Navigating the AI Maturity Framework
By: Abhinav Kolhe
Defining "good" AI can feel like chasing a moving target. The field of AI is evolving rapidly, with new models, techniques, and innovations emerging almost daily. Each year, tens of thousands of research papers and articles are published at an ever-increasing pace, and new findings quickly make their way into products thanks to the wide array of programming language libraries available.
To truly gauge the effectiveness of AI, we need to consider several factors. These include how well AI solutions serve business users, the direct value clients gain from AI, the sophistication of the tools and frameworks used, the inclusion of bias detection and removal algorithms, the quality of data cleansing mechanisms, and how understandable AI decisions are to end-users.
We've observed that the success of AI applications is closely tied to how they perform across these parameters. For example, an application that doesn't prioritize data quality or bias detection is unlikely to deliver high value to clients. On the other hand, where the AI runs—whether on a public cloud, private cloud, or in a hybrid model—adds another layer of complexity. Additionally, the need to monitor AI performance is increasingly becoming a critical part of AI design itself.
Maturity Assessment Framework
We created a model to assess the AI Maturity of our Offerings to measure progress towards AI infusion.
Dimensions + Criteria
Impact on our Business
Business Impact, Portfolio Impact
Value to Client Business Process Outcome, Differentiators
Technology Sophistication
Appropriateness of the technology to the business problem, Learning Techniques, Reuse of Models, Use of Inner Source or Open Source
Trustworthiness
Integrity, Quality, Bias (fairness), Explainability, Security
Ease of Use
Intuitive for use by the intended user
AI Operating Model
Deployment (Manual, Automated), Update Frequency, Infrastructure/Architecture Scale, Monitoring
Data
Data Acquisition & Instrumentation, Data Management
Each AI capability is assessed on a scale of 0 to 3 across these dimensions,
and the overall score places it in one of three phases:
For business leaders, dimensions such as "impact on your business" and "value to the client" are critical. If you're working in an enterprise product company or a systems integrator, these factors will resonate. However, IT organizations incorporating AI into their systems may need to adapt these dimensions to meet their specific business goals. For technical teams, the other dimensions—such as technology sophistication, trustworthiness, and ease of use—are where the focus will be. Business leaders should also familiarize themselves with these technical aspects, as they directly impact the adoption and success of AI.
AI Feature/Offering Maturity: Visualization
Illustrative Example; Impact on Business: 1
A radar plot helps visualize these scores, making it easier to track progress. For instance, in one of our AI applications, you can see how the AI is progressing from "Gold" toward "Platinum."
Silver: At this stage, the AI capabilities have just been introduced to make the product AI-ready. It's the phase where you start exploring AI—how it impacts your business, the tools and technologies needed, and how to prepare data for AI use. While AI at this level enhances the user experience, it’s not yet crucial to the business outcomes that enterprise users are focused on.
Gold: When AI reaches the Gold level, it starts delivering real business value. It offers a competitive edge, providing data-driven recommendations and basic explanations for decisions. Importantly, these AI features are accessible to line-of-business users, so you don't need data scientists to interpret the insights. At this stage, strong data hygiene practices and well-automated engineering processes are also in place, ensuring reliability.
Platinum: AI capabilities at the Platinum level are a true differentiator. They’re deeply embedded in the enterprise's mission-critical workflows, where automated decision-making is trusted, and users only need to step in for exceptions. The AI here is more advanced, able to learn from new data and adapt based on user feedback. Its decisions are transparent and easily understood by business users, who can tweak settings to fine-tune results. Additionally, robust, automated data management and governance systems ensure the AI’s integrity and reliability.
Not every AI capability needs to move through all the phases. Organizations should weigh the costs and benefits of advancing an AI capability, considering both the business outcomes it delivers and the investment required to elevate it from Silver to Gold or Platinum.
Maturity Framework Detailed Criteria