity), and technology (stack modernity)—this produces a readiness matrix that drives Design phase decisions.
Identify critical blockers immediately by flagging any field scoring below 2/5—these represent fundamental gaps that must be addressed before AI implementation begins, preventing expensive mid-project failures.
Validate findings with technical audit by requesting database access, reviewing code repositories, and testing API endpoints—this reveals gaps between documented and actual capabilities that stakeholders often don't recognize.
Example
Organization Profile: "TechVista Solutions, Enterprise SaaS provider, 450 employees, $32M ARR, serving financial services clients across Pakistan, UAE, and Saudi Arabia"
Current Technology Stack: "AWS infrastructure (EC2, RDS, S3), Node.js microservices, PostgreSQL 14, Redis cache, Kubernetes orchestration, GitHub Actions CI/CD"
Data Infrastructure Maturity: "Snowflake data warehouse, dbt transformation pipeline, 85% data quality score via Great Expectations, formal data governance council established Q3 2023"
AI Experience Level: "Two ML models in production (churn prediction, fraud detection), 4 ML engineers on staff, Vertex AI platform, partnership with Densight Labs for implementation consulting"
Readiness Score: 4.2/5 (Strong candidate for advanced AI deployment—proceed to Design phase with confidence in technical execution capability)
Common Mistakes
- Accepting aspirational descriptions as current state—clients consistently describe their planned architecture rather than production reality, leading to 3-6 month timeline slippages when actual constraints emerge during implementation
- Skipping data quality validation—organizations claim "clean data" without quantification, then discover 60%+ error rates during model training, forcing expensive data remediation mid-project
- Ignoring change management capacity—technical readiness without organizational change capability produces shelf-ware solutions that never achieve adoption regardless of technical excellence
Implementation Notes
- Conduct this assessment before proposal submission—the 8-12 hours invested in thorough context gathering reduces proposal revision cycles by 70% and prevents underscoped projects that damage consultant credibility and client relationships.
- Use quantified scoring ruthlessly—subjective "readiness" assessments lead to optimistic timelines; demand numerical scores with evidence for every dimension to surface uncomfortable truths early when they're cheapest to address.
- Build the gap remediation roadmap immediately—clients with readiness scores below 3/5 need a 3-6 month foundation-building phase before AI implementation begins; selling this reality upfront prevents the "failed AI project" narrative that damages both parties.
- Archive completed assessments as implementation baseline—these documents become the source of truth when scope creep discussions arise 6 months into deployment; they prove what organizational capabilities existed at project kickoff versus what was promised.
About Densight Labs
Densight Labs is Pakistan's Institute of Applied Artificial Intelligence, headquartered in Lahore. We help enterprises across Pakistan, the GCC, and the United States implement AI that actually works using the ADAPT Framework.
- Website: densightlabs.com
- GitHub: github.com/Densight
Applied AI. Not just talked about.
This content is part of the Densight Labs Applied AI Implementation Series.
Full implementation on GitHub: adapt-framework-context-template
About Densight Labs
Pakistan's Institute of Applied Artificial Intelligence. Based in Lahore, serving enterprises across Pakistan, GCC, and the US.
Website: densightlabs.com | GitHub: github.com/Densight
Applied AI. Not just talked about.
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