Imagine you're an executive at a big enterprise company. All your competitors seem to be knee-deep in GenAI initiatives — chatbots galore and cost-saving automation or optimization of their internal processes driven by LLMs. These are new technologies so you look to industry leaders to explain how you should think about how your own organisation might grow and develop its own expertise. It is in this context that we find so-called maturity models being drafted.
There are four such models that get cited when you look for best practices around productizing LLMs and associated GenAI technologies. The best-known of these is Microsoft's "GenAIOps Maturity model" which, like the others, offers a roadmap and series of stages through which you should pass on your way to maturity. All have been published in the last year as some stability and best practices have started to coalesce.
In this blogpost I'll go through the details of the four maturity models before offering my own assessment of whether these are useful. Spoiler: probably not so much, but they do all point to things that you and your teams might want to incorporate into your own processes!
Both Microsoft and Google previously published widely-cited MLOps maturity models which I also wrote about on this blog. Microsoft seeks to continue in this tradition though this time round it seems to have been a bit more consciously a sales exercise since each stage in the model is tied to specific products and services that are offered on the Azure cloud platform.
A Note on Terminology: LLMOps vs GenAIOps vs MLOps
Microsoft have gone all in on the use of the term GenAIOps but the industry as a whole seems to prefer LLMOps. You might wonder why or whether we need a new term when we already have MLOps, a term that in any case was fairly free and open. The use cases of Generative AI include more than just LLMs, though it does seem that the real revolution is being felt in that specific area rather than in the world of images, video or sound.
Clearly there are commercial benefits sought from defining a new term, especially if that term takes off, but (as we'll see below) it isn't clear whether a whole new term is warranted. While there are some new techniques being used, many of the core principles that grew out of the DevOps movement and were applied to machine learning are still fit for purpose.
The proliferation of terms like LLMOps and GenAIOps has practical implications for teams working in AI development and operations. On one side, specialized terminology can help teams better articulate the unique challenges they face when deploying and maintaining large language models - from prompt engineering workflows to handling context length limitations to managing API costs. Teams might find it easier to justify dedicated tooling and processes when they can point to LLMOps as a distinct discipline.
However, this terminological splintering also risks creating artificial silos. Many organizations are already struggling to bridge the gap between traditional software development and ML operations - adding another conceptual layer could further complicate cross-team collaboration. There's also the practical challenge of resource allocation and skill development: should teams now have separate LLMOps specialists, or should existing MLOps engineers expand their expertise? The answer likely depends on organizational scale and needs, but the terminology shift could pressure smaller teams to unnecessarily separate these roles.
Perhaps most importantly, practitioners need to focus on solving real problems rather than getting caught up in terminology debates. The core challenges - reproducibility, monitoring, governance, and efficient deployment - remain similar whether we call it MLOps, LLMOps, or GenAIOps. Teams would be better served by identifying which existing MLOps practices they can leverage and which genuinely require new approaches, rather than assuming they need to build entirely new processes just because they're working with LLMs.
The Microsoft GenAIOps Maturity Model
Microsoft's GenAIOps Maturity Model presents a four-level framework for assessing and developing an organization's generative AI operations capabilities. I'll go into it in a bit more detail as it is the most interesting and technically developed of the four models.
Level 1 - Initial
This foundational level focuses on basic experimentation and discovery, characterized by:
- Testing models in a basic way
- Elementary prompt engineering efforts
- Basic evaluation and monitoring processes
- Manual processes and isolated experiments
Level 2 - Defined
At this stage, organizations move toward more systematic operations:
- Structured prompt engineering practices (meta processes within the organisation as well as actual structure for how prompts get defined)
- Implementation of RAG (Retrieval Augmented Generation)
- Iteration processes for model augmentation
- Structured evaluation methods
Level 3 - Managed
This level represents significant operational maturity:
- Comprehensive prompt management systems (tracking / tracing)
- More sophisticated prompt evaluation processes
- Real-time deployment capabilities
- Advanced monitoring systems
- Automated alerting mechanisms
Level 4 - Optimized
The highest level of maturity, featuring:
- Fully integrated CI/CD environments
- Automated monitoring systems
- Automated model and prompt refinement processes
- Fine-tuning capabilities for LLMs targeted at specific use cases
Maturity processes
The diagram representing their maturity model has a green arrow that continues further from here, implying that the work of continuing maturity never ceases.
