AIMM: The Maturity Model for AI corporate adoption



Following is the logic of a maturity model:

1. AI Readiness:

Level one: no maturity, efforts in place to agree on a common language and common terminology.

Level two: specific key processes are aligned across the organization. These are chosen for their relevance but are separate from each other. Failure in implementing one of the processes will stay isolated and not impact the alignment of the other processes.

2. AI Maturity:

Level three: introduction of a methodology. Processes are aligned and interrelated. Reaching this level successfully will bring very significant value because of the alignment across the corporate system, but also very significant risks, since a failure in one area will reverberate across the system since parts are highly integrated and interrelated.

Level four: Tracking and monitoring the progress of the system. Introduction of bench-marking (internal and external), dashboards, information radiators. Change is managed by exception, gaps and challenges are quickly identified, and action plans may be devised to fill them.

Level five: Change is embedded into the normal ways of working. Seeking failure out and acting on improvement is part of the culture.

AI Maturity Model

AI Readiness


Level One: Initial Awareness

– No Maturity: The organization has just begun exploring AI, with efforts focused on building a foundational understanding among team members.

– Common Language and Terminology: Initiatives are in place to ensure everyone within the organization understands basic AI concepts, terminology, and the potential impact of AI on the business.

Level Two: Process Alignment

– Key Process Identification: The organization identifies key processes where AI could be beneficial and starts aligning AI initiatives with business objectives, albeit in isolated projects.

– Isolated Implementation: Each AI project is managed independently. Failures in one project do not impact others, maintaining isolated risks.

AI Maturity


Level Three: Methodological Integration

– Introduction of a Methodology: The company adopts a structured methodology for AI implementation, integrating AI with existing business processes.

– Interrelated Processes: AI projects are no longer isolated; they are interrelated and aligned across the organization, leading to more significant value but also increased risk.

Level Four: Systemic Management

– Tracking and Monitoring: The organization implements systems for tracking and monitoring AI projects, using benchmarks, dashboards, and information radiators to measure progress and performance.

– Change Management: The company adopts a proactive approach to managing change, identifying gaps and challenges in AI adoption, and addressing them systemically.

Level Five: Cultural Integration

– Embedded Change: AI adoption is fully integrated into the company’s ways of working. Seeking out areas for improvement and implementing changes becomes a natural part of the organizational culture.

– Continuous Improvement: The organization fosters an environment where employees are encouraged to identify failures and inefficiencies as opportunities for learning and growth in AI capabilities.

This model should serve as a guideline for organizations aiming to adopt AI effectively. It emphasizes the importance of building a common understanding, aligning AI with business processes, integrating methodologies, managing change systematically, and embedding continuous improvement into the company culture.

From level one to two


To fulfil Level One and prepare to move on to Level Two in the AI adoption maturity model, an organization can undertake the following actions:


Level One: Initial Awareness


1. Assessment of Current AI Knowledge:

– Conduct an initial assessment to determine the current level of AI understanding within the organization.

– Identify key stakeholders and their understanding of AI.

2. Developing a Common Language and Understanding:

– Host workshops, seminars, or training sessions to introduce AI concepts, terminology, and potential applications relevant to the business.

– Create and distribute educational materials, such as glossaries, FAQs, and introductory guides to AI.

3. Establishing AI Awareness Goals:

– Set clear, measurable objectives for increasing AI awareness and understanding across the organization.

– Define what success looks like for reaching an adequate level of AI literacy.

4. Communication Plan:

– Develop a communication plan to keep all employees informed about AI initiatives, progress, and how they can contribute.

– Use various channels such as newsletters, intranet posts, and regular meetings to disseminate AI knowledge.

5. Feedback Mechanism:

– Implement a system for collecting feedback on the AI awareness programs.

– Use this feedback to improve the programs and ensure they meet the needs of all employees.

6. Identifying AI Champions:

– Identify and empower a group of AI champions or ambassadors from different departments who can help spread AI awareness and encourage their peers.


Preparation for Level Two: Process Alignment


1. AI Potential Identification:

– Begin identifying key business processes and areas where AI could provide significant value.

– Prioritize processes based on factors like impact, feasibility, and alignment with business goals.

2. Baseline Performance Metrics:

– Establish baseline metrics for the identified processes to measure the impact of future AI implementations.

