AI Strategy Assessment Quiz

AI State Maturity Assessment for GCCs

Evaluate your organization's AI readiness across five critical dimensions.

1. Vision & Leadership

1.1 How would you describe your company's AI vision?

No formal AI vision exists. AI vision is discussed informally but not documented. A documented AI vision exists and is shared with key stakeholders. A clear, documented AI vision is integrated into the overall business strategy and widely communicated.

1.2 How is executive leadership involved in the AI strategy?

Minimal to no executive involvement. Executives show interest but provide little active sponsorship. Executives actively sponsor specific AI projects. C-suite actively champions AI, allocates strategic budget, and ties AI outcomes to business KPIs.

1.3 How are AI initiatives funded?

Ad-hoc, project-by-project basis with no dedicated budget. Funded through departmental or IT budgets. A dedicated, centralized budget for AI experimentation exists. Strategic, long-term AI investment portfolio with clear ROI metrics.

1.4 How is success for AI projects measured?

Success is not formally measured. Measured by technical metrics only (e.g., model accuracy). Measured by project-level business outcomes (e.g., cost savings). Measured by strategic business impact and transformation (e.g., new revenue streams, market share).

1.5 How is change management handled for AI implementation?

Change management is not considered. Handled reactively when issues arise. A formal change management plan is created for major AI projects. Proactive, continuous change management is embedded in the AI program to foster adoption and new ways of working.

2. Data & Infrastructure

2.1 How accessible is high-quality data for AI initiatives?

Data is siloed, of poor quality, and difficult to access. Data access requires significant manual effort and cleansing for each project. A centralized data lake or warehouse exists, but data governance is inconsistent. A well-governed, unified data platform provides easy access to high-quality, analysis-ready data.

2.2 What is the state of your technology infrastructure for AI?

Infrastructure is entirely on-premise and not optimized for AI workloads. Some cloud services are used, but primarily for storage or basic compute. A hybrid or cloud-native infrastructure is in place, with access to scalable compute (e.g., GPUs). A scalable, flexible MLOps platform is implemented, automating the end-to-end ML lifecycle.

2.3 How is data governance and security managed?

Ad-hoc and inconsistent data governance. Basic security measures are in place, but governance is not AI-specific. A formal data governance framework exists, covering data lineage, quality, and security. Automated, robust data governance and security are integrated into the data platform, ensuring compliance and trust.

2.4 What tools are available for data scientists and analysts?

Basic tools like spreadsheets. Standard BI tools and some open-source libraries on local machines. A standardized set of advanced analytics and ML tools is provided. A comprehensive, collaborative AI/ML platform with a rich toolset for experimentation, development, and deployment.

2.5 How quickly can a new AI model be moved from prototype to production?

Months, or it's an entirely manual and bespoke process. Several weeks, with significant handoffs between teams. A few weeks, with some automated CI/CD processes. Days, via a fully automated MLOps pipeline with continuous monitoring.

3. Talent & Skills

3.1 What is your strategy for acquiring AI talent?

No specific strategy; we hire as needs arise. Primarily focused on external hiring of experienced specialists. A mix of external hiring and internal upskilling programs. A strategic workforce plan for AI, including internal academies, external hiring, and partnerships with universities.

3.2 How would you describe the AI literacy of the broader organization?

Very low; AI is seen as a black box for technical teams only. Basic awareness exists, but there's a lack of understanding of AI's business potential. Business leaders have a good understanding of AI use cases in their domains. Widespread AI literacy, with a common language between business and tech teams, fostering collaboration.

3.3 How are AI teams structured?

No dedicated AI team; individuals work in silos. A centralized R&D team, isolated from business units. Decentralized teams embedded within business units. A hybrid model (Center of Excellence) that sets standards and supports cross-functional, agile product teams.

3.4 Are there defined career paths for AI professionals?

No, AI roles are not formally defined. Some roles exist but without a clear growth trajectory. Formal career paths exist for technical AI roles (e.g., ML Engineer). Comprehensive career frameworks exist for a range of AI-related roles, including technical, business, and ethical roles.

3.5 What is the culture around experimentation and failure?

Failure is not tolerated. Experimentation is allowed but is risk-averse. "Fail fast" is encouraged in sandboxed environments. A culture of psychological safety exists, where learning from failure is systematic and rewarded.

4. Ethics & Governance

4.1 Is there a formal framework for responsible AI?

No, AI ethics are not formally considered. Ethical principles are discussed but not operationalized. A set of responsible AI principles (e.g., fairness, transparency) has been defined. A comprehensive governance framework with review boards, impact assessments, and clear accountability is in place.

4.2 How do you ensure fairness and mitigate bias in AI models?

Bias is not actively measured or mitigated. Developers are aware of bias but lack formal tools or processes. We use specific tools to detect and measure bias in datasets and models. Bias mitigation is a required, automated step in the ML lifecycle, with continuous monitoring post-deployment.

4.3 How transparent and explainable are your AI models?

Models are treated as "black boxes". Explainability is addressed only when required by regulators. We use explainability techniques (e.g., SHAP, LIME) for key models to understand their decisions. Explainability and transparency are designed into systems from the start, with explanations tailored to different audiences (e.g., customers, operators, regulators).

4.4 Who is accountable for the outcomes of AI systems?

Accountability is unclear. The data science team that built the model is considered accountable. A designated product owner or business leader is accountable. A clear, multi-layered accountability framework exists, defining roles and responsibilities for developers, business owners, and oversight committees.

4.5 How do you manage data privacy in the context of AI?

We follow general data privacy regulations (e.g., GDPR) but have no AI-specific policies. Privacy is reviewed on a case-by-case basis for AI projects. We conduct Privacy Impact Assessments for AI systems and use anonymization techniques. Privacy-by-design is a core principle, and we employ advanced privacy-enhancing technologies (PETs) like federated learning or differential privacy.

5. Innovation & R&D

5.1 How does your organization identify new AI opportunities?

Reactively, based on competitor moves or client requests. Through informal brainstorming within the IT department. Through structured workshops with business units to identify pain points and opportunities. Through a systematic process of market sensing, technology scouting, and strategic ideation linked to business goals.

5.2 How do you engage with the external AI ecosystem?

We do not engage with the external ecosystem. We attend conferences and monitor technology trends. We partner with vendors and have some connections with academic institutions. We have a strategic partnership program with startups, universities, and research labs, and actively contribute to open-source projects.

5.3 How is AI used to create new products or services?

AI is only used for internal process optimization. We have enhanced some existing products with AI features. We are actively developing new, AI-native products and services. Our business model is being transformed by AI, leading to disruptive new offerings and revenue streams.

5.4 How do you manage your portfolio of AI projects?

Projects are managed independently with no central oversight. We track projects but don't manage them as a portfolio. We manage a portfolio of AI projects, balancing short-term wins and long-term bets. We use a sophisticated portfolio management approach, continuously re-evaluating projects against strategic goals and market changes.

5.5 How do you scale successful AI pilots?

Pilots rarely move beyond the proof-of-concept stage ("pilot purgatory"). Scaling is a slow, manual process that re-engineers the pilot from scratch. We have a defined process for evaluating and scaling successful pilots. A clear, repeatable "path to production" exists, enabling rapid scaling of valuable AI solutions across the enterprise.

Your AI Strategy Profile

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