AI Maturity Assessment
Evaluate your organization's AI readiness across 6 key dimensions
AI Maturity Assessment
Evaluate your organization's AI readiness across 6 key dimensions
Section 1 of 6
Governance & Strategy
Q1
Is there a clearly documented AI/Automation strategy aligned with business goals, supported by leadership ownership, accountability, and structured budget allocation?
Q2
Are responsible AI principles—including ethics, compliance, and risk governance—formally established, communicated, and understood across leadership?
Q3
Does the organization maintain a short-, medium-, and long-term AI roadmap that is reviewed periodically and integrated into the broader strategic transformation plan?
Q4
To what extent does the organization have dedicated AI leadership or a formal team responsible for decision-making, oversight, and driving AI initiatives?
Section 2 of 6
Organization, People & Skills
Q1
Does the organization have the right AI/RPA/Automation roles and talent in place, and how effective is it at attracting and retaining AI/ML professionals?
Q2
Are employees supported with ongoing training, upskilling, and a defined competency framework for AI, RPA, and data-driven roles?
Q3
How well do cross-functional teams collaborate (business, IT, data, operations) to deliver AI and automation initiatives?
Q4
To what extent does the organization leverage external partnerships and foster a culture of innovation, experimentation, and data-driven decision-making?
Section 3 of 6
Process & Operational Readiness
Q1
Are business processes well documented and standardized before automation?
Q2
Are automation delivery methods standardized (SDLC, RPA/ML-specific SOPs)?
Q3
To what extent are business processes digitized and ready for AI-driven automation?
Q4
How prepared is the organization to update and adapt its processes in response to evolving AI technologies and regulatory changes?
Section 4 of 6
Data, Technology & Infrastructure
Q1
Does the organization have the required infrastructure and mature data pipelines/ETL processes to support AI at scale?
Q2
Are data quality and governance practices monitored and enforced?
Q3
Are AI/ML development tools, automation platforms, CI/CD pipelines, and security controls standardized and enterprise-ready?
Q4
How advanced and robust is the architecture and API/integration layer for supporting real-time analytics, AI workloads, and enterprise integrations?
Section 5 of 6
Responsible AI, Security & Compliance
Q1
Are there policies for fairness, transparency, explainability, and bias testing?
Q2
Are regulatory requirements (local/global) tracked and adhered to?
Q3
Are AI decisions explainable, auditable, and backed by formal risk assessments before deployment?
Q4
Does the organization follow formal AI frameworks or standards (e.g., ISO/IEC 42001) and maintain readiness for emerging AI regulations (e.g., EU AI Act)?
Section 6 of 6
Use Case Effectiveness & Adoption
Q1
Is there measurable ROI from implemented automation/AI initiatives?
Q2
Are performance metrics (accuracy, throughput, productivity) tracked post-deployment?
Q3
What is the track record of successfully piloting and validating AI use cases?
Q4
How effectively does the organization track and realize business value from AI initiatives?
Final Step
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