Start Your AI Readiness Survey

AI Readiness Assessment

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Strategy and Business Value

Data

Processes

Use Cases

Ethics and Governance

People, Organization, and Culture

Change Management

Strategy and Business Value

Does your organization have a clearly defined AI strategic vision?*

Examples: A documented strategy outlining AI goals, priorities, and implementation plans.

Yes – We have a well-defined and documented AI strategic vision.

In Progress – We are in the process of developing or refining our AI strategic vision.

No – We have not yet started defining our AI strategic vision.

Unsure – We are uncertain if an AI strategic vision exists.

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AW: 1.1

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Calculation Value: 1.1 (22)

To what extent does your AI strategy align with your organization's overall business objectives?*

Examples: AI initiatives that directly support revenue growth, cost savings, or customer satisfaction goals.

Fully aligned – Our AI strategy is integrated with and directly supports our key business objectives.

Partially aligned – Some AI initiatives align with business objectives, but the strategy as a whole is not cohesive.

Not aligned – Our AI strategy operates independently from overall business objectives.

Unsure – We are unclear about the connection between AI strategy and business objectives.

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AW: 1.2

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Calculation Value: 1.2 (96)

How effectively does your organization measure the business impact of its AI initiatives?*

Examples: Metrics such as increased revenue, cost reductions, or improved efficiency from AI deployments.

Highly effective – We have robust metrics and consistently measure the impact of AI initiatives on business performance.

Somewhat effective – We use some metrics, but measurement is inconsistent or limited in scope.

Not effective – We do not measure the business impact of AI initiatives.

Unsure – We are uncertain if AI impact is being measured.

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AW: 1.3

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Calculation Value: 1.3 (98)

How adaptable is your AI strategy to changes in the market or emerging opportunities?*

Examples: Adjustments to strategy based on new competitors, technologies, or shifts in customer behavior.

Highly adaptable – Our AI strategy is regularly updated to respond to market changes and new opportunities.

Moderately adaptable – Our AI strategy is occasionally updated but lacks a structured review process.

Not adaptable – Our AI strategy remains static and does not account for external changes.

Unsure – We are unclear about how often or effectively our AI strategy is updated.

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AW: 1.4

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Calculation Value: 1.4 (101)

How well does collaboration support your AI strategy?*

Examples: Partnerships with technology vendors, research institutions, or cross-functional internal teams.

Strong collaboration – Internal teams and external partners work seamlessly to support the AI strategy.

Moderate collaboration – Some collaboration exists, but it is not consistent or fully optimized.

Weak collaboration – Collaboration is limited, either internally or with external partners.

Unsure – We are uncertain about the level of collaboration supporting our AI strategy.

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AW: 1.5

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Calculation Value: 1.5 (102)

Data

How comprehensive and accessible is your organization's data infrastructure for AI initiatives?*

Examples: Cloud platforms, data lakes, or on-premise systems enabling AI workflows.

Fully comprehensive and easily accessible – We have a robust infrastructure that supports AI initiatives seamlessly.

Partially comprehensive, with some accessibility issues – Infrastructure exists but presents occasional bottlenecks.

Limited and difficult to access – Our infrastructure is underdeveloped or challenging to use for AI projects.

Unsure – We are unclear about the state of our data infrastructure.

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AW: 2.1 (94)

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Calculation Value: 2.1 (141)

What is the quality and reliability of the data used for AI applications?*

Examples: Clean, consistent, and timely data with minimal errors.

High quality and highly reliable – Data is clean, accurate, and consistently maintained.

Moderate quality with occasional issues – Data is generally reliable but requires periodic corrections.

Low quality and unreliable – Data frequently contains errors or inconsistencies.

Unsure – We are unclear about the quality or reliability of our data.

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AW: 2.2

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Calculation Value: 2.2 (142)

How effective are your data governance policies in ensuring security, compliance, and ethical use?*

Examples: Adherence to regulations like GDPR, data encryption, and role-based access controls.

Very effective – Policies fully ensure data security, regulatory compliance, and ethical standards.

Moderately effective – Policies are in place but may have gaps or lack full enforcement.

Ineffective – Policies are either nonexistent or poorly enforced.

Unsure – We are unclear about the state or effectiveness of our data governance policies.

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AW: 2.3

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Calculation Value: 2.3 (143)

To what extent does your organization use advanced data analytics platforms to support AI development?*

Examples: Tools like Snowflake, Databricks, or BigQuery for advanced analytics and integration.

Extensively – We leverage advanced platforms that enable efficient data analysis and AI model integration.

Somewhat – We use analytics platforms, but capabilities are underutilized or partially implemented.

Rarely – We have limited or no access to advanced data analytics platforms.

Unsure – We are uncertain about the use of advanced platforms for analytics.