– Ensure that data collection systems are in place for these metrics.

3. Stakeholder Engagement:

– Engage with stakeholders from the identified key processes to understand their needs, challenges, and expectations from AI.

– Start building a cross-functional team that will lead the AI integration efforts.

4. Resource Assessment:

– Assess the current resources available for AI initiatives, including budget, talent, and technology.

– Identify gaps and plan for resource acquisition or development.

5. Risk Assessment and Management Plan:

– Begin assessing potential risks associated with AI adoption for the identified processes.

– Develop a preliminary risk management plan to address these risks.

6. Learning and Development Plan:

– Develop a learning and development plan to upskill employees, focusing on the skills needed for the upcoming AI projects.

– Include both technical and non-technical skills relevant to AI adoption.

By completing these actions, the organization will have built a solid foundation of AI awareness and understanding, setting the stage for successful AI integration into specific key processes in Level Two.

From level two to three


To fulfill Level Two and prepare to move on to Level Three in the AI adoption maturity model, an organization can undertake the following actions:


Level Two: Process Alignment


1. Selection of Key Processes for AI Integration:

– Finalize the selection of specific key business processes where AI can be most beneficial based on the preliminary work done in Level One.

– Ensure these processes are varied but critical to the business, to showcase the potential of AI across different areas.

2. AI Project Planning:

– Develop detailed project plans for each selected AI initiative, outlining objectives, required resources, timelines, and expected outcomes.

– Establish clear roles and responsibilities within each AI project team.

3. Data Preparation and Infrastructure Setup:

– Ensure the availability and accessibility of quality data required for AI projects.

– Set up or enhance the necessary IT and data infrastructure to support AI initiatives, ensuring security, privacy, and scalability.

4. AI Pilot Projects:

– Launch pilot projects for the selected processes to test and refine AI solutions in controlled environments.

– Monitor performance, collect feedback, and make necessary adjustments.

5. Stakeholder Communication and Engagement:

– Keep stakeholders informed about the progress and outcomes of AI pilot projects.

– Engage them in the process to gather insights, secure buy-in, and foster collaboration.

6. Evaluation and Learning:

– Systematically evaluate the outcomes of AI pilot projects against predefined metrics and goals.

– Capture lessons learned, best practices, and areas for improvement.

7. Scaling and Integration:

– Based on pilot results, refine AI models and strategies.

– Begin scaling successful AI solutions across the targeted processes, integrating them into everyday business operations.


Preparation for Level Three: Methodological Integration


1. Methodology Development:

– Start developing a comprehensive methodology for AI integration, considering organizational culture, structure, and business objectives.

– Include frameworks for governance, project management, and continuous improvement.

2. Cross-Process Alignment:

– Identify opportunities for aligning and interrelating AI initiatives across different business processes.

– Plan for the integration of AI solutions to ensure they complement and enhance each other.

3. Capability and Skill Development:

– Assess the broader AI capabilities and skills needed across the organization for the upcoming integration phase.

– Expand the learning and development plan to address these needs, focusing on interdisciplinary skills and AI literacy beyond the IT department.

4. Change Management Strategy:

– Develop a change management strategy to support the upcoming organizational changes and ensure smooth adoption of integrated AI solutions.

– Include communication plans, training programs, and support structures.

5. Risk Management Enhancement:

– Update and enhance the risk management plan to address new risks associated with broader AI integration.

– Include contingency plans for potential failures or disruptions.

6. Infrastructure and Data Strategy Review:

– Review and update the data and IT infrastructure strategy to support expanded AI use.

– Ensure scalability, security, and efficiency as AI becomes more integrated into business processes.

By completing these actions, the organization will have successfully integrated AI into specific key processes and prepared itself for the broader methodological integration of AI solutions across different areas of the business in Level Three.

From level three to four


To fulfill Level Three and prepare to move on to Level Four in the AI adoption maturity model, an organization can undertake the following actions:


Level Three: Methodological Integration


1. Comprehensive AI Strategy:

– Develop or refine a comprehensive AI strategy that aligns with the organization’s overall business goals and objectives.

– Ensure the strategy includes a clear methodology for integrating AI across various business processes and functions.

