Introducing Maturity Maps — A New Way to Measure AI Adoption
The AI Daily Brief: Artificial Intelligence News and Analysis Podcast Recap
Published:
Duration: 25 min
Summary
This episode introduces Maturity Maps, a new framework to measure AI adoption within companies across ten functions. It offers insights into the limitations of existing benchmarking methods and emphasizes the need for robust data sharing to improve AI maturity understanding.
What Happened
Maturity Maps have been introduced as a new framework for assessing AI adoption and readiness in organizations. They categorize AI maturity into six areas, including deployment depth, systems integration, data, outcomes, people, and governance, across ten functions such as customer service and IT.
The AI Daily Brief podcast, sponsored by KPMG, Robots and Pencils, and Blitzy, discussed AI ROI benchmarking, focusing on eight dimensions such as time savings and increased output. Despite strong self-reported impacts from AI initiatives, the data is limited due to its imprecision and focus on advanced users.
AI Maturity Maps utilize a five-point scale to assess organizational progress relative to an 'on track' line. This line is subjective and reflects where organizations should be, not their current status. The maps are informed by 480 studies and surveys involving over 150,000 respondents.
Common findings from these studies include a gap between AI adoption and embedding, as well as discrepancies between worker and leader perceptions of AI training and upskilling. Deloitte highlighted that 93% of AI spending is on infrastructure, leaving only 7% for people-related investments.
Data management emerged as a critical constraint, with many organizations scoring low in this area. The rapid adoption of AI has led to a lack of proper ROI measurement, impacting the assessment of outcomes.
Specific functions like customer service, engineering, and IT are on track in terms of deployment depth and systems integration. However, customer service workers experience high stress as AI takes over routine tasks, leaving more complex cases to humans.
Governance is lagging in many organizations, with IT often taking on AI governance responsibilities. Finance, however, scores well on governance due to existing regulatory frameworks.
The maturity maps are in their early stages, with plans to refine them based on organization size and industry. The speaker encourages data sharing to help create new benchmarks and better understand what being 'on track' means in AI adoption.
Key Insights
- Maturity Maps offer a structured approach to evaluating AI adoption across six key areas, providing a more comprehensive understanding of organizational readiness.
- AI ROI benchmarking reveals strong positive impacts from AI initiatives, though the data is limited due to its focus on advanced users and imprecision.
- There is a significant gap between the perceived and actual adoption of AI, with a notable discrepancy in AI training and upskilling perceptions between workers and leaders.
- Finance functions score well on governance due to existing regulatory frameworks, while other functions struggle with data management and integration challenges.
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