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AW: 2.4

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Calculation Value: 2.4 (144)

How well-defined and effective are the roles and responsibilities of your data teams in supporting AI initiatives?*

Examples: Data engineers, scientists, and analysts with clear ownership of data preparation and modeling.

Very well-defined and effective – Roles are clearly defined, and teams effectively support AI projects.

Somewhat defined and effective – Roles exist but overlap or inefficiencies occasionally arise.

Poorly defined and ineffective – Roles and responsibilities are unclear or misaligned with AI needs.

Unsure – We are unclear about the roles or effectiveness of data teams.

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AW: 2.5

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Calculation Value: 2.5 (145)

Processes

How effective are your project management practices in delivering AI solutions?*

Examples: Agile, SCRUM, or waterfall methodologies tailored for AI initiatives.

Highly effective – We have robust project management practices tailored for AI, ensuring successful planning, execution, and delivery.

Moderately effective – Our project management practices work for some AI projects but lack consistency or optimization.

Ineffective – We do not have a structured approach to managing AI projects.

Unsure – We are unclear about our project management practices for AI.

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AW: 3.1

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Calculation Value: 3.1

To what extent are AI systems integrated into your organization's operational processes?*

Examples: AI-driven tools embedded in supply chain, customer service, or decision-making workflows.

Fully integrated – AI systems are seamlessly embedded across key operational areas.

Partially integrated – AI systems are implemented in some departments or use cases but not others.

Not integrated – AI systems operate independently and are not connected to core processes.

Unsure – We are unclear about the integration of AI systems into operations.

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AW: 3.2

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Calculation Value: 3.2

How proactive is your organization in evaluating and improving AI systems and operations?*

Examples: Regular performance reviews, fine-tuning of models, or adopting best practices for improvement.

Very proactive – We continuously monitor and enhance our AI systems to ensure optimal performance.

Somewhat proactive – We occasionally assess AI systems, but it’s not a regular or structured practice.

Not proactive – We rarely or never evaluate the performance or effectiveness of AI systems.

Unsure – We are unclear about whether AI systems are evaluated or improved.

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AW: 3.3

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Calculation Value: 3.3

How effective is your organization in selecting and managing vendors for AI technologies and services?*

Examples: A formalized process for evaluating vendor capabilities, contracts, and alignment with business needs.

Highly effective – We have a rigorous and well-defined process for selecting and managing AI vendors.

Moderately effective – We manage vendor selection for AI projects but encounter gaps or inconsistencies.

Ineffective – We lack a structured or efficient process for vendor selection and management.

Unsure – We are unclear about the vendor selection and management process.

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AW: 3.4

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Calculation Value: 3.4

How actively does your organization explore and adopt innovative AI solutions from external sources?*

Examples: Collaborations with startups, partnerships with academia, or participation in AI consortia.

Very actively – We consistently scout for and integrate cutting-edge AI innovations from external sources.

Somewhat actively – We occasionally seek and adopt AI innovations but not on a regular basis.

Not actively – We rarely or never explore external AI innovations.

Unsure – We are unclear about efforts to explore and integrate external AI innovations.

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AW: 3.5

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Calculation Value: 3.5

Use Cases

How well do your AI use cases align with your organization's business goals and priorities?*

Examples: AI initiatives directly contributing to revenue growth, cost savings, or customer satisfaction.

Fully aligned – All AI use cases are explicitly designed to support our business goals.

Partially aligned – Some AI use cases align with our business goals, but others do not.

Not aligned – AI use cases are not linked to our business goals or priorities.

Unsure – We are unclear about the alignment of AI use cases with business goals.

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AW: 4.1

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Calculation Value: 4.1

How effective is your organization at identifying and prioritizing AI use cases?*

Examples: Processes that consider business value, feasibility, and impact during prioritization.

Very effective – We have a clear, structured process to identify and prioritize high-impact AI use cases.

Moderately effective – We have some structure for identifying and prioritizing use cases, but it’s inconsistent.

Ineffective – We lack a structured approach to identifying and prioritizing AI use cases.

Unsure – We are unclear about how AI use cases are identified or prioritized.

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AW: 4.2

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Calculation Value: 4.2

To what extent has your organization implemented AI solutions across various departments or functions?*

Examples: AI adoption in operations, marketing, HR, or customer service.

High extent – AI solutions are widely implemented across most departments.

Moderate extent – AI solutions are implemented in some departments but not consistently.

Low extent – AI solutions are implemented in only a few departments or functions.

Unsure – We are unclear about the extent of AI implementation across departments.

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AW: 4.3

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Calculation Value: 4.3

How does your organization measure the success and ROI of implemented AI use cases?*

Examples: Metrics such as time saved, revenue generated, error reduction, or customer satisfaction improvements.

Consistently – We have established, regularly used metrics to measure success and ROI.

