AI Maturity Model: How to Assess and Scale

As AI grows quickly, business leaders now have to move from just trying it out to making it a key part of their strategy. But jumping into AI without a clear roadmap often leads to fragmented pilots, low ROI, and operational friction. That’s where AI maturity models come in.

An AI maturity model is a strategic framework that helps organizations assess their current capabilities, align leadership, workflows, and infrastructure, choose the right tools and partners for each stage, and track progress toward measurable ROI.

This guide breaks down how businesses at every level of AI maturity, from early adopters to innovation leaders, can use structured models to evolve faster, reduce risk, and stay competitive.

By analyzing the data that AI both produces and processes, businesses innovate faster and improve overall results in line with their strategic goals.

Companies of all sizes and industries will sit at different levels of the AI maturity spectrum. At one end, there are beginners who may be experimenting with AI image generators for their social media posts, on the other end, experienced power users with formal AI usage for strategies and possibly creating their own models.

Understanding where a business stands on this spectrum helps leaders prioritize efforts and focus resources to maximize AI’s impact.

What are the five stages of AI maturity model?

The goal of the generative AI maturity model is to help businesses measure their current level of preparedness and usage of AI within their organizations. Here are five stages that organizations typically progress through: 

1. Ad hoc/aware/foundational 

The first stage of AI maturity is awareness and occasional usage of AI technology. There is no formal plan forAI to be integrated into company operations, and most employees don’t use these tools. At the foundational level, the seeds of future usage are planted, but there is little to no testing or documenting of experimentation.

2. Developing/approaching

When a business is ready to develop a more formal AI plan and is using the technology for more regular projects, it moves into the second maturity level. This typically looks like simple automations for routine work tasks, focusing on internal projects only. 

At this stage, businesses are unlikely to use AI for client-facing work.

3. Mature 

Having a formal AI usage strategy and rolling it out across numerous projects at once is considered the third, or mature, level of AI maturity. The technology being used has been thoroughly tested within the organization, and teams are confidently using it to carry out both internal and client-facing tasks.

4. Leading 

Once the adoption of AI has become company-wide, the business is at the leading level of AI maturity. This is seen as a competitive advantage, as innovation is now possible from this point on to truly customize AI models in accordance with the organization. 

5. Transformative 

This final level reshapes the company through deeply embedded AI processes. Few businesses achieve this stage, most remaining at the leading level. Here, AI transforms the products, services, or processes that the business offers.

Why does AI maturity matter?

Where a business falls on the AI maturity framework directly impacts the company’s ability to use AI technology in strategic and meaningful ways. Not only does it help the current team with their daily workload, but AI can also be used to effectively grow a business and create a tangible competitive advantage that results in more business.

Greater efficiency 

As businesses climb the AI maturity ladder, they can automate more tasks, even the most complex. Workflows can be more effectively optimized, making the whole organization more efficient. This can often lead to increased team productivity, reduced operational costs, and even greater revenue.

Informed decision making 

Having access to increased levels of data through the AI maturity process means that businesses can act in real time more strategically. Timely and accurate decision-making is essential for staying ahead of competitors, and using AI throughout the business can result in this.

Enhanced customer experience 

Using AI in customer-facing capacities can transform their experience with a business. Whether it’s improving the speed of customer service responses or providing a more personalized interaction, building AI technology into these aspects of a company often increases customer satisfaction and retention.

Possibilities for innovation 

For the most experienced AI users, being at the top levels of the AI maturity model can lead to new products and services that wouldn’t be possible without this technology. Comfortability with AI can also allow companies to experiment with building their own custom algorithms that are designed specifically around their business needs.

How to assess AI maturity: 3 frameworks

AI maturity is about the depth of AI adoption, internal alignment, infrastructure readiness, and your ability to scale AI across business functions. To avoid vague or subjective evaluations, most successful organizations rely on structured frameworks to self-assess and plan their AI roadmap.

1. Deloitte’s AI Maturity Framework

Deloitte’s model segments organizations into distinct maturity levels, each reflecting increased structure, strategy, and value realization from AI programs.

