It is not news by now that AI is a critical component in helping organizations transform how they operate, improve customer service, and offer new opportunities to their workforce. However, to harness the true potential of AI, organizations need to adopt a strategy, culture, and set of core capabilities around AI that match the organization’s maturity.

As we know by now, deploying an AI-based system is very different than acquiring off-the-shelf software or developing a custom-coded non-AI solution. AI-based systems must be continuously trained, monitored, and evaluated for performance if organizations realize their full benefits while guarding against bias, privacy violations, and safety concerns. Neglecting this maturity assessment can seriously impede an AI project, potentially causing a rejection of the technology by employees who perceive it as too difficult to use or untrustworthy.

To understand the next steps in your journey to build world-class AI capabilities, you need to evaluate where you are and what the next milestones look like. There are many AI framework maturity maps now available. This one is my formulation, but you will find that the theme is generally aligned across most of the frameworks out there.

My perspective of the AI Maturity Model consists of four stages of development:

Stage 1: Freshman

Organizations at the Freshman stage seek to understand the varieties and applications of AI and how others in their industry use it. They strive to make more data-driven decisions and currently rely on experienced leaders’ instincts to make decisions. Freshman organizations must invest in projects focusing on fast, iterative experimentation. Doing this successfully requires organizations to build a culture that embraces experimentation and empowers colleagues to make data-driven decisions. Organizations at this maturity level should adopt AI technologies built on established platforms, helping them grow into digital businesses.

Stage 2: Sophomore

Organizations at this level of maturity are implementing cultural changes to empower employees and make data-driven decisions. They are focused on building a data culture and using AI to build new processes and streamline operations. Having digitized assets and deployed AI to automate certain processes, these organizations are ready to explore
Owning custom AI solutions. Sophomore organizations are poised to embrace rapid experimentation and invest more in understanding how to implement and improve AI over time. Investments should continue in accountability protocols for AI governance, monitoring, orchestrating, improving AI over time, and infusing ethical viewpoints in AI-based systems. Considering these issues will help organizations gain experience using AI to digitally transform.

Stage 3: Junior

Junior organizations understand that AI will be instrumental in helping them compete and transform. These organizations know that others are using AI and understand the competitive disruption this may entail. Organizations at this maturity level focus on shifting culture to empower employees, increasing collaboration, and generating ideas for optimization, new offerings, and business models. These organizations are becoming increasingly comfortable taking risks and striving to transition from fixed projects to more iterative ones.

Junior organizations can adopt configurable AI hosted by technology companies. This abstracts away the operational complexity of maintaining the core AI while allowing organizations to infuse AI into digital experiences. At the same time, experimentation with more advanced AI technologies, such as custom AI, is encouraged by these organizations to learn about how to operate and coordinate more complex systems.

Stage 4: Senior

Senior AI Mature organizations have shifted their culture to embrace rapid, iterative experimentation and a data-driven approach. Senior organizations develop AI talent and understand how to simultaneously apply this resource to multiple AI initiatives.

Senior organizations ask what we can do with AI and what we should do with AI. The organizations also infuse ethical perspectives into their experience-creation process. Organizations at this level of maturity should continue to evaluate toolchains for configurable and custom AI while being vigilant about monitoring, retraining, and updating AI-based systems. Maintaining AI talent, prioritizing new strategic initiatives, and continued agile experimentation are focus areas at this level.

Journey of Innovation

Digital transformation is a journey. It provides a path for organizations to innovate and establish new and better business methods that benefit all stakeholders. Considering and assessing an organization’s AI maturity provides a clear path to guide AI technology adoption efforts. Organizations closer to the Foundational or Approaching stages should adopt configuration-based AI first, where partners develop the concepts of operationalization. When more specific AI capabilities are required, organizations should assess themselves across strategic, cultural, and capability boundaries to determine if they are ready to own and operate a custom AI solution.