A maturity model is a framework used to assess how well your organization uses a capability—in this case, AI. It helps chart your journey from early experimentation to full-scale transformation, guiding technology adoption and investment decisions.
Most maturity models have stages like:
-
- Initial – Ad hoc, reactive use of technology
- Repeatable – Some processes, basic KPIs emerging
- Defined – Standardized, documented, metrics-driven
- Managed – Data-led performance tuning
- Optimized – Scalable innovation, continuous improvement
Industries like IT, healthcare, manufacturing, and BFSI have widely adopted this approach to scale AI with confidence.
The AI Maturity Journey
At Applied AI Consulting, we’ve helped organizations across sectors define and accelerate their AI journeys. Based on industry insights and client experience, here’s how the AI maturity curve typically looks:
Level 1: Awareness
Organizations at this stage are learning about AI. There’s excitement—but little clarity on how to apply it. Conversations are more theoretical than tactical.
Level 2: Active
Early adopters begin experimenting—often through small-scale pilots or proof-of-concepts. Data scientists might explore models using Jupyter notebooks or public ML libraries. Success is sporadic, and infrastructure is limited.
Level 3: Operational
AI is embedded into specific business processes. There’s usually a dedicated team of ML engineers. Models are trained, deployed, and monitored. Infrastructure like MLOps, versioned data pipelines, and reproducibility frameworks become essential.
Level 4: Systemic
AI starts to reshape the way business operates. Teams no longer view it as a tool for “efficiency” but as a catalyst for business model innovation. Think recommendation engines, dynamic pricing, or automated underwriting.
Level 5: Transformational
AI is a core pillar of the business strategy. Products and services are built around AI. The organization competes on its ability to process information faster and more intelligently than others.
Netflix, Google, and Amazon are classic examples—but so are newer disruptors in manufacturing, fintech, and logistics who leverage AI as a competitive moat.
How to Start: Making AI Adoption Practical
Before implementing AI, leadership must ask:
- What decisions drive our business?
- What data do we collect (or can we collect)?
- What value will automation unlock?
Adopting AI isn’t a one-time event. It’s a strategic two-part cycle:
1. Explore Current Practices
Start with what’s already being done. Even repetitive tasks—like sorting customer inquiries or flagging anomalies—are ripe for automation.
Freeing human teams from routine decisions unlocks bandwidth for creativity, empathy, and innovation.
2. Envision New Decisions
Once the groundwork is laid, ask: What decisions aren’t we making yet?
Where can predictive models or intelligent agents guide the business in new directions? How can we think of daily operations as a series of data-driven decisions?
As of 2025, several leading organizations have developed AI maturity models to guide enterprises in assessing and advancing their AI capabilities. Here’s an overview of some of the most prominent models:
Five well known AI Maturity models
1. MIT Sloan's Four-Stage AI Maturity Model
Developed by the MIT Center for Information Systems Research (CISR), this model outlines four stages of AI maturity
- Stage 1: Experiment and Prepare
Organizations focus on educating their workforce, formulating AI policies, and experimenting with AI technologies to become more comfortable with automated decision-making - Stage 2: Build Pilots and Capabilities
Companies develop AI pilots that create value for both the enterprise and its workers, focusing on building capabilities and infrastructure - Stage 3: Scale and Transform
AI initiatives are scaled across the organization, transforming business processes and creating new value propositions. - Stage 4: Innovate and Lead
Organizations leverage AI to drive innovation, leading their industries with AI-powered products and services.
2. Accenture's AI Maturity Framework
Accenture’s framework assesses AI maturity across various dimensions, including strategy, data, talent, and governance. It provides a roadmap for organizations to advance and accelerate AI business transformation, highlighting the need for a strong foundation in data and analytics, a clear AI strategy aligned with business goals, and a culture that fosters innovation.
3. Boston Consulting Group's AI Maturity Matrix
BCG’s AI Maturity Matrix evaluates organizations based on their AI capabilities and the value derived from AI initiatives. It provides insights into how companies can progress from passive users of AI to pioneers that integrate AI into their core business strategies, emphasizing the importance of leadership commitment, data infrastructure, and cross-functional collaboration.