• Starters: Conduct isolated AI projects without organizational coordination or strategic alignment.
• Pathseekers: Deploy several AI initiatives, often with cross-functional participation, but value impact remains mixed.
• Transformers: Scale AI with strong ROI, embedded governance, and formal AI Centers of Excellence.
• High‑outcome organizations: Achieve enterprise-wide value from AI, including new markets and innovation-led revenue.

Deloitte’s model creates a clear progression from experimentation to strategic AI integration, emphasizing cross-functional execution and formal governance.

Organizations should assess their current classification (starters, pathseekers, transformers) and focus on building the missing capabilities, especially strategy alignment and ROI tracking. Becoming a transformer means moving beyond pilots to embed AI as a key driver of business outcome and innovation.

2. McKinsey’s AI Readiness Index

McKinsey’s AI Readiness Index evaluates organizational preparedness based on five critical dimensions: strategy, data, technology, organization, and capabilities.

• Strategy: Does the organization have a clear AI vision and business-aligned roadmap?
• Data: Are data sources unified, clean, and ready for training AI systems
• Technology: Is there scalable infrastructure and tooling to support AI in production?
• Organization: Are teams structured for cross-functional AI collaboration and decision-making?
• Capabilities: Does the organization possess or can it develop the necessary AI skills?

McKinsey’s framework highlights that readiness is multidimensional. Firms may be strong in data and tech but weak in strategy or skills. A comprehensive assessment across all five levers helps pinpoint where to invest for real scalability. Organizations can then move beyond pilots, ensuring each dimension reaches a baseline before scaling AI initiatives.

3. PwC AI Maturity Diagnostic Tool

PwC’s tool assesses how well organizations embed AI into leadership, trust, business processes, technology, and outcomes across five maturity levels.

• Leadership and vision: Senior leaders explicitly sponsor AI initiatives and integrate them into strategy.
• Business adoption: AI is used across operations, with multiple use cases showing measurable business impact.
• Trust and ethics: Responsible AI frameworks, including bias detection and data privacy, are consistently in place.
• Tech and data: Robust infrastructure supports high-quality data access, ML pipelines, and scalable deployments.
• Performance management: Outcomes are systematically monitored, tied to KPIs, and inform continuous improvement.

PwC’s diagnostic combines organizational, technical, and ethical elements into a single framework, ideal for regulated industries. It emphasizes that maturity means embedding trusted, measurable, and repeatable practices. Organizations advancing through the levels should focus not just on tech adoption but also on leadership commitment and responsible AI governance.

How to choose the right tools and partners based on your AI maturity model

As organizations go from experimentation to operational AI, the types of tools, partnerships, and team structures they need change dramatically. Each stage requires a unique approach to building trust in the technology, scaling its use, and ensuring long-term business alignment.

Below is a breakdown of tooling and strategy aligned to each level of the maturity curve.

1. Which partners and tools should you choose for an ad hoc AI maturity level?

At the foundational stage, the goal is simple experimentation and internal awareness. Organizations typically benefit from lightweight, low-risk tools that allow non-technical teams to experiment without major infrastructure commitments.

Low-code platforms are ideal for early prototyping. Teams may also explore GPT-based integrations through Zapier AI, Slack GPT, or Notion AI for everyday productivity boosts like summarization, drafting, and workflow triggering.

AutoML tools such as Amazon SageMaker Autopilot or Google Cloud AutoML also become attractive here, letting small teams explore ML modeling with minimal expertise.

At this point, enterprises should avoid heavyweight consulting engagements and instead seek onboarding-focused vendor teams or open-source communities.

Organizations like Hugging Face and OpenAI often provide educational ramp-up resources suitable for this phase. Team structure is informal. AI champions typically emerge from IT, marketing, or operations, experimenting organically.

There’s no centralized governance or documented AI playbook at this level, and that’s expected. The focus is to build comfort, document learning, and prepare the groundwork for a more coordinated approach in the next stage.

2. Which partners and tools should you choose for a developing AI maturity level?

In the developing stage, the organization is piloting operational AI use cases and proving internal ROI through repeatable automations.

