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The question is no longer “Is your company ready for AI?” – in all likelihood, your employees are already experimenting with AI tools in their daily work. According to a recent Work Trend Index, 75% of employees are using AI at work in some capacity. Likewise, in the survey by Devoteam, 59% of respondents reported incorporating generative AI assistants into daily workflows. The real question is whether your company is prepared to succeed with AI.
Two concepts, AI readiness and AI maturity, can help ensure your organisation can innovate and grow with AI. Once you have a clear picture of your organisation’s AI readiness, the next step is to channel that insight into a concrete action plan. For a comprehensive guide on building a robust strategy from the ground up, download our AI Strategy Playbook.
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In this article, Let’s break down what AI readiness and maturity mean, how they’re assessed, and how they directly impact business success.
AI Readiness vs. AI Maturity
There is a lot of confusion around AI maturity and AI readiness assessments. Sometimes the terms are used interchangeably, but they actually refer to different (though related) aspects of an organisation’s AI journey.
Let’s clarify these two concepts and explore their relationship.
What is AI Maturity?
AI maturity describes an organisation’s progress on the AI adoption journey – essentially, how far along you are in leveraging AI. An AI maturity assessment evaluates the current state of AI implementation against an advancement scale or model. This typically involves looking at multiple dimensions of capability (strategy, data, technology, talent, etc.) and the breadth and depth of AI use cases deployed.
Importantly, AI maturity is measured relative to where the organisation aspires to be. Even companies just starting with AI can be assessed for maturity, as it’s more about the position on the path than simply time or size.
In practice, an AI maturity assessment clearly shows “where we are today” in our AI journey. It identifies the level of advancement in key domains like strategy, operations, data infrastructure, technical skills, human capital, and even financial planning. The outcome is often expressed as a stage or level of maturity on a defined scale. Let’s look closer at what a maturity scale looks like.
AI Maturity Framework – Stages of Maturity
Most AI maturity models define stages or levels that organisations progress through as they integrate AI. Let’s expand on a typical multi-stage AI maturity framework, with real-world examples at each stage to illustrate:
Stage 1: No Strategy (Ad Hoc)
At this initial stage, the organisation has no formal AI strategy or processes in place. Any AI usage is minimal. Data is likely siloed in individual applications with little sharing or analytics. This lack of strategy and governance is risky. The absence of an AI/data strategy is cited as a top reason AI projects fail.
Example: A small local business might only use basic automation (like an email spam filter) and have no dedicated data scientist. Employees might dabble with tools like ChatGPT on their own, but there’s no management awareness, strategy or plan.
Stage 2: Exploring
Organisations at this stage have developed an interest in AI and begun exploring possibilities but still lack a cohesive plan or strategy. There may be some prototypes or pilots, driven by enthusiastic individuals or teams, but efforts are disconnected. Data remains tightly coupled to specific apps or departments, without enterprise sharing (e.g. each team hoards its own spreadsheets). This “toe in the water” phase shows openness to AI, but not much coordination yet.
Example: A mid-size company’s IT team might experiment with a chatbot for customer service on the side, and some business analysts use machine learning in isolated cases, but leadership hasn’t defined an AI roadmap.
Stage 3: Experimenting
At this point, the company recognises AI’s potential and organises stakeholders to conduct pilots and proof-of-concepts. An initial AI strategy or vision begins taking shape. The organisation starts allocating resources to AI experiments and establishing cross-functional teams to evaluate use cases. They set some preliminary success metrics to assess value. This stage is about learning and proving value: testing AI in the real world, gathering ROI data, and getting early wins. According to MIT CISR research, roughly one-third of companies are at the pilot stage, focusing on moving from isolated experiments to more systematic innovation.
Example: A regional bank launching a pilot using AI for fraud detection, assembling data engineers and risk managers to test the solution on a small scale.
Stage 4: Formalising
In this stage, the organisation has demonstrated clear ROI from AI projects and is now formalising and standardising its approach. AI projects start moving from pilot to production. The company develops standard tools, templates, or methods for AI development and deployment. An AI use-case portfolio or roadmap is defined, and governance begins to tighten. There is an effort to codify best practices, ensure data pipelines are in place, and plan broader rollouts. At this level, AI is still not ubiquitous, but it’s no longer ad hoc. The company has repeatable processes for building AI solutions and has proven they can impact business outcomes.
Example: An e-commerce firm might have successfully used AI to personalise marketing and can show a lift in sales from those efforts. Now, they have established an internal AI Center of Excellence to create guidelines for all new AI initiatives.
