11. Inclusive and Sustainable AI Maturity Models

This chapter argues that many organisations’ AI efforts remain fragmented—dominated by pilots, proofs of concept, and isolated vertical solutions—because they lack a coherent vision, governance, and an implementation roadmap aligned with organisational purpose. It positions AI maturity models as a practical response to this gap: structured frameworks that assess current capabilities against benchmarks and guide sequenced improvement. Drawing on evidence that large investments in generative AI often fail to translate into measurable returns, the chapter emphasises that sustainable progress depends on balancing architecture and process integration with culture and upskilling, risk management, and explicit ethical commitments, including sustainability and inclusion.The chapter then surveys a set of prominent AI maturity models (e.g., SEI, MIT CIRS, Accenture–Stanford, Gartner, Microsoft, and Jisc), comparing their stages and focal capability areas and noting differences in transparency and emphasis on responsible AI. It highlights particular value in models that explicitly incorporate DEIA and sustainability dimensions, and proposes that embedding these concerns into general AI maturity models can raise baseline practice across ecosystems—analogous to maturity-model effects seen in software engineering and healthcare digitisation. Concluding, it recommends using maturity models as instruments to institutionalise inclusive and sustainable AI, anchored by a strategic agenda, and outlines future work by the AI4ALL community to evaluate and help validate models against DEIA and sustainability criteria.