The IBM Maturity Model for GenAI Adoption
IBM Generative AI reference architectures
Overview

In 2020 (with further updates in 2021), IBM introduced the AI maturity framework for enterprise applications with 7 dimensions.

With the advent of GenAI, we have aligned the IBM GenAI Architecture with an "IBM Maturity Model for GenAI Adoption":

Phase

Characteristics

Recommendations

1

  • Consume generic models
  • Unpredictable and reactive approach
  • Localized efforts
  • Limited understanding
  • Develop basic awareness
  • Initiate pilot projects

2

  • Fit-for-purpose models on primary Gen AI env.
  • Inconsistent processes
  • Initial documentation
  • Recognizing data quality needs
  • Establish centralized strategy
  • Basic training
  • Evaluate data standards

3

  • Leverage enterprise-wide data on Gen AI env.
  • Organization-wide standards
  • Established governance
  • Focus on ethics
  • Enhance collaboration
  • Address GenAI challenges
  • Continuous feedback mechanisms

4

  • Run and infer Gen AI models to scale compute/costs
  • Active metrics tracking
  • Quantitative evaluation
  • Data-driven decision-making
  • Advanced analytics
  • Link to business objectives
  • Robust risk management

5

  • Build & use models cross env. securely at optimal costs
  • Continuous refinement
  • Established feedback loops
  • Proactive approach
  • Foster innovation
  • Engage with experts
  • Review governance framework
GenAI Capabilities Mapped to the Capability Model

Here's how the IBM GenAI Architecture Capability Model maps to this maturity model:

Phase

GenAI Capabilities

Governance Maturity

1

  • GenAI Resources: Hardware and platform basics.
  • Basic Data Management: Initial storage and management of data.

No AI lifecycle governance

2

  • Model Hub: Basic model importing and data importing capabilities.
  • Supporting Capabilities: Basic IT operations for GenAI.
  • Initial steps in GenAI Application Development: Beginning to tune foundational models.

Some AI policies available to guide AI lifecycle

3

  • Model Hosting: Deployment of models as API services, and Model Access Policy Management.
  • Model Customization: Introduction to tuning and training models for specific needs.
  • GenAI Tuning: Basic customization using Prompt Engineering and Model Fine-tuning.

Common set of metrics to govern AI lifecycle

4

  • Model Governance: Addressing risks like bias introduction, regulatory and compliance adherence.
  • Model Monitoring: Real-time monitoring capabilities including Bias Detection and HAP Detection.
  • GenAI Application Capabilities: Incorporating advanced features like Orchestration and Intent Detection.

Automated validation and monitoring

5

  • Advanced Model Customization: Employing cloud platforms for dynamic needs.
  • Prompt Monitoring and Security: Ensuring models are protected from advanced threats.
  • Advanced GenAI Tuning: Thoroughly customizing models to enterprise-specific jargon and processes.
  • Advanced GenAI Application Development: Full-feature generative AI application development and potential model creation from scratch.

Fully automated AI lifecycle governance

Resources 7 dimensions in the Cloud Adoption and Transformation framework An IT Maturity Model IBM - AI Maturity Framework for Enterprise Applications Cloud Adoption and Transformation Assessment IBM Modern Integration Assessment
Next steps

Talk to our experts about implementing a hybrid cloud deployment pattern.

More ways to explore Hybrid Cloud Architecture Center Diagram tools and templates IBM Well-Architected Framework
Contributors

Mihai Criveti, Wissam DibChris Kirby

Updated: December 5, 2023