These days, everyone wants to implement Generative AI (Gen AI) but an approach that works well for one business does not work for other businesses. A lot of Gen AI success depends on the data maturity level of your organization. That is why understanding your maturity level for Gen AI is crucial for success. To help you set realistic goals, below are different levels of Gen AI maturity and our recommendations on each.

1. AI Dabblers (Just starting out): You’ve heard of Gen AI and the bosses are interested. You’re exploring options and figuring out what it can do. But there is a risk of overhyping the technology.

Key characteristics of an organization in this level include:

• Strategy: Lacks a broad strategy; mostly aspirational.
• Use Cases: Basic awareness of potential uses, often influenced by competition or media.
• Tech & Tools: Your company/teams use Gen AI tools like ChatGPT frequently.
• Data: You have identified data associated with use cases, but you have concerns about data usage & security.
• Ops: Awareness of Gen AI’s potential in IT and software operations, with controlled access to LLM chat models.

Our Recommendation: If you think you are at this level, focus on building a foundational understanding and exploring speculative use cases with available resources.

2. AI Experimenters (Giving it a try): You’re testing the waters with pre-built Gen AI tools like ChatGPT. You’re also learning the ropes and setting some rules to keep things under control. This phase is about controlling and channeling enthusiasm into more structured experimentation. Characteristics are:

• Strategy: Executive interest leads to funding and the development of initial strategic plans.
• Use Cases: Proof of concepts (POCs) are identified along with costs, ROI, and technical issues.
• Tech & Tools: Trials with Gen AI-enabled software development and prompt tools.
• Data: Testing on small data sets, improvements in data literacy, and initial taxonomy development is done.
• Ops: Development of practice guidelines for using LLM and other models.

Our Recommendation: Conduct POCs to gain clearer insights into technical and operational challenges and begin developing governance frameworks.

3. AI Early Adopters (Making it work): You’ve moved beyond basic tools and are tackling your own projects. You’re also getting better at managing data and keeping everything running smoothly. Disappointments arise from data issues and ROI challenges, but governance begins to mature:

• Strategy: Prioritization of strategic and operational opportunities, with feedback mechanisms in place.
• Use Cases: You take up more complete and complex use cases with increased experience.
• Tech & Tools: Success with techniques like Retrieval-Augmented Generation (RAG) and vector databases.
• Data: Implementation of data governance policies to handle issues like hallucinations and bias.
• Ops: Expansion of software practices and tools into production environments.

Our Recommendation: Prioritize strategic opportunities and establish comprehensive governance to mitigate risks and manage costs.

4. Experienced Campaigners (Gen AI masters): You’re using advanced Gen AI techniques and seeing a big impact on your business. You’re also thinking about the ethical implications and how Gen AI affects your employees.

• Strategy: Continuous evolution of strategies with agile approaches.
• Use Cases: Continuous evaluation and evolution, integrating with development processes.
• Tech & Tools: Advanced techniques like Chain of Thought and agentic architectures explored.
• Data: Robust data engineering and cleansing practices, with bidirectional metadata exchange.
• Ops: Integration of Gen AI throughout the software development life cycle.

Our Recommendation: Focus on advanced techniques and continuous strategy evolution to drive innovation and efficiency.

5. Evolved, State of the Art (SOTA) AI: At a high level, at this stage AI and Gen AI projects work together in your company to automate tasks, improve sales & services, enhance processes and make smarter decisions.

• Strategy: Unified Gen AI and AI strategy.
• Use Cases: Automation and augmentation of processes and tasks.
• Tech & Tools: Composable platform architecture for optimal end-to-end integration.
• Data: Semantic reconciliation across different data corpuses.
• Ops: Continuous integration and deployment integrating AI, Gen AI, and software methods.

Our Recommendation: Develop a composable platform architecture and focus on integrating AI and Gen AI across all operations for maximum synergy.

Which Gen AI maturity stage are you at?

Prepare to rapidly evolve since Gen AI itself is rapidly changing each day. Stay agile and be ready to adapt over the next 2 years. You can use something like the detailed version of the above maturity model to keep track of progress and identify specific areas needing progress. You also need to consider the fact that different project areas will mature at different rates. So, tracking of project level progress and ROI should be independent for each project.

Konverge AI’s Gen AI readiness assessment helps organizations understand their Gen AI maturity for better navigating Data & AI complexities. Holistically, achieving Gen AI maturity requires advancing all areas of the organization. We can help you at any stage of your AI journey.