AI Maturity Models

In today’s digital and dynamic world, it’s impossible to discuss business strategy without mentioning AI. AI can automate simple, repetitive tasks, analyze sales patterns, predict supply needs, identify opportunities for growth, and provide dynamic pricing suggestions based on current demand and supply. AI is no longer just a “nice to have”—it’s fast becoming a competitive necessity. 

However, it can take time to fully embed AI into a business’s practices and strategy. It’s not enough to simply invest in the tools; there must be a clear understanding of how AI is integrated, used, and will evolve for an organization to succeed. An AI maturity model is a framework that helps organizations assess their current AI capabilities and plan how they will advance their journey to maximize the use of AI to boost efficiency, foster innovation, and make data-driven decisions.1 Different models outline anywhere from 4 to 7 stages; this version presents a streamlined four-stage interpretation:

1. Awareness/Experimentation: At this stage, companies are aware that AI may help improve the business, but there is no formal adoption. Employees may be using AI to assist with some tasks on an ad-hoc basis, discovering its potential benefits and limitations.2
2. Active/Operational: Organizations have now started implementing AI into their day-to-day tasks and across teams. For the most part, AI is helping to simplify and automate processes and generate reports through descriptive analytics that support decision-making.3
3. Expansion/Mature: At this stage, organizations have developed an AI strategy and are embedding it across teams. Teams are using AI for more complex tasks, and the organization may have begun developing custom AI tools in-house. Predictive analytics may be a component of this stage, with companies using AI to support future decisions.3
4. Leading/Transformational: AI is a prominent part of a business’s strategy for continuous improvement, woven into how the organization runs. Employees are comfortable using AI and it is part of the organizational culture. AI is being used to drive innovation and provide the organization with a competitive advantage over competitors.2

While the underlying logic of AI maturity models is the same, terminology varies across models. For example, Microsoft uses “Foundational – Approaching – Aspirational – Mature.”4 The key takeaway is that maturity is less about labels, and more about organizations assessing their capabilities and readiness. Maturity models are important as they provide direction for where and how companies need to collect data, what technology they need to invest in, required change management, and how AI feeds into their broader strategy. These models help companies identify where they are, how they can improve, and create a strategic roadmap to evolve to the final stage.5