Data Readiness Assessment
Completeness, accuracy, consistency, and timeliness of datasets.
Policies, ownership, lineage tracking, and compliance frameworks.
Storage systems, integration capabilities, and scalability assessment.
Data protection measures, consent management, and compliance.
Technical Infrastructure Assessment
Ability to handle AI workloads and scale efficiently.
APIs, data pipelines, and interoperability evaluation.
Cybersecurity measures specific to AI systems and data.
Pipelines, version control, and model lifecycle management.
Organizational Capability Assessment
Technical skills and AI understanding across departments.
Ability to adapt processes and embrace new technologies.
Executive understanding of AI capabilities and implications.
Openness to experimentation, risk tolerance, and learning.
Process & Workflow Analysis
Workflows and AI automation opportunities.
How decisions are made and where AI could augment them.
Touchpoints where AI could enhance experience.
Metrics for comparison pre/post AI implementation.
Strategic Alignment Assessment
Clarity of AI vision, objectives, and success metrics.
Evaluate AI applications by impact and feasibility.
Measure AI investment returns and value creation.
Risks associated with AI adoption (tech, ops, reputation).
Competitive & Market Analysis
How competitors and leaders are using AI.
AI-driven revenue opportunities and market advantages.
Current/emerging AI regulations affecting industries.
Available AI tools, platforms, and partners.
Financial Readiness Review
Current tech spending and AI investment capacity.
Comprehensive cost modeling for AI initiatives.
Internal budgets, grants, or external investment.
Projected costs, savings, and revenue opportunities.
Governance & Ethics Framework
Principles for responsible AI deployment.
Processes to identify and address algorithmic bias.
Requirements for accountable AI decisions.
Oversight and human-in-the-loop mechanisms.
Skills & Training Assessment
Skills needed for AI success.
Upskilling programs for existing staff.
Plan for acquiring AI talent and expertise.
Capture and share AI learnings across teams.
Performance Measurement Framework
Current performance indicators before AI implementation.
KPIs specific to AI initiatives and outcomes.
Ongoing assessment of AI performance and impact.
Framework for optimizing AI systems over time.