Pillar 1: Strategy

1. How is your organization’s AI strategy defined and communicated?

Initial: We have no formal or documented AI strategy.

Developing: AI initiatives are discussed in isolated pockets or departments.

Defined: We have a centrally defined AI strategy that is beginning to be communicated.

Integrated: Our AI strategy is fully integrated with our overall business strategy and is widely understood.

2. How are AI projects funded and prioritized?

Ad-Hoc: Funding is opportunistic and lacks a formal process.

Departmental: Individual departments fund their own AI experiments.

Centralized: There is a central budget for AI projects with a defined prioritization framework.

Portfolio-Based: AI is a dedicated investment portfolio, managed for long-term business value and ROI.

Pillar 2: Talent

3. What is the state of AI-related talent and skills within your organization?

Lacking: We have very few individuals with the necessary AI/data science skills.

Concentrated: We rely on a few key individuals or external consultants for AI projects.

Growing: We have dedicated data science/AI teams and are investing in training programs.

Embedded: We have a strong internal talent pipeline and a culture of continuous AI learning.

4. How do business and technical teams collaborate on AI initiatives?

Siloed: There is little to no collaboration between business and technical teams.

Hand-offs: Collaboration is limited to formal hand-offs and requirements gathering.

Cross-Functional: We use cross-functional teams for specific AI projects.

Fused: Business and tech teams are fully integrated, co-owning AI initiatives from ideation to production.

Pillar 3: Technology

5. How would you describe your organization’s data infrastructure and accessibility?

Fragmented: Data is siloed, inaccessible, and we lack appropriate tools.

Basic: We have a basic data warehouse, but data access and quality are frequent challenges.

Modernized: We have a modern, cloud-based data platform and are standardizing our AI/ML tools.

Democratized: We have a unified data fabric, enabling self-service and real-time data access for AI models.

6. How are AI models deployed and managed in production?

Manual: Deployment is a manual, infrequent, and risky process.

Scripted: We have scripts for deployment, but monitoring and management are reactive.

Automated: We use CI/CD pipelines for automated deployment and have basic model monitoring.

MLOps Driven: We have a highly automated MLOps platform for continuous training, deployment, and governance.

AI Maturity Ladder Assessment

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