Generative AI Maturity Model

According to OpenAI, over two million developers and more than 92% of Fortune 500 organizations are experimenting with or deploying generative AI. To help them assess their progress, we created a simple maturity model.

This maturity model is based on our article defining cognitive applications and platforms. Check it out for additional context and background.

It's important to note that generative AI adoption must occur in three dimensions: people, processes, and technology. Here, we present the technology adoption maturity model. Similar models can be built for the other two dimensions: people and processes.

Stage 0 - Experimentation, not shown in the diagram. At this point, multiple groups across the organization are trying and learning, but nothing is deployed systematically to enhance performance or productivity.

Stage 1 – AI-enabled content generation & summarization. Generative AI assists humans with content generation, most commonly in software development, marketing, sales, and customer service. It may also be used to summarize information, such as meeting notes, call transcripts, video recordings, and other types of content, with the results automatically saved in systems of record: Salesforce, ServiceNow, and others.

Stage 2 – Retrieval-augmented generation (RAG). At Stage 1, organizations become aware of two main challenges associated with generative AI. First, AI models are frozen at the time of their training and often produce outdated or incorrect content. Second, generated content is too generic to be useful. They address these challenges by enhancing models with context, relevant proprietary information retrieved from their systems of record.

Stage 3 – Intelligent automation. At Stages 1 and 2, large models are used to process and create content. At Stage 3, organizations begin taking advantage of the models’ ability to use tools, such as pricing calculators, programming languages, and enterprise APIs, to automate individual steps in business processes. A pricing calculator can be used to generate a personalized customer proposal. A Python sandbox can be employed to generate and run code for advanced data visualizations. An enterprise API can be utilized to enrich leads with additional information. There is an infinite number of use cases and applications.

Stage 4 – Task-decomposition and task-specific models and tools. At Stage 3, organizations become aware that AI models are limited in their ability to plan and execute complex assignments. Domain and AI experts amplify their reach by breaking down such assignments into tasks that can be consistently and reliably performed by the models. For each task, they identify task-specific tools and fine-tune task-specific models. Tasks are assembled into flows.

Stage 5 – Integration and transformation. At this stage, AI has been seamlessly blended into the organizational processes and information systems, most likely as a result of a deep and thoughtful AI-enabled transformation. Humans are not aware that they are using AI, it is not even a term banded around, but their work is less stressful, more satisfying, and more productive.

Where is your organization in this maturity model?

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