4. ServiceNow & Oxford Economics' Enterprise AI Maturity Index
This index, based on a survey of 4,470 executives, measures AI maturity across five key pillars: strategy, data, technology, talent, and governance. It offers a comprehensive view of how organizations are adopting AI, the challenges they face, and the best practices that lead to successful AI integration.
5. AIM Research & Hansa Cequity's Generative AI Maturity Framework
Focusing on generative AI, this framework assesses maturity across six strategic dimensions: strategic alignment, technology and infrastructure, talent and skills, data management, process integration, and governance and ethics. It provides a structured approach for organizations to evaluate and enhance their generative AI capabilities.
Each of these models offers unique insights into AI maturity, catering to different organizational needs and industry contexts. Organizations may choose the model that best aligns with their strategic objectives and operational realities.
Case Study: AI for Smarter Sales Teams
Imagine your sales team could automatically segment leads by buyer persona, purchasing behavior, or predicted lifetime value. AI can ingest demographic, behavioral, and historical data to cluster customers using algorithms like K-Means or Random Forests.
The result?
- Better targeting
- Higher conversion
- More productive reps
More importantly, the mental load of low-level decisions disappears, freeing sales teams to spend time where it matters—refining messaging, building relationships, and closing strategic deals.
This applies across departments—from customer support triage to dynamic inventory management to contract risk scoring.
AI Use Cases That Deliver
Here are just a few ways AI can directly enhance decision-making in your organization:
Ranking: Prioritize tasks, leads, or alerts based on contextual value
- Recommendation: Offer the next-best action, product, or service
- Anomaly Detection: Spot outliers in performance, security, or user behavior
- Segmentation: Tailor engagement based on user behavior, intent, or profile
- Forecasting: Predict outcomes, trends, and potential risks
Recommended Model: MIT Sloan’s Four-Stage AI Maturity Model (Enhanced for Decision Intelligence)
We suggest using an enhanced version of MIT Sloan’s model, rebranded and adapted for our positioning. It’s strategic, simple, and client-friendly—and can be extended to emphasize AI for decision-making.
Applied AI's AI Maturity Model – Focused on Decision Intelligence
| Stage | Theme | What the Customer Is Doing | How Applied AI Can Help |
|---|---|---|---|
| 1. Explore & Educate | Awareness | Experimenting with AI pilots, reading about trends, exploring GPT demos | Conduct workshops, perform decision audits, identify high-leverage AI decision zones |
| 2. Build & Experiment | Enablement | Running PoCs, building basic data pipelines, exploring use cases in silos | Deliver lightweight MVPs, provide data engineering & model-building services, validate business ROI |
| 3. Operationalize & Scale | Integration | Embedding AI into key workflows like sales, support, and operations | Set up AI Copilots, build MLOps infrastructure, drive automation of decisions across departments |
| 4. Transform & Lead | Differentiation | Making AI central to value proposition; AI becomes a competitive advantage | Co-create AI product roadmaps, offer managed AI services, and build long-term decision AI platforms |
Why This Works for Strategic Partnerships with our clients?
- Strategic Framing: Starts with vision and decision-making, not just model accuracy or tech
- Flexibility: Tailored to each customer’s current maturity
- Value-Based: Positions Applied AI as a business-value enabler, not a software vendor
- Scalability: Sets up long-term engagement: PoC → MVP → Integration → Co-innovation
Final Thought: The Shift to Information-Driven Strategy
AI isn’t a magic bullet. It’s a tool—and like any tool, its value depends on how thoughtfully it’s applied.
At Applied AI Consulting, we help businesses move beyond the hype to build sustainable AI practices that transform operations, enhance decision-making, and drive real value. Whether you’re at Level 1 or Level 4, the path forward is clear:
Think in terms of decisions, not models. Think in terms of value, not vanity.
If you’re ready to explore your AI maturity, let’s talk.
WhatsApp: +91 9923417213
sanju@appliedaiconsulting.com OR vijay@appliedaiconsulting.com
