Elevating AI Maturity: Steps to Future-Proof Your Business
A recent Gartner survey highlights how Artificial Intelligence maturity correlates with sustained AI success. Organizations with high AI maturity are far more likely to keep AI initiatives running long-term – 45% of high-maturity companies keep AI projects operational for over three years, compared to only 20% of low-maturity organizations.
This longevity translates to greater AI ROI and lasting impact. High-maturity firms also foster greater trust in AI (57% of business units trusting new AI solutions vs just 14% in low-maturity firms, thanks to strong leadership and robust practices.
What is Artificial Intelligence Maturity? Gartner’s 5-Levels Of AI Maturity
AI maturity refers to an organization’s level of advancement in integrating AI into its operations, culture, and strategy. It indicates how well the company can leverage AI for business value, from initial experimentation to pervasive, transformational use.
Gartner’s AI Maturity Model, for example, defines five stages of maturity on a 1–5 scale:
Level 1 – Awareness: The organization is aware of AI’s potential but has not yet implemented it. Interest is high, but AI is still more buzz than action – ideas are floated, though no concrete projects are in place.
Level 2 – Active: The company is experimenting with AI in a limited, ad-hoc way. Teams might run pilot projects or build simple models (e.g. in sandbox environments or using open-source libraries) to test AI capabilities..
Level 3 – Operational: AI and machine learning are employed in day-to-day operations. Dedicated ML engineers or data scientists manage models in production, data pipelines, and ML infrastructure. AI solutions start delivering business results in specific workflows.
Level 4 – Systemic: AI is used more broadly and strategically to transform processes or business models. The organization has a solid data and ML foundation and is innovating with AI at scale, beyond isolated use cases.
Level 5 – Transformational: AI is embedded in the company’s DNA – it drives core products, services, and decisions. These organizations rely on AI for competitive advantage, with continuous optimization and AI scaling across all facets of the business.
Reaching the highest level of Artificial Intelligence maturity isn’t an end in itself for every organization – what matters is using AI at the right maturity level to meet your business goals.
However, studies show that higher maturity brings clear benefits: more sustained projects, greater trust and adoption, and competitive differentiation.
How to Do an AI Maturity Assessment In 6 Steps – Step-by-Step Guide
Evaluating your organization’s AI readiness is a crucial first step to improve it. An AI maturity assessment provides a structured look at where you stand and helps identify what’s needed to reach the next level. Here’s a step-by-step guide to conducting an AI maturity assessment:
1. Choose a Maturity Framework:
Select an AI maturity model as your benchmark. Using an established framework (like Gartner’s five-level model or others) gives you clear criteria for evaluation. This framework will define the stages and the key dimensions (e.g, strategy, data, technology, people) that you should assess.
2. Evaluate Key Dimensions of AI Capability
Gather data on your current capabilities across core areas of AI readiness. This typically includes examining your data infrastructure, technical stack, talent skills, governance policies, and how AI is currently used in processes. For each dimension, collect evidence through surveys, interviews, or performance metrics. Be honest and objective – the goal is to identify strengths and areas for improvement.
3. Assess and Score Current Maturity
Using the framework’s criteria, determine your organization’s current maturity level in each dimension (and overall). For example, you might find your data practices are at a “managed” level 3, but your governance or talent might be lagging at level 2. Some assessments provide an AI maturity score or rating that summarizes your readiness. This scoring highlights where you excel and where improvement is needed.
4. Identify Gaps and Set Targets
Compare your current state to where you want to be. Identify gaps between your current maturity and the target level required to achieve your business goals. For instance, if your goal is to operationalize AI in customer service, but your maturity in data quality or model deployment is low, note those gaps. It’s often neither necessary nor feasible to jump to the highest maturity at once – instead, set realistic target levels for each area that align with your strategy.
5. Develop an AI Roadmap
Based on the identified gaps, formulate a plan to close them. Prioritize initiatives that will have the biggest impact on reaching your target maturity. This roadmap should include specific projects or actions, such as improving data governance, investing in new tooling, hiring or training staff, or establishing an AI Center of Excellence, along with timelines and ownership. A concrete roadmap is a key output of the assessment process.
