Your organization has AI pilots underway. Can you measure which ones should scale?
According to MIT CISR research, organizations in early stages (stages 1 and 2) of AI maturity tend to have financial performance below industry average, while those in later stages (stages 3 and 4) perform well above. The greatest financial impact comes from progressing from pilot programs (stage 2) to scaled AI ways of working (stage 3). AI maturity measurement provides the roadmap to connect AI initiatives to business value through systematic advancement.
The MIT CISR report describes organizations as advancing through four predictable stages in AI adoption.
Note that fewer than half of organizations (46%) are “below the line” - that is, in the first two stages - in the current survey. In a previous survey (2022), 62% of organizations were in the first two stages, a decrease of roughly 25%.
The Gartner AI Maturity Model evaluates seven areas of AI readiness:
According to Gartner, key steps toward moving forward in AI maturity include:
According toGartner, 34% of low-maturity and 29% of high-maturity organizations cite data quality as top challenges, which shows that even advanced organizations face fundamental issues.
Start by using an AI readiness index to establish baseline capabilities. TheCNA AI Maturity Model provides 52 topic areas and 450 milestones to help organizations assess current state and set up a roadmap for growth.
Follow these steps to move forward in your AI maturity.
Compare your activities against maturity model indicators. Identify which stage you occupy across different dimensions. Some areas might be stage 3, while others remain in stage 1. Uneven progress is normal as organizations optimize different capability areas at different speeds.
Not every organization needs stage 4 across all dimensions. Target maturity is a function of your organization’s mission, resources, and business strategy. Identify which capabilities matter most for your AI adoption goals and competitive positioning. Focus investment where advancement drives the most business value.
Break advancement into phases to gradually move up the maturity curve. Use frameworks such as CMMI for AI, providing structured process improvement approaches. Prioritize gaps between current and target capabilities based on business impact and resource requirements.
AI maturity measurement is ongoing. Suggested steps include tracking progress quarterly using consistent metrics. Establish a baseline, documenting the starting point across several dimensions, such as: AI adoption levels, AI tools usage, data sources quality, workforce AI skills, and business outcomes from existing initiatives.
Monitor key indicators, such as the percentage of processes with AI integration, the number of scaled use cases beyond pilots, adoption rates, the rate of data governance compliance across your organization, and training completion. Benchmark against industry standards where available.
According to the Larridin State of Enterprise AI Report, 88% of leaders believe AI measurement determines market winners. With that in mind, link maturity advancement to operational efficiency gains, customer experience improvements, and revenue impact. Show how moving from stage 2 to stage 3 is likely to improve financial performance for your organization.
MIT CISR research identifies four challenges in moving from stage 2 to 3:
Once those four challenges are clear, it helps to translate them into practical priorities by stage.
AI maturity measurement only creates value when it drives action. Use assessment results to guide investment, prioritize initiatives, and track progress toward business goals.
Organizations that improve AI maturity over time get more value from AI. They make data-driven decisions about AI adoption, scale successful AI practices across operations, and unlock the potential of AI for competitive advantage. McKinsey research finds that high performers focus on six areas: strategy, talent, operating model, technology, data, and adoption.
Start measuring AI maturity today. Conduct a baseline assessment, identify target capabilities, create a phased roadmap, track metrics showing progress, and connect advancement to business value. Organizations that measure and advance AI maturity will be the ones that optimize artificial intelligence for sustainable competitive advantage.
As an old management cliche states elegantly, “that which gets measured gets done.” Measurement of AI usage, including AI dashboards, is critical to making progress. (link to article, Building an AI Usage Dashboard That Drives Action)
Larridin provides industry-leading AI dashboards that measure progress and inform action. Even if you have experience in building dashboards internally, you may find that engaging with Larridin for AI measurement saves you time and money, with better quality and support. If you don’t have such experience, working with an established provider can greatly accelerate your AI implementation efforts.
If you want to work quickly to progress in your use of AI, improve your company’s financial performance as a result, and make the journey easier and more productive by partnering with Larridin, consider reaching out for a demo.
Ready to measure and advance your AI maturity?