AI Maturity Assessment Guide

AI (artificial intelligence) maturity refers to an organization’s ability to effectively build, support, and utilize AI technology and innovate in its field. It’s recommended that you assess your AI maturity before you start implementing AI technology and periodically thereafter, so you can identify gaps and determine where improvements are needed to progress to a higher level. Advancing your AI maturity will lead to better outcomes for your projects, including greater productivity and improved ROI.

This AI maturity assessment guide defines the maturity levels and provides a set of 25 self-assessment questions to help you get started.

AI maturity levels

AI maturity can be thought of as five levels that broadly define where an organization is in its AI journey and where there are gaps to be addressed.

5 AI maturity levels


Level 1: Exploration	You’re in the process of exploring AI, but you haven’t deployed it for any use cases yet. Individuals within the company may use public AI tools for certain tasks, but not as part of a broader AI deployment strategy.
Level 2: Incorporation	Your organization is initiating AI pilot projects or even using AI in production at a very limited scale. The focus is on gathering feedback from pilot projects and refining your approach to AI implementation.
Level 3: Proliferation	Your organization is starting to implement AI at scale to run production workloads. There are defined processes for AI implementation, and AI systems are integrated with some business processes and workflows.
Level 4: Optimization	AI has become a crucial part of your business strategy and operations. You have a well-defined AI strategy and have integrated AI across multiple business functions. You’re likely exploring emerging AI technologies to create new opportunities.
Level 5: Transformation	Your organization is utilizing AI as a transformative force that drives business innovation. Your AI capabilities, infrastructure, and processes are highly scalable, and your governance structure fully supports the transformative use of AI.

AI maturity assessment questions - and what the answers mean

Below are some examples of AI maturity assessment questions, broken out into five different categories. For each category, count the number of “Yes” answers to determine your readiness level.

Governance

This category focuses on evaluating the organization’s long-term AI strategy and its ability to clearly define what is required to achieve its goals.

AI governance maturity assessment questions:

Have you defined an organization-wide role and scope for AI governance?
Does your organization have documented policies and standards for AI pilot development and deployment?
Is there an organizational approach to delegating authority and defining accountability?
Do you have the tools needed to automatically comply with policies, principles, and standards?
Are there governance structures in place to enable the transformative use of AI?

What the answers mean:

Level 1: Exploration: You’re still working on developing an initial understanding of AI governance and have little to no governance mechanisms in place.
Level 2: Incorporation: You’ve defined organizational policies and standards for running pilots, and you’re starting to work on AI governance at scale.
Level 3: Proliferation: You’re working to ensure that AI implementation aligns with strategic goals, complies with ethical and privacy standards, and mitigates risks.
Level 4: Optimization: Your focus is on developing comprehensive guidelines and policies to address ethical considerations like bias, fairness, and transparency.
Level 5: Transformation: Your organization has a strong focus on responsible AI usage and risk management, and you’re innovating with AI while aligning with long-term goals and organizational values.

Data

This category focuses on evaluating the organization’s adoption of a data-centric culture that prioritizes seamless sharing and the leveraging of data to drive business insights.

AI data management maturity assessment questions:

Is your organization aware of the data requirements for developing and using AI applications?
Do you have a proven and repeatable process for preparing data for AI projects?
Do you have the standards and staffing required to manage AI data, ensure data quality, integrate data sources, and govern data usage?
Have you optimized data platforms for AI, machine learning, and data analytics with tools to enforce compliance with standards and regulations?
Does the entire organization understand how data can support AI innovation?

What the answers mean:

Level 1: Exploration: Your data teams are still learning about the data requirements for initial AI use cases
Level 2: Incorporation: Data teams are investigating data collection, storage, and processing practices to support AI pilot projects.
Level 3: Proliferation: Data teams are focused on building a robust data infrastructure to support AI in production.
Level 4: Optimization: Your organization fosters a data-driven culture where employees are encouraged to leverage data in decision-making.
Level 5: Transformation: Your entire organization understands data’s importance in the responsible use of AI and places data at the heart of strategic decision-making.

Skills

This category focuses on assessing whether the organization has employees with the necessary skills to build and use AI applications, data architectures, and computing infrastructure.

AI skills maturity assessment questions:

Is your organization aware of the skills required to build, use, and support AI applications?
Does your organization currently have the skills required to build or implement an AI proof of concept (PoC)?
Do you have enough skilled staff to develop, implement, and support AI projects in production?
Have you designated a team responsible for training employees on AI best practices and organizational standards?
Does your organization foster a culture that prioritizes AI innovation and transformation?

What the answers mean:

Level 1: Exploration: Your organization is still learning about what skills are required to build and support AI projects.
Level 2: Incorporation: Your organization is actively recruiting and training for the expertise needed to run pilots and build AI applications.
Level 3: Proliferation: You’re focused on upskilling internal talent and hiring for additional expertise to support broader AI operations in your organization.
Level 4: Proliferation: Your organization is investing in ongoing training programs to strengthen internal expertise and prepare for future developments.
Level 5: Transformation: Your organization’s culture prioritizes continuous skill development and the adoption of new AI technologies.

Processes

This category assesses whether the organization has the necessary tools and processes in place to develop and support AI applications

AI processes maturity assessment questions:

Is there organizational awareness of the core processes and tools needed to build and support AI projects?
Do you have a standard process for iteratively identifying, choosing, and piloting new AI use cases?
Does your organization have standard processes in place to scale, release, deploy, and support AI projects?
Do you have automated deployment, testing, governance, and support processes for the entire AI environment?
Is your organization leading in your industry by continuously innovating core AI processes?

What the answers mean:

Level 1: Exploration: You’re still developing processes to identify and select AI pilot use cases.
Level 2: Incorporation: You have processes in place for AI pilots, and you’re currently focused on developing processes and selecting tools to support AI projects in production.
Level 3: Proliferation: You’ve defined and standardized processes for AI deployment and support.
Level 4: Optimization: Your AI processes are agile and automated to enable rapid adjustments to workflows and strategies.
Level 5: Transformation: You’re continuously improving and reengineering your core AI processes, making you a leader in your industry.

Infrastructure

This final category determines whether the organization has the necessary technology infrastructure to support AI projects and achieve digital transformation.

AI processes maturity assessment questions:

Is your organization aware of the hardware and software infrastructure required to build and support AI applications?
Do you have the necessary infrastructure and tools to build pilot AI projects?
Do you have a standardized technology infrastructure to support building and running multiple AI applications in production?
Have you optimized your infrastructure for AI so that it can be deployed and scaled on demand?
Is your organization innovating with new approaches to building and running AI infrastructure?

What the answers mean:

Level 1: Exploration: Your organization is working to understand the infrastructure requirements to build AI pilot projects.
Level 2: Incorporation: You’re focused on building out the technology infrastructure to support AI pilot projects.
Level 3: Proliferation: You’re investing in a robust and scalable technology infrastructure to support AI applications in production.
Level 4: Optimization: You have a highly scalable infrastructure that can adapt to varying workloads and is optimized for performance.
Level 5: Transformation: Your organization is developing innovative new infrastructure solutions to enhance AI performance, scalability, and efficiency.

Advance your AI maturity roadmap with 10Pearls

Performing a self-assessment is a quick and inexpensive way to discover where your strengths and weaknesses are, but only if you know how to find the right answers and interpret your results.

Working with a trusted AI consulting firm like 10Pearls is the best way to get a comprehensive AI maturity assessment with a personalized roadmap for how to improve. Reach out today for a risk-free consultation to learn how we can help you advance to the next level.

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