AI Readiness & Data Strategy Assessment

A comprehensive diagnostic consulting engagement that provides a 360-degree assessment of your company’s preparedness to successfully implement artificial intelligence. We evaluate your data, technology, people, and processes to produce a clear “Readiness Scorecard” and actionable data strategy.

Replace assumptions with evidence and avoid the common pitfalls that lead to AI project failure. Our objective assessment answers the critical question: “Are we truly ready for AI?” before you make significant investments.

Designed for mid-market to enterprise companies considering their first major AI initiative or those who have experienced past data project failures and want to ensure success this time.

What to Expect

Our 4-6 Week Comprehensive Assessment:

Discovery & Scoping (Week 1)

• Kickoff workshop with leadership to align on target AI use case
• Define scope and identify key stakeholders for discovery sessions
• Schedule interviews with data owners, IT infrastructure managers, and business unit heads

Multi-Pillar Deep Assessment (Weeks 2-3)

• Data Readiness Analysis: Quality, quantity, accessibility, and relevance of required data; identify silos and integrity issues
• Technology & Infrastructure Review: Current tech stack assessment including databases, data warehousing, cloud infrastructure, and API capabilities
• People & Skills Evaluation: Team skills assessment, AI literacy evaluation, and critical skill gap identification
• Process & Governance Audit: Data governance policies, security protocols, compliance considerations, and decision-making processes

Gap Analysis & Strategy Development (Week 4)

• AI Readiness Scorecard development based on comprehensive findings
• Prioritized list of actionable recommendations to improve readiness
• Foundational Data Strategy construction

Your AI Readiness Report Includes

• Executive Summary: Clear overview of your company’s overall AI readiness
• AI Readiness Scorecard: Visual dashboard scoring your company 1-5 across all four pillars
• Detailed Gap Analysis: Specific issues and recommendations (e.g., data fragmentation, duplication rates)
• Actionable Data Strategy: Prioritized roadmap of foundational projects to close gaps
• Go/No-Go Recommendation: Official recommendation on proceeding with large-scale AI projects

Business Outcomes

• Massively De-risked AI Investment: “Measure twice, cut once” assurance preventing destined-to-fail projects
• Clear Data Strategy: Concrete plan to turn your data into a strategic asset
• Budgetary Clarity: True foundational costs required for AI success
• Internal Consensus: Shared understanding of current capabilities and improvement needs
• Accelerated Long-Term Success: Solid foundation ensures faster, more successful AI applications with higher ROI