Tools now shift toward ML pipeline builders, which offer drag-and-drop model design with some level of governance. Teams may also explore intelligence layers for apps via APIs from AssemblyAI, Clarifai, or AWS Rekognition, adding speech, vision, or NLP capabilities to internal systems.

The data stack begins to matter more here. Tools like Snowflake, dbt, and Fivetran help unify structured and semi-structured data to improve model performance.

Partner strategy should evolve too: rather than generalist consultants, firms should look to niche experts who understand AI within specific domains like HR tech, manufacturing, or logistics. Proof-of-concept-driven partnerships work best, especially those that commit to defined success metrics and timelines.

Team structures expand into small task forces made up of functional leaders, data analysts, and IT stakeholders. Organizations begin drafting AI adoption playbooks and light governance guidelines that introduce standards without restricting early innovation.

3. Which partners and tools should you choose for a mature AI maturity level?

At the mature stage, the focus turns to scale and systematization. AI is now connected to real business KPIs like customer retention, margin improvement, or throughput. So, tooling must support repeatable, auditable, and observable AI deployments.

Teams adopt full ML lifecycle platforms like MLflow, Weights & Biases, or Databricks to track experiments, manage model versions, and automate deployment.

For production-grade models, MLOps platforms like Seldon, Arize AI, Kubeflow, or Tecton are key to managing reliability, drift, and monitoring. Companies may also start deploying open-source LLMs like LLaMA 3, Mistral, or Command R+ internally for privacy-sensitive tasks.

At this point, strategic partnerships shift to enterprise AI platform vendors like DataRobot or H2O.ai who can support broader scaling needs.

Team structure becomes more formalized, with dedicated AI product managers, data engineers, and a centralized AI Council that ensures alignment between business and technical teams. Training programs are rolled out to functional teams across the business to ensure consistent adoption.

4. Which partners and tools should you choose for a leading AI maturity level?

At this stage, AI has become a core driver of business process innovation.

Real-time systems come into play, and tooling must support performance at scale. Feature stores like Tecton or Feast allow real-time model input tracking, while observability tools like Fiddler and WhyLabs help ensure model integrity through bias detection and drift monitoring.

Organizations working with low-sample or high-sensitivity datasets also adopt synthetic data platforms such as Mostly AI or Gretel.ai to mitigate bias and protect privacy.

Partner relationships evolve into co-innovation models. Businesses may collaborate with Microsoft Applied AI Services or NVIDIA’s Inception program to accelerate custom model development and experimentation. These partnerships should include clear SLAs, especially around model uptime and governance.

Team structure reflects enterprise-level maturity: AI teams are embedded into business units like finance, operations, or product. Internal accelerators launch new AI use cases rapidly, and compliance teams are trained to audit models for ethical, legal, and regulatory adherence.

5. Which partners and tools should you choose for a transformative AI maturity level?

At the transformative level, AI is a business model enabler. Companies develop proprietary IP through AI, influence product design with predictive systems, and may even license models or data.

Tools include custom LLM stacks fine-tuned on private data lakes, often using models like LLaMA-3, Claude, or GPT-4o. Simulators and digital twin platforms such as NVIDIA Omniverse, Ansys, or Altair AI are deployed to train models in complex, real-world scenarios.

Adaptive learning systems and reinforcement learning platforms also emerge to allow models to evolve autonomously. Partnering here means forming strategic alliances with hyperscalers like AWS, Azure, or GCP to co-develop AI assets. Some companies even invest in university partnerships or create internal AI research labs.

Org design is AI-native at this point. AI is woven into R&D, GTM, Legal, and Ops. Proprietary datasets and ML/LLM pipelines are maintained in-house. The board and C-suite track AI’s business impact with the same rigor as revenue and risk.