Stage 5: Optimising
By Stage 5, the organisation has built well-defined, automated pipelines for developing, deploying, evaluating and maintaining AI agents at scale. AI isn’t just a series of projects, it’s becoming an integral part of business operations. Data infrastructure is likely modernised (e.g. cloud data lakes, real-time data feeds) to support company-wide AI needs. The company offers self-service AI tools or platforms to its teams. A well-structured AI product/catalogue exists, and many processes are enhanced by AI. At this stage, AI capabilities are enterprise-wide. One hallmark of Stage 5 is that new AI solutions can be deployed faster and more reliably because the organisation’s AI “machinery” is in place (platforms, pipelines, governance, skills).
Example: A global financial firm uses a company-wide AI assistant to help employees analyse documents, automate tasks, and support customers. With standardised processes for managing and improving the AI, they can quickly deploy new AI solutions across all departments.
Stage 6: Transforming
In the highest maturity stage, AI is core to the business’s strategy and competitive advantage. The enterprise is truly AI-driven. AI systems are deeply embedded in every facet of decision-making and operations, often in real-time. The organisation likely continuously refines its AI strategy as the market and technology evolve, practising a culture of data-driven innovation. At this level, companies often develop proprietary AI models and may even offer AI-based products or services externally. In a recent MIT study, only ~7% of firms reached this “AI Future-Ready” tier. At Stage 6, AI is not just an enabler but a strategic differentiator; the business might even be redefining its industry through AI innovation. This “transformative” stage is the pinnacle where the enterprise is all-in on AI, much like how leading firms in the past became all-in on the internet or cloud.
Example: A professional services firm uses advanced AI agents for key tasks like market analysis, forecasting, and client interaction. These agents, powered by specialised language models, work together to find opportunities, predict risks, and suggest strategies, giving the firm a competitive edge and driving innovation. Such companies continually push the frontier of what AI can do, and AI gives them a sustainable competitive edge.
It’s worth noting that different frameworks may label or count stages differently (some use four or five stages, others six or more). But the progression is broadly similar – from no AI, to experimenting, to implementing, to integrating, to truly transforming the business with AI. The key is that AI maturity is a journey of cumulative capability-building. As one expert put it, organisations must build up capabilities and lessons at each stage as they move toward being “future-ready” for AI.
What is AI Readiness?
If AI maturity is about how far you’ve come, AI readiness is about how prepared you are to move forward. AI readiness refers to an organisation’s preparedness to effectively adopt, integrate, and derive value from AI. It’s essentially an operational check: do we have the necessary ingredients in place to start (or scale up) AI initiatives? While maturity looks at the current state and progress, readiness focuses on the prerequisites for successful AI projects.
An AI readiness assessment typically results in a gap analysis – highlighting areas that need improvement before embarking on ambitious AI plans. Think of it like evaluating if you’re “ready to launch.” It examines everything from data quality, to infrastructure, to skills and culture, to identify what might hinder AI adoption. Even a very AI-mature company would still do readiness checks when pursuing new AI capabilities.
In summary, AI readiness is about having the groundwork laid for AI success. It doesn’t necessarily mean you’ve implemented AI broadly yet, but that you are ready to do so. This involves ensuring the right technology, data, people, and governance are in place. A readiness assessment is often done before or at the start of an AI project or program, to make sure any gaps are addressed early. As a result, improving AI readiness accelerates and de-risks the journey toward higher AI maturity.
AI Readiness Assessment – Key Elements
Conducting an AI readiness assessment means evaluating your organisation across a number of critical dimensions::
- Strategic Vision & Alignment: Is there a clear business strategy for AI? This checks if leadership has defined how AI will drive business goals and a roadmap to get there. Aligning AI initiatives with a concrete strategic vision ensures AI projects target real business value rather than chasing hype.
- AI Governance & Risk Management: Have you established the policies, oversight, and ethical guidelines for AI use? AI governance means having committees or processes to review AI projects, manage risks, and ensure compliance with regulations and ethical standards. Governance readiness is crucial, especially with emerging regulations.
- Data Foundation: Is your data in good shape for AI? This involves data quality, availability, integration, and governance. AI is data-hungry; without reliable, well-structured data, even the best algorithms will fail. A readiness assessment looks at whether you have the data (and data infrastructure) needed for AI – for instance, are data sources integrated or siloed? Are there large gaps or biases in the data? Poor data readiness, on the other hand, has sunk many AI projects.
- Technology Infrastructure: Do you have the required hardware, software platforms, and tools to support AI initiatives? This could mean cloud computing resources, scalable storage/processing for big data, and deployment platforms for machine learning models. If a company’s IT environment is outdated, an assessment will flag that – perhaps you need to invest in a cloud data warehouse or specialised AI platforms before proceeding. Secure, scalable infrastructure is a must for AI readiness.