The full post in which this framework is proposed offers suggestions for how to move from level to level, including suggestions like:
- Experimenting with different LLM APIs to understand their capabilities
- Implementing structured prompt design and engineering practices
- Establishing basic metrics for LLM application performance evaluation
- Developing more sophisticated prompt engineering techniques
- Systematizing LLM deployment processes, potentially incorporating CI/CD
- Implementing advanced evaluation metrics (groundedness, relevance, similarity)
- Incorporating content safety and ethical considerations
- Implementing advanced LLM workflows with proactive monitoring
- Developing predictive analytics and comprehensive content safety monitoring
- Fine-tuning LLM applications for specific use cases
- Establishing advanced version control and rollback capabilities
The framework emphasizes that beyond technical capabilities, organizations should focus on:
- Maintaining awareness of latest developments in the field
- Regularly evaluating LLM strategies against business objectives
- Fostering a culture of continuous innovation and learning
- Contributing to the wider community through knowledge sharing
Note that that mature GenAIOps, as defined in this model, isn't just about technical sophistication, but also about building sustainable practices and a learning culture within the organization. This is one of the many ways in which this model overlaps with similar efforts around DevOps and MLOps.
IBM Maturity Model for GenAI Adoption
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IBM's maturity model outlines five distinct phases of organizational maturity in generative AI adoption. Organizations typically begin in Phase 1, characterized by experimental use of generic models, where efforts are largely localized and reactive, with limited organizational understanding of the technology. During this initial phase, IBM recommends focusing on developing basic awareness and initiating pilot projects.
Moving into Phase 2, organizations start implementing fit-for-purpose models in their primary generative AI environment. While processes remain inconsistent, this phase marks the beginning of formal documentation and a growing recognition of data quality requirements. At this stage, IBM emphasizes the importance of establishing a centralized strategy and implementing basic training while evaluating data standards.
Phase 3 represents a significant evolution, where organizations begin leveraging enterprise-wide data in their generative AI environment. This phase introduces organization-wide standards and established governance frameworks, with an increased focus on ethical considerations. IBM recommends enhancing collaboration, addressing technical debt challenges, and implementing continuous feedback mechanisms during this phase.
The fourth phase demonstrates considerable maturity, characterized by the ability to run and infer AI models at scale while optimizing compute costs. Organizations at this level engage in active metrics tracking, quantitative evaluation, and data-driven decision-making. IBM suggests focusing on advanced analytics, linking initiatives to business objectives, and implementing robust risk management strategies.
In the final and most mature phase, Phase 5, organizations can build and use models across their environment securely and at optimal costs. This phase is marked by continuous refinement, established feedback loops, and a proactive approach to AI implementation. IBM's recommendations for this phase include fostering innovation, engaging with experts, and regularly reviewing governance frameworks to ensure continued effectiveness and alignment with organizational goals.
Both Microsoft and IBM emphasize the importance of experimentation during the early phases but then over time adding in more monitoring and automation. There's a clear parallel in how both models view advanced maturity. Microsoft's "Optimized" level focuses on fully automated monitoring and CI/CD environments, while IBM's Phase 4 and 5 emphasize running models at scale and building secure, cost-optimal implementations. Also, IBM's model places more explicit emphasis on cost optimization and business metrics throughout its phases, particularly in Phases 4 and 5. While Microsoft's model touches on this, it's not as central to their framework.
Overall, Microsoft's model is more specific about technical implementations (like RAG, prompt engineering, etc.), while IBM's model takes a somewhat more business-oriented view of maturity. The remaining two models are also similarly focused at the business level, albeit with some reference to technical implementation details.
Datastax Generative AI Maturity Model
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The Datastax model takes a distinctive approach by evaluating maturity across four key pillars - Contextualization, Architecture, Culture & Talent, and Trust - while progressing through four maturity levels: Beginning, Responsive, Proactive, and Leading.
At the Beginning level, organizations start with foundational models and implement basic security measures. They begin exploring prompt engineering capabilities and establish event streaming as their initial architectural foundation. This represents the entry point for organizations starting their generative AI journey.
Moving to the Responsive level, organizations advance to implementing fine-tuning capabilities and begin automating code generation. They establish vector stores for improved data management and notably begin fostering a "culture of safety" within their operations. This level marks the transition from basic implementation to more sophisticated operational capabilities.The Proactive stage demonstrates significant maturity through continuous learning and adaptation mechanisms. Organizations at this level actively contribute to open source software (OSS) and implement automated testing procedures. Importantly, ethical considerations become systematically integrated, with routine ethics and human rights reviews becoming standard practice.
At the Leading level, organizations achieve the highest degree of maturity. They develop new capabilities, engage actively with standards bodies, and maintain strong relationships with regulators. Their governance frameworks become fully auditable, representing a sophisticated level of operational excellence and compliance.
What sets the Datastax model apart is its holistic view across the four pillars, suggesting that true AI maturity requires balanced progress across technical capabilities (Architecture), data handling (Contextualization), organizational development (Culture & Talent), and responsible AI practices (Trust). This multi-dimensional approach acknowledges that successful AI implementation requires excellence not just in technical implementation but across all aspects of the organization.