2. Interdepartmental Collaboration:

– Facilitate collaboration between different departments and teams to ensure AI initiatives are aligned and interrelated across the organization.

– Establish cross-functional teams or working groups to oversee the integration of AI into various business processes.

3. Process Reengineering:

– Review and reengineer business processes as necessary to fully leverage AI capabilities, ensuring processes are streamlined, interconnected, and optimized for AI integration.

– Document changes and ensure all relevant employees are trained on new processes.

4. AI Governance Framework:

– Establish or enhance the AI governance framework to include guidelines, policies, and standards for AI use across the organization.

– Ensure the governance framework addresses ethical considerations, compliance, data privacy, and security.

5. Advanced Data Management:

– Implement advanced data management practices to support AI initiatives, including data quality, accessibility, and interoperability.

– Ensure a robust data infrastructure that supports the seamless flow and analysis of information across systems.

6. Continuous Learning and Improvement:

– Foster a culture of continuous learning and improvement with regular training sessions, workshops, and knowledge-sharing platforms.

– Encourage experimentation and innovation within AI projects.

7. Performance Measurement:

– Develop and implement a comprehensive set of metrics and KPIs to measure the performance and impact of AI initiatives across the organization.

– Regularly review and adjust AI strategies based on performance metrics.


Preparation for Level Four: Systemic Management


1. Advanced Monitoring and Tracking Systems:

– Begin developing or enhancing systems for tracking and monitoring the performance of AI-driven processes and initiatives.

– Consider implementing advanced analytics, dashboards, and reporting tools to provide real-time insights into AI performance.

2. Benchmarking:

– Start benchmarking the organization’s AI capabilities and performance against industry standards or competitors.

– Identify areas for improvement and set targets for reaching best-in-class status in AI adoption.

3. Change Management Enhancement:

– Enhance change management strategies to support the ongoing transformation and integration of AI.

– Ensure mechanisms are in place to manage resistance, foster buy-in, and support employees through the transition.

4. Advanced Risk Management:

– Develop advanced risk management strategies specifically for AI, considering the broader impacts and dependencies introduced by interconnected AI systems.

– Plan for scenario analysis and crisis management to mitigate potential risks associated with AI integration.

5. Leadership and Culture:

– Strengthen leadership support for AI initiatives, ensuring leaders are informed, engaged, and advocate for AI across the organization.

– Continue to cultivate a culture that supports innovation, data-driven decision-making, and continuous improvement.

6. Infrastructure and Technology Review:

– Conduct a comprehensive review of the existing IT infrastructure and technology stack to ensure they support advanced AI applications and data analytics.

– Plan upgrades or enhancements as necessary to support the increasing scale and sophistication of AI solutions.

By completing these actions, the organization will have successfully integrated AI methodologies across its business processes and be well-prepared to move into systemic management and optimization of AI capabilities in Level Four.

From level four to five


To fulfill Level Four and prepare to move on to Level Five in the AI adoption maturity model, an organization can undertake the following actions:


Level Four: Systemic Management


1. Integrated Performance Monitoring:

– Implement comprehensive monitoring systems that track the performance of AI initiatives against business objectives across the organization.

– Utilize dashboards, information radiators, and analytics tools to provide real-time insights and visibility into AI performance.

2. Benchmarking and Best Practices:

– Conduct regular benchmarking against industry standards and competitors to identify areas of improvement and opportunities for innovation in AI usage.

– Adopt best practices from within and outside the industry to continually enhance AI capabilities.

3. Advanced Change Management:

– Implement advanced change management practices to ensure that AI-driven changes are effectively communicated and adopted throughout the organization.

– Develop strategies to handle resistance and empower employees to adapt to new ways of working with AI.

4. Continuous Process Optimization:

– Continuously review and optimize AI-driven processes for efficiency and effectiveness.

– Implement feedback loops where employees can contribute ideas and feedback on AI tools and processes.

5. Risk Management and Compliance:

– Enhance risk management frameworks to address the unique challenges and risks presented by AI, including ethical considerations, bias, and data security.

– Ensure that AI practices comply with all relevant regulations and ethical standards.

6. Leadership and Culture for AI:

– Strengthen the role of leadership in promoting and supporting AI initiatives.

– Cultivate a culture that values data-driven decision-making, innovation, and continuous learning.