- Talent and Skills: Does your organisation have (or have access to) people with the right AI skills? AI readiness means having data scientists, ML engineers, or AI-savvy domain experts – or plans to acquire or train them. It’s not just about a couple of data scientists in IT; consider whether your broader workforce has basic AI literacy and if you have a plan to fill skill gaps. For example, if you’re aiming to deploy AI at scale but have no machine learning engineers on staff, you’re not ready – you might need to hire or upskill teams, or partner with experts.
- Culture and Change Management: How adaptable is your organisation’s culture to embracing AI-driven change? Readiness isn’t only technical – it’s also about mindset. A culture of innovation, willingness to experiment, and employee buy-in for working with AI are important. If your culture is very siloed or risk-averse, that’s a readiness gap. Successful AI adoption often requires change management, training programs, and clear communication to alleviate employee concerns.
- Budget and Financial Readiness: While not always highlighted, a practical element is ensuring you have allocated budget for AI initiatives and a realistic financial plan. AI projects can require upfront investment with payback over time. Being financially ready might mean setting aside an AI innovation fund or securing executive sponsorship for a multi-year AI roadmap.
- Ethical and Responsible AI Considerations: Are you prepared to build AI responsibly? Readiness includes awareness of potential biases, fairness and transparency requirements, and having processes to audit AI decisions. Many organisations now include an ethics checkpoint in readiness assessments to avoid reputational and legal pitfalls down the line.
AI Readiness – Industry Consideration
Not every company will score high on all these dimensions initially, and that’s normal. An AI readiness assessment aims to find the gaps so you can address them. At Devoteam, we recognise that your maturity or readiness can depend on your industry. Different industries have unique challenges and priorities for AI readiness:
- In highly regulated sectors like healthcare or finance, governance, compliance, and ethical AI readiness take on heightened importance. A hospital network must ensure strict patient data privacy and validate algorithms for bias (e.g. in diagnostic AI) before it can responsibly deploy them. Regulatory readiness is key. These industries may move slower with AI not due to lack of interest, but because they need to shore up these safeguards first.
- In data-rich industries like retail or social media, data foundation and infrastructure are often the limiting factors. A retailer assessing AI readiness might find that integrating its online, in-store, and third-party data is the big hurdle to unlocking AI value. The industry is less regulated, but the technical readiness and data strategy determine success.
- In manufacturing, readiness might heavily depend on IoT and real-time data pipeline capabilities. If sensors on the factory floor aren’t in place or feeding data to a central system, AI can’t effectively do predictive analytics. So, the readiness assessment would focus on operational tech integration and training plant managers to trust AI insights for maintenance scheduling.
- The public sector often scores low on AI readiness; governments tend to have legacy systems, limited budgets, and risk-averse culture. This is slowly changing, but a government agency’s readiness assessment may reveal major needs in modernising IT infrastructure and workforce skills before AI can be scaled. On the flip side, public agencies put a strong emphasis on ethics and fairness from the start, given their mandate.
The key takeaway is that AI readiness is multidimensional and context-dependent. A thorough readiness assessment helps tailor your AI strategy to your current reality – ensuring you address critical gaps first.
The Relationship Between Readiness and Maturity
How do AI readiness and AI maturity relate to each other? In simple terms, readiness is about being prepared to climb the ladder, while maturity is about how high you’ve climbed. They are distinct concepts, but very much intertwined:
Readiness enables maturity
You can’t reach the higher stages of AI maturity without being ready at each step. Think of AI readiness elements (strategy, data, skills, etc.) as the building blocks that allow an organisation to advance to more mature levels of AI use. For instance, if you don’t have a solid data foundation and governance (readiness), you’ll stall out in the early experimentation stage of maturity because projects will fail or hit roadblocks. As a Berkeley study on government AI puts it, an agency’s AI readiness provides the necessary infrastructure to progress in AI maturity.
Maturity provides direction for improving readiness
An AI maturity assessment often reveals what you will need to get to the next level – which points back to readiness gaps. For example, realising you are at “Stage 2: experimenting” in maturity might highlight that to reach “Stage 3” you need better infrastructure and more data integration (readiness improvements). In practice, organisations often combine maturity and readiness assessments to get a full picture. The maturity assessment shows where you stand and where you want to go; the readiness assessment shows what’s missing to get there.
