Introducing the AI Maturity Model
Introducing the AI Maturity Model
Mark your progress and achieve enterprise AI success.
Today, we are releasing the CognitivePath AI Maturity Model. This framework provides leaders with a comprehensive framework for understanding their own organization’s current state of AI adoption and charting a course toward a more AI-enabled future.
Every day, we help enterprises and organizations grapple with AI adoption. After speaking with hundreds of executives over the past year, we distilled our learnings into the AI Maturity Model. We have learned that an enterprise's maturation is wide and all-encompassing, from decoding the hype cycle and focusing on core business strategies to strengthening data governance and upskilling teams.
Building the framework was necessary to provide guidance and foster a more structured adoption of AI technologies. The AI Maturity Model aims to inspire leaders. It provides guideposts and encourages action without being overly prescriptive. The model can be used to:
● Benchmark and assess progress
● Create alignment within the implementing team and across the enterprise
● Establish a strategic “North Star” for AI that transforms the business operations
● Develop, implement, and measure a AI roadmap
● Create the future of business and thrive in an increasingly AI-powered world
Showing a natural progression, the AI Maturity Model progresses through five stages of maturity—ad hoc, experimental, systematic, strategic, and pioneering—and several interrelated paths to measure organizational progress. The remainder of this article summarizes the stages and paths.
You can access the full report and charts on the CognitivePath website.
The Five Stages of AI Maturity
The Fives Stages chart can be downloaded here.
The five stages of the AI Maturity journey illustrate an organization’s progression through the technology adoption and skill development necessary to fully benefit from artificial intelligence when applied to internal processes and external-facing programs. The stages—Ad Hoc, Experimental, Systematic, Strategic, and Pioneering—provide benchmarks for organizations gauging their level of AI maturity.
For most organizations, the maturity model is aspirational. The path to a mature AI approach underscores the evolution from experimental and isolated efforts to strategic and integrated AI implementations that drive significant transformation and innovation. As organizations progress through these five stages, they unlock the true potential of AI, driving transformative growth and innovation. Here is a brief description of each stage.
Ad Hoc Stage: At the Ad Hoc stage, organizations lack a clear direction for AI, with sporadic efforts that are not integrated into the broader department or enterprise strategy. AI initiatives are isolated and exploratory, without defined goals or understanding of potential outcomes.
Experimental Stage: In the Experimental stage, organizations begin identifying specific AI opportunities and conducting pilot projects. These pre-scale efforts focus on near-term wins and early proofs of concept, validating the viability and impact of AI applications before wider implementation.
Systematic Stage: As organizations reach the Systematic stage, AI becomes more integrated into key programs and processes. A structured, methodical approach prioritizes AI use cases with clear goals and metrics. Businesses will see replicable use cases and investments in enterprise-ready AI tools, workflows, and workforce upskilling.
Strategic Stage: In the Strategic stage, AI is integral to the core department or business strategy, influencing decision-making and offering new avenues for differentiation. Here, AI becomes a fundamental part of core processes, driving competitive advantage through deeper audience insights, predictive analytics, well-orchestrated customer journeys, and creative innovation.
Pioneering Stage: In the final Pioneering stage, organizations are at the forefront of innovative approaches in their application of AI technologies. They create entirely new models and value propositions, reshaping the competitive landscape and setting new industry standards.
Progress Along Seven Interrelated Paths
The Seven Paths chart can be downloaded here.
The journey toward AI Maturity requires leaders to take a big-picture, multi-disciplinary approach that transforms the entire organization. Teams must build competencies and take action across seven key areas to advance through the five stages of the maturity cycle:
Approach
Technology
Data
Governance
Expertise
Team
Alignment
Below are summaries of the seven paths. We encourage you to read the full report to access the whole AI maturity model.
Approach: While AI must be “strategic,” a stand-alone AI strategy separate from the company or organization’s core business strategy is often counterproductive. Instead, AI should be woven into the fabric of the core strategy and work to achieve the same goals. Key milestones on the path toward a mature approach include increased intention, strategic organizational alignment and integrations, consistency, and a results-based approach to measurement.
Technology: An enterprise organization’s approach to identifying, implementing, and integrating specific artificial intelligence solutions includes set of approaches to processes. Technology leaders focus on AI integration and interoperability with existing technologies and form strategic partnerships with technology providers, benefiting from advanced insights and capabilities. Custom solutions are a later-stage investment to meet specific strategic needs and objectives.
Data: Clean, properly tagged data, data-cleaning processes, and data frameworks are gold. They fuel vital analytics insights, better segmented and personalized AI outputs, and unique brand-specific AI models and implementations. Enterprises that engage in private implementations nurture and strengthen their data repositories to provide higher value, protect their data as assets, and enforce and maintain their data handling processes in a manner that matches the value bestowed upon it.
Governance: The governance path addresses the critical role of structured oversight, ethical considerations, and stakeholder engagement to ensure AI technologies are used responsibly and effectively. To achieve successful governance, enterprises and organizations facilitate increased engagement in and outside of the enterprise, engage in a continuous review of policy and adapt accordingly, and integrate AI governance into broader corporate policy frameworks.
Expertise: Enterprise leaders invest in upskilling and reskilling their workforce for both technical and strategic learning processes that support and enhance AI capabilities. There is an inherent need for human and cultural change to successfully integrate AI that includes continuous learning across the enterprise as well as proactive talent development and acquisition.
Team: More mature AI-enabled organizations embrace new forms of expertise, including newly imagined roles, innovative team structures, innovative ways of working, and new models. Leadership evolves culture to encourage AI adoption in processes and workflows and writes it into position descriptions. Agile organization design fosters embracing AI technologies and builds cross-functional collaboration across departments.
Alignment: AI is a team sport across the enterprise. The evolution from reactive engagement to strategic leadership in AI initiatives emphasizes the importance of cross-functional collaboration, communication, and strategic organizational alignment. To better adapt, companies and organizations lead with active executive sponsorship, build cross-functional teams, engage in change management, and foster strong communications about AI initiatives.
Lead the Way
We hope you find the AI Maturity Model useful in your enterprise initiatives to improve your processes and programs and better achieve your strategic business goals. Please feel free to contact us to find out how to integrate the AI Maturity Model into your efforts.