There’s mass adoption of Artificial intelligence across various industries, and organizations recognize the need to advance their AI capabilities to stay relevant and competitive. As stated by Forbes, “The ability to unlock insights from data is a competitive differentiator in today’s business landscape, and AI is a powerful tool for unlocking those insights.” In this article, we will explore the AI maturity model and how it can help an organization advance its AI capabilities.
AI Maturity Model
The Maturity model is simply a framework that helps organizations assess their AI capabilities and adopt a road map for their AI capabilities by focusing on the development of AI capabilities within the organization across four major areas: Technology, data, people, and algorithms. And they are four levels of maturity.
- Level 1: Ad Hoc – At this level, organizations have not developed a formal AI strategy and just use it on an ad hoc basis.
- Level 2: Developing – This is the level where organizations begin to build a formal AI strategy and implement AI projects on a project-by-project basis.
- Level 3: Mature – This is the level where organizations have a formal AI strategy and implement AI projects in a strategic and coordinated manner.
- Level 4: Leading – This is the level where are heading the adoption of AI and are now using AI to drive innovation and also as a competitive advantage.
The complete AI maturity model also includes a scorecard used to assess an organization’s AI capabilities in various areas ranging from data management, technology, talent, and governance.
The Image above is an example of an AI maturity scorecard, it has different industry averages from 1 to 4 and has three different groups. The left-hand side of the image has the Ambitions, which are the goals of the organization, Use cases which are the practical usefulness of the technology, the Enablers which are the Data, Technology, Organization, Ecosystem, Expertise, and Culture, and finally the Execution which indicates how the organization is carrying out its plans.
Why is the AI Maturity Model important?
The AI maturity model is important for organizations because it gives a roadmap for the advancement of their AI capabilities. By using the framework organizations can assess their current AI capabilities, identify areas that need to improve, and develop a plan to advance it.
This is important because:
- Organizations that are more advanced in their use of AI have a high likelihood of staying competitive in their respective industries
- AI can provide huge benefits to organizations by increasing efficiency, better decision-making, and improving customer experiences.
- The adoption of AI is growing across various industries and organizations that do not keep up with the pace of the adoption will be prone to falling behind sooner than expected.
How does the AI maturity model work?
The AI maturity model works by providing organizations with a framework they can use to evaluate their use of AI and develop a roadmap, to do this successfully the framework has several steps:
Assess your current AI capabilities
The first step in using the AI maturity model is to assess how your organization is currently using AI. This will give you a baseline to better understand the current state of your organization’s AI capabilities.
Identify possible areas for improvement
After the assessment of your organization is done, the next step will be to identify areas that your organization can improve on. Look at the scorecard, to create a score card you need to define the dimensions that will be used to evaluate the AI capabilities, define the maturity levels for each dimension, identify the criteria for evaluation, assign scores, calculate the total score, and then identify the areas that need improvement.
Develop a Roadmap
After identifying possible areas for improvement the next step will be to develop a roadmap. The roadmap should be able to identify specific actions that can be taken to improve your company’s AI capabilities. The roadmap should assess the current maturity level of the organization, set clear goals and objectives for each area that need improvement, create a plan with a timeline, budget, milestones, and action items specific to those areas then implement the plan and constantly evaluate your progress and improve.
Implement the Roadmap
Implementing the roadmap is the next step, as it involves taking practical specific actions like defining AI use cases, creating a data strategy, and conducting an AI readiness assessment, etc to improve your organization’s use of AI, for example, if your organization has low performance in the area of data management base your assessment then you might want to invest more in data management tools or hire a data scientist to improve your organization’s data management capabilities.
Measuring progress by tracking your organization’s Ai maturity level over time. Use the AI maturity assessment tool to assess your organization’s AI capabilities as often as possible by evaluating various dimensions, such as talent, infrastructure, data management, and culture, and then compare the results to the previous assessment. This process will help you track the progress and identify areas where more work is required and areas where progress has been made.
Now we have covered the AI maturity model we will cover another advanced aspect of the AI maturity model called the AI capability maturity model which assesses an organization’s AI capability maturity model. The AI capability maturity model is a framework that gives a more detailed assessment of an organization’s AI capabilities to enable them to know their current AI maturity level. The AI Capability Maturity Model focuses primarily on the development of AI capabilities within an organization. It evaluates an organization’s maturity across four main areas: data, algorithms, technology, and people.
AI capability maturity model
The AI capability maturity model has five levels of maturity:
- Level 0: No AI Capability – At this stage, the organization has no single AI capability.
- Level 1: AI Exploration – At this stage, the organization is exploring the potential of AI and is also conducting research and development activities.
- Level 3: AI Experimentation – At this level, the organization is experimenting with AI and is implementing AI projects of a large scale.
- Level 4: This is the level where the organization has started integrating AI into its business processes and is also using it to drive efficiency and innovation.
- Level 5: AI optimization – At this level, the organization has optimized its use of AI and is using AI to drive business transformation.
The Image above shows the 5 levels of the AI capability maturity model and operational maturity which are AI-specific functional areas.
After using the AI capability maturity model to know their current level, organizations will need a plan on how they can advance to the next level and that’s where the AI maturity framework comes in. The AI Maturity framework is a framework that gives organizations a comprehensive approach to taking their AI capabilities to another level, it takes a more holistic approach and assesses an organization’s general maturity in terms of AI adoption and implementation by focusing on the organization’s AI strategy, governance, technology, skills, and culture, among various factors. Generally, the key difference between the AI Capability Maturity Model and AI Maturity Framework is their focus. While the AI-CMM has its focuses on the development of AI capabilities within an organization, the AI Maturity Framework takes a comprehensive approach and evaluates an organization’s overall readiness and maturity for AI adoption and execution.
AI maturity framework
The AI maturity framework has four dimensions:
- Strategy: The development of AI strategies that align with the organization’s commercial objectives is the primary focus of this component.
- Technology: The technological infrastructure required to enable AI, including machine learning algorithms, deployment platforms, and data processing and storage is the focus here.
- People: Manpower, and the talents needed to develop and implement AI, including data scientists, machine learning engineers, data engineers, and AI strategists.
- Governance: The governance dimension focuses on the rules and guidelines necessary to ensure the moral and responsible usage of AI.
In conclusion, AI maturity gives a framework to help organizations looking to advance their AI capabilities by assessing their present level of maturity and simply identifying areas that require improvements. AI capability maturity model and AI maturity framework are simply a combination of useful tools for organizations hoping to improve their AI capabilities, each of them has its unique use-case, strength, and weakness. It’s very important to mention that implementing an AI maturity model is not a one-time process. As technology and business needs evolve, organizations need to stir back and reassess their AI maturity and continuously improve their capabilities.
Reassessing and improving your capabilities requires a habit of continuous learning and improvement, with a focus on staying up to date with the latest trends and developments in AI and also leveraging the frameworks and tools that are available. Overall, AI is transforming rapidly industries and making room for new opportunities for organizations of all sized.