AI Maturity: A proposal for an A.I. enablement framework, across, and throughout the enterprise.
Systems thinking
My view is to take a systematic methodological approach to the opportunity: define an adoption framework to facilitate its adoption across-the-board within the enterprise, and measure adoption & success.
My proposed solution is two-fold:
AI Maturity Framework: Discover, Define, Design and Deliver an AI adoption maturity framework for standard application throughout the business structure (AI enablement).
Ccoverage: A definition of the aforementioned business structure to which we systematically rollout the adoption framework (AI coverage).
Measurement
AI Maturity can then be measured across the two axis: enterprise coverage x maturity = ai maturity score.
It could also be monitored as a radar plot diagrams for both Organizational Scope and Departmental Scope based along the axes defined by the Framework x Coverage. This will give us a maturity score.
2 Dimensions of application
Enablement: AI plugins for tooling to enable AI for users: eg. IDEs, Confluence, Slack.
Enhancement: Enhance AI across all enterprise departments & features.
Maturity Framework
Creation of a process framework for AI adoption: A process framework typically includes a set of guidelines, templates, and tools for managing processes. It may also include performance metrics, governance processes, and reporting mechanisms to monitor and improve process performance over time.
Plus a team/board/council/working group dedicated to the creation of the framework, and responsible also for its socialisation and targeting a higher maturity score.
AI Adoption Framework (AAIF) components (a process framework)
Possible / probably components of the process framework itself which would outline a high-level process to follow for each team and offering high-level guidance. For example:
Identify areas for improvement: The first step is to identify areas in the user experience that could be improved with the use of AI. This could include things like personalization, chatbots for customer support, or intelligent search algorithms.
Define the problem: Once you have identified the areas for improvement, you need to define the problem you are trying to solve. For example, if you want to improve personalization, you might define the problem as "users are not receiving relevant content recommendations based on their interests."
Choose the right AI solution: After defining the problem, you need to choose the right AI solution to solve it. This could involve using machine learning algorithms for personalization or natural language processing for chatbots.
Collect data: To train your AI solution, you need to collect data. This could include user behavior data, customer support transcripts, or other relevant data sources.
Train and test your AI solution: Once you have collected data, you need to train and test your AI solution. This involves feeding the data into the algorithm and evaluating the results to ensure that the AI is making accurate predictions or responses.
Implement your AI solution: Once your AI solution is trained and tested, you can implement it into your UX. This could involve integrating the AI into your website or app, or using it to automate certain processes.
Monitor and refine your AI solution: Finally, you need to monitor your AI solution to ensure that it is working as expected and refine it as necessary. This could involve tweaking the algorithm or collecting additional data to improve performance.
Examples of other process frameworks (not AI frameworks, but business process frameworks) include the Capability Maturity Model Integration (CMMI) framework for software development, the Project Management Body of Knowledge (PMBOK) framework for project management, and the Information Technology Infrastructure Library (ITIL) framework for IT service management.
In UX design there is the UX Maturity Model which provides a framework for measuring the improved maturity of UX practices within an organisation
Key Concepts of a process framework
Here are some items that a process framework promotes and would provide the axes of the maturity model:
Standardization: By following a consistent framework, we can ensure that AI practices are executed consistently across the organization, regardless of the individual or team responsible for executing them. This can help reduce errors, increase efficiency, and improve overall quality.
Best practices: Identify and publish AI related best practices for teams to follow.
Scalability: Process frameworks help organizations scale their operations more efficiently so by defining and standardizing Ai adoption processes, we can more easily add new employees, teams, or departments to it without sacrificing quality or efficiency.
Continuous improvement: Our framework should include mechanisms for continuous improvement, such as process metrics or feedback loops. By regularly measuring and analyzing process performance, we can identify areas for improvement and make iterative changes to optimize adoption processes over time.
Compliance: By having a standard and updated central process framework we can aid compliance with changing industry regulations or standards and roll these out to teams so they can ensure that they are meeting their legal or contractual obligations and avoid costly penalties or fines.