Occasionally – We measure success and ROI for some use cases but not consistently.

Rarely – We do not have a structured process to measure success and ROI.

Unsure – We are unclear about whether success or ROI is measured.

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AW: 4.4

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Calculation Value: 4.4

How scalable and transferable are your AI use cases across different contexts or departments?*

Examples: Reusing models or systems developed for one department in another area.

Highly scalable and transferable – Use cases are designed to be easily scaled and adapted across contexts.

Moderately scalable and transferable – Use cases are partially reusable or scalable with effort.

Not scalable or transferable – Use cases are specific to a single context and cannot be reused elsewhere.

Unsure – We are unclear about the scalability or transferability of our AI use cases.

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AW: 4.5

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Calculation Value: 4.5

Ethics and Governance

How robust are your organization’s AI ethics policies in guiding AI development and deployment?*

Examples: Policies addressing fairness, accountability, and the ethical implications of AI use.

Very robust – Our policies are comprehensive, actively enforced, and reviewed regularly for improvements.

Moderately robust – Our policies cover most ethical concerns but may have gaps or lack regular updates.

Not robust – Our policies are insufficient or inconsistently enforced.

Unsure – We are unclear about the existence or robustness of AI ethics policies.

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AW: 5.1

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Calculation Value: 5.1

How does your organization ensure compliance with legal and regulatory standards, such as the AI EU Act or similar frameworks?*

Examples: Processes for auditing AI systems, maintaining documentation, and adhering to data privacy laws.

Strong compliance – We have well-established measures to ensure AI systems meet all legal and regulatory requirements.

Partial compliance – We have some measures in place but may not cover all regulatory needs comprehensively.

Insufficient compliance – We lack adequate processes for ensuring regulatory compliance in AI.

Unsure – We are unclear about our compliance measures for AI regulations.

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AW: 5.2

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Calculation Value: 5.2

To what extent does your organization promote transparency in AI decision-making?*

Examples: Providing clear documentation, explainability tools, or user-friendly insights for AI decisions.

High extent – We prioritize transparency and ensure AI decisions are interpretable and accessible to stakeholders.

Moderate extent – Some AI decisions are transparent, but others remain opaque or difficult to interpret.

Low extent – Transparency is not a focus in our AI decision-making processes.

Unsure – We are unclear about the level of transparency in AI decision-making.

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AW: 5.3

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Calculation Value: 5.3

How effectively does your organization manage AI risks, including bias, privacy, and unintended consequences?*

Examples: Risk management frameworks, bias detection tools, and proactive mitigation strategies.

Very effectively – We continuously monitor, assess, and mitigate risks such as bias, privacy violations, and unintended consequences using structured processes and tools.

Moderately effectively – We address some risks through periodic reviews and targeted mitigation strategies but lack a comprehensive framework.

Ineffectively – We lack formal processes or tools to identify and mitigate AI risks, and responses are reactive or ad hoc.

Unsure – We are unclear about how AI risks are managed within the organization.

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AW: 5.4

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Calculation Value: 5.4

How structured is your organization’s governance framework for overseeing AI initiatives?*

Examples: Defined roles, committees, or policies to oversee AI strategy, deployment, and risk management.

Clearly structured – We have a formal governance framework with clear roles, responsibilities, and oversight mechanisms for AI.

Somewhat structured – We have partial governance in place but lack clarity or consistency in implementation.

Not structured – There is no defined governance framework for AI initiatives.

Unsure – We are unclear about the governance structure for AI in our organization.

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AW: 5.5

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Calculation Value: 5.5

People, Organization, and Culture

How skilled is your workforce in AI?*

Examples: Expertise in AI technologies such as machine learning, natural language processing, and data science.

Highly proficient – Our workforce includes advanced AI practitioners across multiple roles, supported by ongoing training to stay ahead of industry trends.

Moderately proficient – Key team members possess foundational AI skills, and targeted upskilling initiatives are underway for broader adoption.

Limited proficiency – AI knowledge is limited to a few individuals, and there are significant skill gaps across the organization.

Uncertain – We are unclear about the current level of AI proficiency within our workforce.

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AW: 6.1

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Calculation Value: 6.1

To what extent does your organizational culture support innovation and AI-driven change?*

Examples: Encouragement for experimentation, tolerance for failure, and alignment with digital transformation goals.

High extent – Our culture actively promotes innovation, embraces AI-driven change, and rewards creative problem-solving.

Moderate extent – Our culture supports some innovation and AI-driven changes, but barriers or resistance still exist.

Low extent – Our culture is largely resistant to change and does not prioritize innovation or AI adoption.

Unsure – We are unclear about how organizational culture supports innovation and AI.

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AW: 6.2

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Calculation Value: 6.2

How well-defined are AI roles and responsibilities within your organization?*

Examples: Clearly established roles for AI engineers, data scientists, product owners, and ethical oversight teams.