Sequential but overlapping
Typically, you address readiness early (ensuring you have prerequisites before major AI projects). As you implement and gain maturity, new needs emerge, prompting fresh readiness evaluations. It’s an ongoing cycle: achieve a level of maturity, identify new gaps to prepare for the next, and so on. Even very AI-mature companies continuously check readiness when adopting new AI technologies (for example, a company might be mature with traditional AI but not ready for adopting the latest generative AI models without upgrading certain tools or policies).
In practice, when we talk about an “AI Maturity and Readiness Assessment”, it usually means examining both: assessing the current maturity level and assessing readiness factors to execute an AI roadmap. The two assessments together answer: Where are we now? How does that compare to peers or to our goal? (maturity) and What must we fix or invest in to successfully move forward? (readiness). In short, AI readiness and AI maturity go hand in hand – readiness is about preparing the soil, and maturity is the growth you cultivate from it. Both are critical to ensure that as you adopt AI, you do so effectively and sustainably. Skipping either one – e.g. deploying AI without readiness, or planning in isolation without measuring progress – can lead to problems. Companies that balance both see the best outcomes.
Business Impact of AI Readiness and Maturity
How do strong AI readiness and high AI maturity affect real business results? In a word: significantly. Organisations focusing on building AI readiness and growing their AI maturity achieve significant benefits, including better financial performance and quicker innovation.
Let’s explore some direct impacts and why upping your AI readiness/maturity pays off:
- Higher Success Rates and ROI: Companies that systematically assess and improve readiness have far greater success with AI deployments, thanks to strategic aligemet, resource optimisation and clear roadmap. Moreover, prioritising readiness evaluation allows enterprises to make informed AI investments, leading to improved returns.
- Improved Financial Performance & Competitive Advantage: Advancing in AI maturity isn’t just a tech vanity metric. According to MIT researchers, enterprises in the later stages of AI maturity financially outperform their industry averages, whereas those in the early stages underperform their peers. AI maturity correlates with competitive advantage. Those who lag in readiness and maturity risk falling behind competitors who leverage AI more effectively.
- Strategic Alignment and Focus: Going through AI readiness and maturity assessments helps ensure that AI initiatives align with business strategy. Rather than random acts of AI, you develop a clear roadmap tied to your goals. This alignment means resources are invested in AI projects that matter most, increasing the impact. It prevents waste on “shiny tools”. We look deeper into that trap and how to avoid it in this article.
- Resource Optimisation: By identifying gaps and needs upfront and understanding your maturity stage, you can prioritise investments more effectively. Businesses can allocate budget and talent to the most critical areas. It’s much more cost-effective to fix foundational issues early than to deal with failed implementations later.
- Faster Innovation and Scalability: An organisation well-prepared for AI can innovate faster. High readiness means less friction to try new AI ideas – the data is there, the tools are available, the team is capable. This allows for rapid prototyping and scaling of successful pilots. This agility is a huge competitive asset in fast-moving markets. Additionally, when an AI initiative works, an AI-mature organisation can scale it enterprise-wide much easier (because governance, infrastructure, etc. are already in place from prior efforts).
- Better Risk Management and Fewer Costly Failures: AI readiness inherently includes identifying and mitigating risks – whether data privacy, ethical pitfalls, or operational risks. By doing this proactively, businesses avoid surprises after deployment.
- Talent Empowerment and Cultural Benefits: Going through the process of improving AI readiness often involves upskilling staff, hiring new talent, and fostering a more data-driven culture. A culture that embraces AI and continuous learning can become a magnet for innovative thinkers. Internally, teams become more adept at using data and analytics in their day-to-day decisions, not just in designated “AI projects.” Over time, this cultivates a culture of innovation and agility. Additionally, involving employees in readiness assessments makes AI adoption more inclusive, increasing acceptance of the changes.
- Clear Roadmaps and Organisational Alignment: Finally, the outputs of maturity/readiness evaluations give leadership and teams a clear roadmap for AI adoption. It turns obscure goals (“we should do AI”) into a concrete plan (“we are at maturity Level 2, to reach Level 3 in two years we need to invest in X data platform, hire Y experts, and implement these 3 pilot projects…”). When everyone understands the plan and their role in it, execution improves. It also becomes easier to communicate progress and wins. In essence, it aligns the organisation on the AI journey.
By leveraging AI readiness and maturity frameworks, businesses gain valuable insights and guidance that translate into tangible outcomes: higher ROI, competitive edge, aligned strategy, efficient operations, and mitigated risks. Ensuring your organisation is AI-ready and steadily maturing in capability will help you think big and execute, leading to sustainable success in the AI era.
