AI READINESS ASSESSMENT

Discover your organization's AI maturity level across strategic, technical, and execution dimensions. Complete this quick but comprehensive assessment to receive personalized insights and actionable recommendations (12 questions, ~5 minutes).

Strategic Foundation

Evaluate your organization's AI vision, leadership commitment, resource allocation, and ROI framework

1. How clearly defined is your organization's AI vision and strategy?

A) Comprehensive AI Strategy - We have a detailed, board-approved AI strategy with clear objectives, timelines, and success metrics aligned with business goals.

B) Developing Strategy - We have high-level AI objectives and are actively developing a more detailed strategic framework.

C) Basic Understanding - We recognize AI's importance and have discussed potential applications but lack a formal strategy.

D) Early Exploration - We're just beginning to explore AI possibilities without clear strategic direction.

2. What level of leadership commitment exists for AI initiatives?

A) Executive Champion - C-suite actively champions AI with dedicated leadership roles, regular board updates, and visible commitment.

B) Strong Support - Senior leadership supports AI initiatives with regular engagement and resource allocation approval.

C) Interested but Cautious - Leadership shows interest in AI but takes a wait-and-see approach with limited commitment.

D) Minimal Awareness - Leadership has basic awareness of AI but no active involvement or commitment to initiatives.

3. How adequate are your dedicated AI resources (budget, personnel, time)?

A) Well-Resourced - Substantial dedicated budget, full-time AI team, and allocated executive time for AI initiatives.

B) Adequately Funded - Reasonable budget allocation with part-time dedicated resources and some leadership time commitment.

C) Limited Resources - Small budget with resources borrowed from other departments and minimal dedicated time.

D) Minimal Investment - No dedicated AI budget or personnel, with AI work done as additional responsibilities.

4. How do you measure and track ROI from AI investments?

A) Comprehensive ROI Framework - Established KPIs, regular ROI measurement, and clear business impact tracking with documented success stories.

B) Basic Measurement - Some metrics in place with periodic ROI assessments and general understanding of AI value.

C) Informal Tracking - Informal assessment of AI benefits without systematic measurement or clear ROI framework.

D) No Measurement - No established method for measuring AI ROI or tracking business impact from AI initiatives.

Technical Readiness

Assess your data infrastructure, technical capabilities, expertise, and integration capabilities

7. What level of AI/ML expertise exists within your organization?

A) Deep AI Expertise - Dedicated AI team with data scientists, ML engineers, and AI researchers capable of advanced implementations.

B) Growing Expertise - Some AI-skilled personnel with ongoing training programs and occasional external expertise augmentation.

C) Basic Understanding - Limited AI knowledge among technical staff with heavy reliance on external vendors or consultants.

D) No AI Expertise - No internal AI knowledge or capabilities, requiring complete external support for any AI initiatives.

6. How suitable is your current IT infrastructure for AI workloads?

A) AI-Optimized Infrastructure - Cloud-native or hybrid infrastructure with GPU capabilities, MLOps tools, and scalable computing resources.

B) Capable Infrastructure - Modern infrastructure that can support AI workloads with some additional configuration or resources.

C) Legacy Systems - Older infrastructure that would require significant upgrades to support meaningful AI applications.

D) Inadequate Infrastructure - Current infrastructure cannot support AI workloads without major overhaul or replacement.

5. What is the quality and accessibility of your data for AI applications?

A) AI-Ready Data - High-quality, well-governed data with established pipelines, proper labeling, and easy access for AI teams.

B) Good Data Foundation - Generally good data quality with some accessibility challenges and ongoing data governance improvements.

C) Basic Data Infrastructure - Data exists but requires significant cleaning and organization before AI use.

D) Limited Data Access - Poor data quality, siloed systems, and significant barriers to accessing data for AI applications.

8. How well can your existing systems integrate with AI solutions?

A) Seamless Integration - Modern APIs, microservices architecture, and integration platforms enable easy AI solution deployment.

B) Good Integration Potential - Reasonable integration capabilities with some additional development work required for AI solutions.

C) Limited Integration - Older systems with basic integration capabilities requiring significant custom development work.

D) Poor Integration - Siloed legacy systems with minimal integration capabilities, making AI deployment very challenging.

12. How capable is your organization at scaling successful pilot projects?

A) Excellent Scaling - Proven track record of scaling pilots to enterprise-wide implementations with dedicated scaling processes.

B) Good Scaling - Capability Generally successful at scaling with established processes and adequate resources for expansion.

C) Limited Scaling - Some success scaling pilots but often face resource constraints or organizational barriers.

D) Poor Scaling Record - Pilots rarely progress beyond initial implementation due to organizational or resource limitations.

11. How would you describe your organizational culture toward innovation and AI?

A) Innovation-Driven Culture - Strong culture of experimentation, learning from failure, and enthusiastic adoption of new technologies like AI.

B) Innovation-Friendly - Generally positive attitude toward innovation with willingness to explore AI applications and learn.

C) Cautious but Open - Conservative culture that's slowly warming to innovation and AI with some pockets of enthusiasm.

D) Change-Resistant - Traditional culture with skepticism toward new technology and significant resistance to AI adoption.

10. What governance and risk management frameworks exist for AI?

A) Comprehensive AI Governance - Established AI ethics committee, bias monitoring, risk assessment frameworks, and compliance protocols.

B) Developing Governance - Basic AI governance policies in development with some risk assessment and ethical considerations.

C) Minimal Governance - General IT governance that could apply to AI but no specific AI risk management or ethical frameworks.

D) No AI Governance - No consideration of AI-specific governance, ethics, or risk management requirements.

9. How effective is your change management for technology initiatives?

A) Excellent Change Management - Proven change management framework with high success rates, stakeholder buy-in, and smooth technology adoptions.

B) Good Change Processes - Established change management processes with generally successful technology implementations.

C) Basic Change Management - Informal change processes with mixed success rates and some resistance to new technology adoption.

D) Poor Change Management - No formal change management with frequent implementation failures and strong resistance to change.

Execution Capability

Evaluate your change management, governance frameworks, company culture, and scaling capabilities

AI READINESS ASSESSMENT

Strategic Foundation

Technical Readiness

Execution Capability

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