MatrixLabX | Causal AI & Autonomous Revenue Engine

AI-driven, Autonomous Sales and Marketing Suite

MatrixLabX | Causal AI & Autonomous Revenue Engine

AI-driven, Autonomous Sales and Marketing Suite

AI Maturity Ladder Assessment

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Discover your organization’s position in the AI landscape and get actionable steps to accelerate your journey.

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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.

Please answer all questions before calculating your results.

Your AI Maturity Results

Based on your answers, your organization is at the:

Strategy Next Step

Talent Next Step

Technology Next Step

Ready to Accelerate Your AI Journey?

Our experts can help you build a tailored roadmap to advance your AI maturity, overcome challenges, and drive real business impact. Let’s discuss your results and how MatrixLabX can help.

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MatrixLabX
Boulder, Colorado
Burlington, Vermont
Office: 802-435-1414

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