Industries that benefit from AI maturity models 

For companies that choose to work within the AI maturity model, a distinct competitive advantage can be quickly established. As a result, many industries are beginning to adopt these models, including:

• Manufacturing: Reducing machinery downtime and increasing production outputs is necessary for growth in the manufacturing industry. AI technology can be used for automation that reduces operational costs and provides more accurate quality control measures.
• Retail: Both online and in-store retail businesses can benefit from AI technology. Real-time customer analysis and personalized recommendations can increase sales, while also providing demand forecasting and inventory assessments for more strategic planning. 
• Energy and utilities: Monitoring energy consumption and planning for necessary maintenance are two of the biggest benefits of AI. Not only do these tools save companies money, but they also help the end consumer enhance grid reliability and lower energy or water waste.
• Insurance: Fraud is a significant problem in this industry, and AI is a good solution for detecting it. Object detection and image classification tools can quickly spot fraudulent or tampered-with documents, while other models can help automate the claims process to improve customer satisfaction.
• Real estate: From market analysis to property valuation, AI can support the work of real estate agents and property managers to provide a better experience for their clients. 
• Education: For teachers, automating routine tasks and assessing student performance can result in better engagement and improved academic outcomes. In the office, administrators can use AI to reduce any operational efficiencies that may be occurring.

Although there are many benefits to using AI, some industries still fall behind. Technology businesses, naturally, are industry leaders when it comes to the use of AI, but the automotive, aerospace and defense, and public services industries have all seen significant AI usage increases in the last three years.

Traditional industries like finance and healthcare continue to lag behind in both AI maturity and adoption. This is likely due to both legal and compliance challenges, along with the lack of trained employees to use this technology.

How do you assess the current level of AI maturity?

To determine where a company stands on the AI maturity model, leaders need to evaluate four key areas:

• Technical infrastructure: Businesses must have the IT infrastructure to support AI. Network capacity and storage levels are critical pieces of the puzzle, along with computational power and performance. Knowing where upgrades are needed and having the time and money to invest in this will play a crucial role in determining a company’s AI maturity level.
• Team: In-house employees must be willing to adapt to new technology if a business is going to move up the maturity levels. Training should be provided, while a significant financial investment for an organization, but necessary for ongoing AI adoption.
• Governance: Particularly for regulated industries, any AI tools being used must be compliant. New internal policies will likely need to be developed to accommodate this technology, and ongoing reviews with legal and regulatory boards must be possible to ensure that sensitive information is kept private.
• Strategy: Especially at the early stages of adoption, businesses will likely have no strategy in place for using AI company-wide. But, as assessments are made, particularly for companies at the higher levels, any formal strategy should be reviewed to determine what’s working well and where improvement can be made.

How do you achieve greater AI maturity?

With the willingness to experiment and money to invest in new technology, any business can move up the AI maturity model levels. No matter where the company is, a thorough strategic review of current AI usage and preparedness should be the starting point. From there, the focus should be on planning and executing a new strategy.

Building an AI maturity roadmap 

Look for areas where improvements can be made in the organization's current AI usage and strategically determine the next steps. It’s important to be realistic here, both in terms of time and budget. Set targets and timelines for integrating new AI processes into the business and outline how success will be measured.

Implement the strategy 

Creating the infrastructure for extensive machine learning (ML) takes time. But with a firm strategy in place, it’s possible to roll out these changes effectively and with full team support. Having a plan for not only what AI technology will be used but also how it will become part of the day-to-day workflow will help team members who aren’t as confident in using AI adapt to these updates.

Track and make changes 

Progress must be measured to determine whether the new strategy is a success. Outline the benchmarks in the strategy documentation, making it clear for leadership to see improvements or areas that need adjustments.

Turn maturity into competitive advantage

Businesses that approach AI as a long-term operating system consistently outperform their peers. AI maturity models provide the structure to grow with intention, evaluate risk, and scale capabilities in a measurable way.

The difference between AI leaders and laggards isn’t access to technology. It’s strategic alignment, process readiness, and the ability to invest in the right tools at the right time. Use the frameworks in this article to audit where you're today, refind your AI roadmap, evaluate partnerships with clarity, and drive measurable value from your next AI investment. 

Discover how AI can be leveraged in the real world in a range of different industries before diving in with AI technology in your own business.