7. Skills and Capabilities Development:

– Continue investing in training and development programs to build AI literacy and skills across the organization.

– Encourage cross-disciplinary learning and collaboration to enhance the AI ecosystem within the company.


Preparation for Level Five: Embedded Innovation


1. Innovation Management:

– Develop mechanisms to encourage and manage innovation within the organization, such as innovation labs, hackathons, and idea incubation platforms.

– Ensure that there are clear processes for scaling successful innovations across the business.

2. Advanced AI Integration:

– Plan for deeper integration of AI into business processes and decision-making, moving beyond operational tasks to strategic areas.

– Explore advanced AI technologies such as machine learning, natural language processing, and robotics, and assess their potential applications within the organization.

3. Organizational Agility:

– Enhance organizational structures and processes to be more agile and adaptable to changes brought about by AI and other emerging technologies.

– Ensure that the organization can quickly respond to new opportunities and challenges in the AI landscape.

4. Proactive Failure Management:

– Develop a culture that not only tolerates but encourages experimentation and learns from failures.

– Implement systems to capture and analyze failures and near-misses to improve future AI initiatives.

5. Customer-Centric AI Solutions:

– Focus on developing AI solutions that enhance customer experiences and meet customer needs.

– Utilize customer feedback and data analytics to continuously improve AI-driven products and services.

6. Sustainable AI Practices:

– Ensure that AI practices are sustainable and consider long-term impacts on society and the environment.

– Adopt responsible AI principles that promote transparency, fairness, and accountability.

By completing these actions, the organization will have systematically managed and optimized its AI capabilities and be well-prepared to move into embedding AI as a core aspect of its culture and operations in Level Five.

Reaching level five


To fulfill Level Five and consolidate the results obtained in the AI adoption maturity model, an organization can undertake the following actions:


Level Five: Embedded Innovation and Continuous Improvement


1. Cultural Transformation:

– Ensure that the organization fully embraces a culture of innovation, experimentation, and continuous improvement, where AI is an integral part of every employee’s mindset and daily activities.

– Recognize and reward contributions to AI initiatives and improvements to foster an environment where seeking out and acting on improvement is the norm.

2. Continuous AI Integration:

– Continuously integrate AI into new and existing business processes, ensuring it becomes a natural part of the workflow and decision-making processes.

– Encourage departments to identify opportunities for AI application independently and to initiate projects with the support of the AI team.

3. Organization-Wide AI Literacy:

– Ensure that all levels of the organization have a sufficient understanding of AI capabilities and limitations, enabling them to identify opportunities for AI use and contribute to AI initiatives.

– Provide ongoing education and resources to keep up with evolving AI technologies and methodologies.

4. Innovation Ecosystems:

– Strengthen relationships with external partners, academia, industry groups, and technology providers to stay at the forefront of AI innovation.

– Participate in or create ecosystems that foster collaboration, co-innovation, and sharing of best practices in AI.

5. Customer and Stakeholder Engagement:

– Embed AI solutions that enhance customer and stakeholder engagement, using AI to better understand and predict customer needs and improve the customer experience.

– Involve customers and other stakeholders in the co-creation of AI solutions to ensure they add real value.

6. Sustainable and Ethical AI:

– Ensure that AI solutions are developed and used in an ethical, transparent, and accountable manner, with a focus on long-term sustainability.

– Regularly review and update AI governance frameworks to address emerging ethical, legal, and societal challenges.

7. Advanced Analytics and Decision-Making:

– Utilize advanced analytics, machine learning, and AI to derive deeper insights from data, supporting more informed and proactive decision-making across the organization.

– Embed predictive analytics and AI-driven insights into strategic planning and operational processes.

8. Agile and Resilient Operations:

– Develop agile and resilient operations that can quickly adapt to changes and challenges, leveraging AI for scenario planning, risk management, and crisis response.

– Ensure that the organization can rapidly scale AI solutions up or down based on changing needs and circumstances.

9. Measuring Impact and ROI:

– Implement comprehensive metrics and KPIs to measure the impact and ROI of AI initiatives, ensuring alignment with business objectives.

– Use these measurements to refine AI strategies, demonstrate value, and secure ongoing investment in AI.