How to Measure and Improve AI Maturity and Readiness
Measuring AI Maturity and Readiness
So, how do organisations actually measure their AI maturity and readiness? And once you have an assessment, what do you do next?
Typically, measuring AI maturity or readiness involves a mix of surveys, interviews, workshops, and documentation reviews. Many companies start with a structured questionnaire or scorecard that rates the organisation across various dimensions (as we discussed). These might be self-assessments or facilitated by an external expert. For example, answering questions like “Do we have an AI strategy endorsed by leadership?”, “What percentage of our workflows are augmented by AI?”, or “How centralised and clean is our data?”. Some use a point system or levels for each dimension, which aggregate into an overall maturity stage. Additionally, interviews with key stakeholders can provide qualitative insights (e.g. interviewing department heads about AI usage and challenges). Workshops can help calibrate scores and get consensus on where the organisation stands. In many cases, companies also benchmark against industry peers, either through published frameworks or consulting services, to see how they compare.
Devoteam recognises that AI maturity and readiness are not one-size-fits-all concepts. That’s why our assessment depends on the industry, the organisation’s AI goals, and even the company’s current tech infrastructure. As partners of the leading cloud providers, we offer both a general AI maturity/readiness assessment and a partner-specific assessment. Depending on your chosen provider, you can contact our Azure, AWS, or Google Cloud experts.
If you are interested in Google Cloud-specific AI Readiness, you can also explore our self-assessment tool Aira.
Self-Assessment vs External Assessment
While self-assessment is a good starting point, many companies find value in bringing in an external expert to conduct a thorough assessment. The investment in an expert assessment can pay off by accelerating your AI program with the right roadmap.
Bringing in an external company offers several key advantages:
- Objective and Impartial View: Avoids internal biases and provides a truly independent assessment. Offers a fresh perspective, highlighting areas internal teams might miss.
- Specialised Expertise: Brings in deep AI knowledge and experience across various industries. Utilises proven methodologies for accurate evaluation. Provides valuable industry benchmarking.
- Comprehensive Insights: Offers a holistic evaluation covering strategy, data, technology, talent, and culture. Delivers actionable recommendations and a clear roadmap. Helps mitigate potential risks and challenges.
- Efficiency and Time Saving: Accelerates the assessment process, saving valuable time and resources. Reduces the burden on internal teams, allowing them to focus on core tasks.
Improving Readiness and Maturity
Once the assessment is done, the next step is to act on it. In practical terms, companies will develop an AI improvement roadmap.
- Identifying Quick Wins: Look at the assessment results and find if there are any “low-hanging fruit.” Quick wins help build confidence and justify further investment.
- Closing Key Gaps: Prioritise the major readiness gaps that are holding you back. If lack of data integration is a big issue, make it a priority project to implement a data lake or integrate systems over the next year. The idea is to address foundational issues early.
- Scaling and Broadening: As readiness improves, you can expand AI efforts. For instance, after solidifying your data infrastructure and doing a successful pilot, you might scale that AI solution to all business units.
- Continuous Reassessment: AI technology and business environments are continually evolving. Leading organisations make AI maturity and readiness assessment a regular checkup, keeping them aware of new gaps that emerge. AI maturity is a moving target—continuous improvement is part of the journey.
- Leverage Partners and Tools: To accelerate progress, many organisations partner with consultants or vendors (for example, using a cloud provider’s AI tools can instantly give you world-class infrastructure without building it all in-house). There are also pre-built solutions for common needs (like data catalogue tools to improve data readiness, or MLOps platforms to help with deployment maturity). Don’t reinvent the wheel if a partnership or product can speed up your capability.
AI Readiness and Maturity Assessment with Devoteam
Devoteam has extensive experience with conducting AI readiness and maturity assessments for companies across different industries. Here is what our experts bring to the table:
- Trusted AI Consulting Expertise: With over 1,000 AI consultants and a track record of 300+ successful AI projects, you can expect impactful, knowledge-driven results from your assessment.
- Access to Leading AI Technologies: Benefit from our strong partnerships with industry leaders, including AWS, Google Cloud, and Microsoft. Thanks to that, we can provide assessments tailored to your current infrastructure, or help you to leverage pre-built solutions, improving your readiness.
- Customised AI Solutions for Your Business: We recognise each business’s unique needs. Our AI Readiness and Maturity assessments provide a personalised approach to addressing your specific requirements and goals. We also recognise the specific challenges of your industry and ensure that our assessment reflects that.
- Driving Measurable AI ROI: We focus on delivering tangible business outcomes, not just technology. We’ll help you measure and maximise the return on your AI investments, starting with AI maturity and readiness assessment.
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