AHFE Open AccessArtificial Intelligence Maturity Model (AIMM)
Abstract
Maturity models have long served as effective frameworks for guiding organizations toward progressively advanced practices, offering structured pathways for capability development and benchmarking. Models such as the Capability Maturity Model Integration (CMMI), Organizational Project Management Maturity Model (OPM3), Portfolio, Program and Project Management Maturity Model (P2CMM), and the Data Management Maturity Model (DMM) have each demonstrated the value of systematic assessment in driving adoption, standardization, and continuous improvement across diverse fields. Despite the rapid rise of artificial intelligence (AI) adoption, no broadly accepted maturity model exists to help organizations evaluate and advance their AI capabilities. This paper introduces the Artificial Intelligence Maturity Model (AIMM), a structured framework designed to assess individual and organizational sophistication in AI agent utilization. The proposed model defines distinct maturity levels, enabling organizations to identify their current state, benchmark progress, and establish a roadmap for advancement. By providing a standardized approach to AI capability assessment, AIMM can accelerate more effective use of AI technologies across industries.
Keywords: AI, Agent, Maturity Model, LLM
DOI: 10.54941/ahfe1007059
Cite this paper
More from this volume
- An Experimental Study on Consensus Building with an AI Chatbot Across Two Topics
- An Agent-Based Simulation Framework for ADHD: Modeling Attention Regulation and Adaptive Therapeutic Interventions
- CRMSON: Co-Designing Adaptive and Ethical AI Systems to Address Mental Health Barriers in Aviation
- Usability Evaluation of FAIR Data Planning in the Data Stewardship Wizard
- Seeing the Invisible Load: XR + Multimodal Sensing for Cognitive Ergonomics in Industrial Training
- Conceptual Framework for Designing Domain-Specific LLM-Based Information Systems
- Shaping Conversations: Custom GPTs to Spark Reflection in Design
- Privacy at the Core: Toward Automated Detection of Privacy-Sensitive Content in an LLM-Based Care Documentation Support System
- Dynamic Difficulty Adjustment via Dynamic Scripting: An Empirical Study of Player Flow in a Brawler Game
- Sinusoidal time-based features and human error metrics: Advancing software defect prediction in safety-critical systems
- Designing an Experimental Method for Evaluating Divergent Thinking with a Color Queue under Time Constraints
- Designing Experiments to Explore Optimal Timing for Refreshing Breaks During Cognitive Tasks Using Time-Series Changes