Generative AI Adoption Maturity Model
The last two weeks’ articles on the Generative AI adoption Maturity framework sparked discussion within the AI circle. Thank you for sharing the comments and feedback, and even triggering a few thought-provoking views on this topic.
We have started a journey to develop a Gen AI Maturity Model or framework as a joint effort with a few organizations’ colleagues, friends, and leadership teams.
Earlier work:
- Where Are You on the Generative AI Maturity Curve?
- Generative AI Maturity Framework for Structured Guidance
- Why Maturity matters and levels of Gen AI Maturity model
This week, we will continue this journey:
Generative AI Maturity Model Overview
The model defines six sequential levels and six dimensions.
Levels:
Six levels from ‘Aware’ to ‘Transformative.’ Each step represents a distinct operating state. At Level 1, teams dabble with public LLMs; nothing is funded or governed. Level 2 introduces budgeted experiments and basic data hygiene.
By Level 3, a single function runs production Gen-AI with an MLOps pipeline. Level 4 extends that success enterprise-wide under a unified ethics board. Level 5 shifts to an agent platform/mesh: autonomous agents handle end-to-end workflows with human guardrails. Level 6 is the destination, an AI-first organization where models and data pipelines self-optimize and drive continuous reinvention.
Now, when we add Agentic AI into the situation, as the next wave is moving to Agentic AI
Level 1
Level 1 marks the curiosity phase. Teams test ChatGPT, Gemini, Grok, or open-source LLMs in isolation. There is no budget, strategy, or policy alignment, so value extraction is hit-or-miss, and risk exposure is high. Shadow IT flourishes; sensitive data often ends up in public models.
The goal here is not to rush pilots into production but to establish guardrails and shared understanding.
Quick wins include a concise experimentation policy, an executive crash-course on Gen-AI, and a central repository/registry of who is doing what. These steps convert scattered enthusiasm into a managed exploration path and prepare the ground for Level Two.
Level 2
Level 2 transitions from curiosity to structured exploration. Funding exists, a sponsor is named, and a handful of proofs-of-concept are live.
Data work starts, catalogues, cleaning, the first vector database, quickest to begin with your existing Oracle Database 23ai.
Success depends on capturing learning across silos and linking each PoC to a clear value hypothesis.
The key moves now are to ratify a governance charter, select the highest-impact use cases, and create a basic model repository/registry so experiments don’t disappear.
Skill gaps emerge quickly, so an accelerated training sprint keeps momentum.
Agentic capabilities remain minimal; prompt libraries dominate, with a few sandbox task agents being tested under tight controls.
Executing these steps allows the organization to cross the threshold to the next level.
Level 3
Level three marks the shift from trial to dependable operations. A live Gen AI workload now serves real users, often in internal employee support, customer support, marketing copy, or code assistance.
Reliability matters, so an MLOps pipeline handles versioning, tests, and automated deployment.
Real-time dashboards watch drift, latency, and spend, while red-team exercises probe security and bias before each release.
Business KPIs link model output to measurable value, creating a feedback loop between tech and finance.
Risks pivot from ‘will it work’ to ‘will it stay accurate and affordable?’ Drift, cost spikes, and thinly spread talent become the primary watch-outs. Priority moves include automated rollback, incident playbooks, and targeted upskilling for PromptOps and AI-SRE roles.
Agentic capability is still narrow, single-task agents run with mandatory human sign-off, but the governance foundation is now strong enough to scale.
Next week, we will cover levels 4 to 6 and continue the journey of building the Generative AI Adoption Maturity Model.