AIM Research Generative AI Maturity Framework
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The AIM Research Generative AI Maturity Framework presents a five-level progression model that focuses on organizational adoption and integration of generative AI technologies:
- Exploration represents the entry point, where organizations begin basic tool usage, typically experimenting with fundamental generative AI capabilities like image generators and text tools. This stage is characterized by initial discovery and learning.
- Experimentation marks the transition to more structured engagement, where organizations begin developing proofs of concept (POCs) and implementing small projects. Notably, this level sees the formation of dedicated teams that begin to coalesce around generative AI initiatives.
- Implementation represents a significant step forward, where organizations begin developing customized solutions and, importantly, see adoption spreading beyond technical departments. This phase is characterized by the spread of GenAI into various business functions, including marketing and product design.
- Integration shows maturity in deployment, where GenAI becomes a focus across the organization and begins to interface with external stakeholders. This level is distinguished by the inclusion of generative AI in relationships with customers and suppliers, marking its evolution into a core business capability.
- Transformation represents the highest level of maturity, where generative AI is used across the entire enterprise and provides clear competitive advantages. At this stage, AI has become thoroughly embedded in operational processes and creates distinctive value for the organization.
What's particularly notable about the AIM framework is its emphasis on organizational adoption patterns rather than technical capabilities. It charts a path from isolated experimentation to enterprise-wide transformation, with special attention to how generative AI spreads from technical to non-technical departments and eventually to external stakeholders. The framework suggests that true maturity isn't just about technical sophistication but about how deeply and broadly AI is integrated into business operations and relationships.
Are These Maturity Models Actually Useful?
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The rapid evolution of generative AI has spawned multiple frameworks attempting to guide organizations through its adoption, just as we saw for MLOps. While these models offer valuable perspectives, they should be viewed more as descriptive snapshots of current thinking rather than prescriptive roadmaps. This is particularly true given how quickly the landscape is changing – we're still discovering the most effective ways to deploy and maintain GenAI systems at scale.
What The Models Get Right
These frameworks correctly recognize that technical capability alone isn't sufficient for successful GenAI adoption. They emphasize the critical interplay between technological advancement and organizational readiness. The models also reinforce essential engineering practices that have proven their worth in traditional software development and MLOps: comprehensive monitoring, systematic automation, clear standards, and robust governance frameworks.
Notable Blind Spots
Perhaps most striking is the complete absence of AI agents from these models. As autonomous systems become more sophisticated and organizations experiment with agent-based architectures, this gap becomes increasingly apparent. This isn’t necessarily to endorse or play up the claims of proponents of agents, just that to ignore what has become such a big part of the discussion of GenAI in 2024 feels like an oversight. The models also give limited attention to emerging architectural patterns, though this partly reflects the relative scarcity of published real-world implementations.
Additionally, many models heavily emphasize current patterns like RAG (Retrieval Augmented Generation), which, while important today, may become just one of many architectural approaches as the technology matures. This focus on current best practices, while practical, might inadvertently constrain thinking about future possibilities.
New Considerations for GenAI
While many core DevOps and MLOps principles remain fundamental, GenAI introduces unique challenges that organizations must address:
- Prompt Engineering Lifecycle: Managing, versioning, and optimizing prompts across development and production environments
- Cost Management: Balancing token usage, model size, and inference speed with business value
- Output Safety and Reliability: Ensuring consistent, appropriate, and accurate model outputs
- Vendor Independence: Building systems that can adapt to rapid changes in model capabilities and availability
The Path Forward
Rather than rigidly following any single maturity model, organizations should focus on building flexible, adaptable systems that can evolve with the technology. The most successful approaches we've seen share some common characteristics:
- Start with Business Value
- Focus on solving real problems rather than chasing arbitrary maturity levels
- Let actual use cases drive technical decisions
- Maintain clear metrics tied to business outcomes
- Build on Proven Foundations
- Leverage existing DevOps and MLOps practices where applicable
- Maintain strong engineering fundamentals (monitoring, testing, automation)
- Keep systems modular and adaptable
- Stay Pragmatic and Flexible
- Begin with smaller, manageable projects
- Iterate based on practical experience
- Remain open to emerging patterns and technologies
Looking Ahead
The field of GenAI operations is still in its early stages, and today's best practices might become tomorrow's anti-patterns. While these maturity models provide useful reference points for discussion and planning, they shouldn't be treated as rigid roadmaps. Instead, organizations should focus on building robust, flexible systems that can adapt to changing requirements and technologies.
What matters most is solving real problems while maintaining strong engineering practices. The most successful organizations will be those that can balance immediate practical needs with long-term architectural flexibility, all while keeping a close eye on the rapidly evolving capabilities of generative AI technologies.
As we move forward, we'll likely see these maturity models evolve to encompass new patterns and practices. For now, they serve best as conversation starters and rough guides rather than detailed blueprints. The real measure of maturity will be how effectively organizations can adapt to and leverage new capabilities as they emerge, while maintaining reliable and efficient operations.