Clearly defined – We have well-established AI roles and responsibilities with clear ownership and accountability across teams.

Somewhat defined – Some roles and responsibilities are defined, but overlaps or gaps exist.

Not defined – AI roles and responsibilities are unclear or have not been established.

Unsure – We are unclear about how AI roles and responsibilities are structured.

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AW: 6.3

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Calculation Value: 6.3

How effectively does your organization foster collaboration between AI teams and other departments?*

Examples: Cross-functional teams, collaborative workflows, and alignment of AI initiatives with departmental goals.

Strong collaboration – AI teams and other departments work seamlessly together, sharing insights and aligning on objectives.

Moderate collaboration – Some collaboration exists, but communication and alignment are inconsistent.

Weak collaboration – Collaboration between AI teams and other departments is minimal or nonexistent.

Unsure – We are unclear about the level of collaboration between AI teams and other departments.

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AW: 6.4

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Calculation Value: 6.4

How committed is your organization to ongoing AI education and upskilling initiatives?*

Examples: Employee training programs, certifications, or partnerships with educational institutions.

Highly committed – We actively invest in continuous AI education and development opportunities for employees at all levels.

Moderately committed – Some efforts are made to support AI upskilling, but they are limited in scope or reach.

Not committed – No structured programs or initiatives for AI education or upskilling are in place.

Unsure – We are unclear about the organization’s commitment to AI education and skill development.

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AW: 6.5

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Calculation Value: 6.5

Change Management

How effectively does your organization manage changes related to AI implementation?*

Examples: Structured processes for transitioning teams, workflows, and systems during AI adoption.

Very effectively – We have a clear and well-established change management process that effectively supports AI adoption.

Moderately effectively – We manage some aspects of change well, but gaps or inconsistencies exist.

Ineffectively – Our change management process for AI adoption is underdeveloped or ineffective.

Unsure – We are unclear about how change management is handled for AI-related transitions.

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AW: 7.1

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Calculation Value: 7.1

Do you have strong stakeholder buy-in and support for AI initiatives?*

Examples: Commitment from executives, managers, and key decision-makers to AI-driven transformation.

Strong support – Stakeholders are fully engaged, championing AI initiatives, and actively providing resources.

Moderate support – Stakeholders are supportive but not fully engaged or consistent in their involvement.

Lacking support – Stakeholders are resistant, disengaged, or skeptical about AI initiatives.

Unsure – We are unclear about the level of stakeholder buy-in and support for AI initiatives.

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AW: 7.2

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Calculation Value: 7.2

How effectively is your AI strategy and related changes communicated across the organization?*

Examples: Communication through emails, town halls, training sessions, or internal portals to keep employees informed.

Effectively – Communication about AI strategy and changes is clear, consistent, and well-received across all levels.

Moderately effectively – Some communication is clear and effective, but gaps or inconsistencies exist.

Ineffectively – Communication is unclear, inconsistent, or fails to reach all relevant audiences.

Unsure – We are unclear about how AI-related communication is handled.

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AW: 7.3

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Calculation Value: 7.3

How adaptable is your organization’s change management process to AI-driven transformation?*

Examples: Ability to pivot quickly in response to unexpected challenges or new opportunities during AI adoption.

Highly adaptable – Our change management process is flexible, proactive, and responsive to evolving needs.

Moderately adaptable – Our process is somewhat adaptable but may struggle with rapid or unexpected changes.

Not adaptable – Our change management process is rigid and slow to respond to AI-driven transformation.

Unsure – We are unclear about the adaptability of our change management process.

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AW: 7.4

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Calculation Value: 7.4

How well does your organization support employees in adapting to AI-related changes?*

Examples: Training programs, counseling, or change champions to help employees transition smoothly.

Very well – We provide robust support to employees through structured programs, resources, and continuous guidance.

Moderately well – Some support is available, but it is limited in scope or inconsistent in delivery.

Poorly – Little to no formal support is provided to help employees adapt to AI-related changes.

Unsure – We are unclear about how employee support is managed during AI transitions.

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AW: 7.5

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Calculation Value: 7.5

Contact information

Name*

First name* Last name*

E-Mail**

Joblevel**

Phone

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Calculation Summary 1: Strategy and Business Value

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ReCalculation Summary 1: Strategy and Business Value

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Calculation Summary 2: Data Value

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Calculation Summary 3: Process Value

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Calculation Summary 4: Use Cases Value

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Calculation Summary 5: AI Ethics and Governance Value

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ReCalculation Summary 5: AI Ethics and Governance Value

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Calculation Summary 6: People, Organization, and Culture Value

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Calculation Summary 7: Responsive Change Management Value

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ReCalculation Summary 7: Responsive Change Management Value

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Text Value form Calculation 1

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