10. Future-Proofing the Organization:

– Regularly reassess and update the AI strategy to align with evolving business goals, technological advancements, and market conditions.

– Prepare the workforce for future changes by fostering a mindset of lifelong learning and adaptability.

By completing these actions, the organization will have successfully embedded AI into its culture and operations, driving continuous innovation and improvement. The consolidation of results involves not only reflecting on the successes and learnings from AI initiatives but also ensuring these insights are integrated into future strategies and operations, thereby maintaining a competitive edge and delivering ongoing value to customers and stakeholders.

Sample questions to use for level assessment


Questions to evaluate level 1

For assessing an organization’s attainment of Level 1 in the AI maturity model, which focuses on initial awareness and the establishment of a common language, we can use the following questions. Employees should be able to respond with options ranging from “Strongly Agree” to “Strongly Disagree”:

  1. Awareness: I am aware of what Artificial Intelligence (AI) means and its potential impact on our industry and organization.
  2. Understanding: I understand basic AI concepts and terminology (such as machine learning, algorithms, natural language processing).
  3. Communication: I have received clear communication from the organization regarding the importance and role of AI in our operations.
  4. Resources: I know where to find resources and learning materials if I want to increase my understanding of AI.
  5. Training and Development: The organization has provided training sessions or workshops on AI that I have found accessible and informative.
  6. Relevance: I can see how AI could be relevant and beneficial to my role or department within the organization.
  7. Initiatives Awareness: I am aware of any current or upcoming AI initiatives within the organization.
  8. Feedback Opportunities: I feel encouraged to provide my input or feedback on AI-related topics or projects within the organization.
  9. Strategy and Goals: I am familiar with our organization’s goals or strategy regarding the adoption and integration of AI.
  10. Cultural Readiness: I believe there is a supportive culture within our organization that encourages learning and curiosity about AI.

These questions, answerable on a scale from “Strongly Agree” to “Strongly Disagree,” will help gauge employees’ awareness and understanding of AI within the organization, which are key indicators of whether the organization has reached Level 1 maturity in AI adoption.


Questions to evaluate level 2

For assessing whether an organization has reached Level 2 in the AI maturity model, which focuses on process alignment and the beginning of AI project implementation, we consider the following questions. Employees should respond with options ranging from “Strongly Agree” to “Strongly Disagree”:

  1. Process Identification: I am aware of specific key processes within our organization that have been identified for AI integration.
  2. AI Project Clarity: I understand the objectives and expected outcomes of AI projects within our organization.
  3. Role Clarity: I know how my role or department can contribute to or is impacted by AI initiatives.
  4. Isolation of Projects: I believe that failure in one AI project would not adversely affect other processes or projects within the organization.
  5. Communication of AI Projects: I receive regular updates and clear communication about the progress and results of AI projects.
  6. Training for AI Projects: I have access to training or resources that help me understand and contribute to AI projects relevant to my work.
  7. Cross-departmental Collaboration: There is effective collaboration between different departments and teams working on AI projects.
  8. Supportive Environment for AI Projects: The organization provides adequate support (such as resources, time, and leadership) for AI projects.
  9. Feedback Mechanism: There is a mechanism in place for providing feedback on AI projects and this feedback is used to improve future projects.
  10. Alignment with Business Goals: AI initiatives within our organization are aligned with and contribute to our overall business goals.

These questions, designed to be answered on a scale from “Strongly Agree” to “Strongly Disagree,” will help gauge the level of process alignment, clarity, and support for AI initiatives within the organization, indicative of reaching Level 2 maturity in AI adoption.


Questions to evaluate level 3

For evaluating whether an organization has reached Level 3 in the AI maturity model, which focuses on methodological integration and the alignment of AI initiatives across the organization, we consider the following questions. Employees should be able to respond with options from “Strongly Agree” to “Strongly Disagree”:

  1. Integrated Methodology: I am aware that our organization employs a standardized methodology for integrating AI across different business processes.
  2. Interrelated Initiatives: I believe that AI initiatives in our organization are interrelated and support one another across different departments.
  3. Value Addition: I feel that the integration of AI has added significant value to our organization’s operations and strategic goals.
  4. Risk Awareness: I am aware of the risks associated with AI integration in our business and understand how these risks are managed.
  5. Employee Involvement: I feel involved in the AI initiatives and believe my input can influence the outcome of these projects.
  6. Cross-functional Teams: I see effective communication and collaboration between cross-functional teams working on AI initiatives.
  7. Change Management: I believe our organization effectively manages changes associated with AI integration, including addressing employee concerns and training needs.
  8. Comprehensive Training: I have access to comprehensive training and resources to understand and work effectively with our AI systems.
  9. Decision-making Support: I find that AI tools and systems in our organization support better decision-making and efficiency in my work.
  10. Organizational Culture: I believe that our organizational culture supports the alignment and integration of AI across various processes and departments.

These questions, designed to be answered on a scale from “Strongly Agree” to “Strongly Disagree,” will help assess the extent of methodological integration, employee involvement, and the perceived value of AI within the organization, which are indicative of reaching Level 3 maturity in AI adoption.


Questions to evaluate level 4

For assessing whether an organization has reached Level 4 in the AI maturity model, which emphasizes systematic management, tracking, benchmarking, and change management, we consider these questions for employees. Responses should range from “Strongly Agree” to “Strongly Disagree”:

  1. Performance Tracking: I am aware that our organization systematically tracks and monitors the performance of AI initiatives using specific metrics and KPIs.
  2. Benchmarking Practices: I know that our organization regularly benchmarks our AI capabilities against industry standards or competitors.
  3. Data-Driven Decisions: In my department, decisions are increasingly made based on data and insights generated by AI systems.
  4. Change Management: I believe that changes resulting from AI integration are managed effectively, minimizing disruptions and ensuring a smooth transition.
  5. Proactive Issue Identification: Our organization proactively identifies and addresses issues in AI projects before they escalate.
  6. AI Solutions Scalability: I am confident that successful AI solutions are scaled and implemented across the organization efficiently.
  7. Employee Empowerment: I feel empowered to suggest improvements or changes to AI systems and processes within the organization.
  8. Learning and Development: I have ongoing opportunities for learning and development related to AI and its applications in our business.
  9. Innovation Support: I see a supportive environment for innovation where employees are encouraged to develop and propose new AI-driven solutions.
  10. Strategic Alignment: I believe that AI initiatives are closely aligned with the strategic goals and objectives of our organization.

These questions are structured to gauge the level of systematic AI management within the organization, including performance monitoring, change management, employee involvement, and strategic alignment, which are indicative of reaching Level 4 maturity in AI adoption.


Questions to evaluate level 5

For evaluating whether an organization has reached Level 5 in the AI maturity model, which focuses on the widespread integration of AI into the culture and continuous improvement cycles, we consider the following questions. Employees should be able to respond with options ranging from “Strongly Agree” to “Strongly Disagree”:

  1. AI as Standard Practice: I believe that using AI solutions and tools is a standard practice across our organization in decision-making and operational processes.
  2. Continuous Improvement: I see a continuous effort within our organization to improve and update AI systems and technologies based on new information and feedback.
  3. Innovation Culture: I feel that our organization actively fosters a culture of innovation where employees are encouraged to come up with and implement new AI-driven solutions.
  4. Failure as a Learning Tool: I agree that in our organization, failures in AI projects are viewed as opportunities for learning and improvement, not as setbacks.
  5. Employee Empowerment: I believe all employees are empowered to suggest improvements or changes to AI systems and processes.
  6. Customer-Centric AI: I am confident that our AI initiatives are aligned with and actively contribute to enhancing customer satisfaction and experience.
  7. Ethical AI Use: I am assured that our organization adheres to ethical standards in the development and application of AI, prioritizing fairness, transparency, and accountability.
  8. Proactive Skill Development: I have access to ongoing education and training opportunities related to AI and am encouraged to develop relevant skills for future needs.
  9. Strategic AI Alignment: I believe our organization’s AI strategies are well-aligned with its long-term goals and industry trends.
  10. Global AI Leadership: I feel that our organization is a leader in AI innovation and application in our industry.

These questions, designed to be answered on a scale from “Strongly Agree” to “Strongly Disagree,” will help gauge the level of AI integration into the organizational culture, the emphasis on continuous improvement and innovation, and the overall employee engagement and perception of AI, indicative of reaching Level 5 maturity in AI adoption.