6. Implement Improvements and Monitor Progress
Execute the roadmap step by step. Monitor how your maturity evolves using the same metrics you started with. It’s wise to conduct periodic reassessments. For example, measure the growth and adjust the plan quarterly. Continuous improvement and governance oversight ensure you sustain momentum and mitigate risks as you scale AI.
By following these steps, an AI assessment will not only reveal where your organization stands but also provide a clear action plan to elevate your AI capabilities. Remember that AI implementation should be approached incrementally by focusing on achieving maturity in foundational areas (like data and skills) as a springboard to more advanced AI uses.
Learn more about how AI data can help CIOs and CTOs foster innovation and transformation.
7 Key Factors in an AI Maturity Assessment – Executive Snapshot
Strategy & Leadership:
Clear AI vision, C-suite sponsorship, and a roadmap that links AI investments to measurable business outcomes. High-maturity firms appoint dedicated AI leaders and fund projects accordingly.
Data Quality & Infrastructure:
Reliable, well-governed data pipelines and modern platforms that supply clean, timely data to models. Persistent data gaps or silos instantly cap AI potential.
Technology & Engineering:
Scalable cloud/ML stack, ML-Ops, version control, and automated monitoring that keep models secure, compliant, and production-ready. Robust engineering standards separate pilots from enterprise AI.
People & Skills:
Access to data scientists, ML engineers, and domain experts, plus ongoing upskilling programs. Without talent depth, even the best tools stall.
Governance & Ethics:
Formal policies for privacy, security, model risk, and bias mitigation. Well-defined approval workflows and AI councils build trust and control exposure.
Process Integration:
Degree to which AI is embedded in day-to-day operations and decision flows. Mature organizations weave models into core systems; beginners keep AI in isolated pilots.
Culture & Adoption:
Degree to which AI is embedded in day-to-day operations and decision flows. Mature organizations weave models into core systems; beginners keep AI in isolated pilots.
How an AI Solution Provider Can Help Assess Your AI Maturity
Assessing AI maturity can be complex. This is where partnering with an experienced AI solutions provider or consultant can add significant value. An external AI consulting firm brings an objective perspective and deep expertise to ensure your assessment is thorough and accurate.
Here are a few ways an AI solution provider can help:
Expert Frameworks & Benchmarking:
AI consultants have tried-and-true frameworks to evaluate your capabilities. They can quickly benchmark your organization’s AI readiness against industry standards or competitors, identifying where you stand. Instead of starting from scratch, you benefit from their refined assessment tools and libraries of criteria.
Unbiased, Comprehensive Evaluation:
External assessors provide an unbiased look at your strengths and weaknesses. They will thoroughly evaluate your processes, infrastructure, talent, and strategy without internal blind spots. This objectivity can uncover issues your internal teams might overlook or be hesitant to acknowledge.
Identification of Blind Spots & Opportunities:
Seasoned AI solution providers have a broad view of what factors drive success. They might spot, for example, that your company lacks a proper data governance framework or that your customer-facing AI use cases are too narrow. These could be blind spots internally. Consultants help identify hidden gaps and untapped opportunities to leverage AI, drawing from their cross-industry experience.
Accelerated Roadmap & Best Practices:
With specialized knowledge, AI providers can accelerate the creation of your improvement roadmap. They bring in best practices and lessons learned from other implementations. This includes recommending the right tools, architectures, or organizational structures (such as setting up an AI Center of Excellence) that suit your context. Essentially, they act as a guide to help you avoid common pitfalls and focus on initiatives that yield quick wins (for immediate ROI) as well as long-term capability building.
Customized Training and Change Management:
An AI solutions partner can also assist in educating your team and fostering a pro-AI culture. They might conduct workshops to increase awareness, help train your staff on new AI tools, or establish governance practices. This outside support can build confidence and momentum within your organization to embrace AI changes.
In short, an AI solution provider serves as both a strategist and an enabler in the maturity assessment journey. They augment your capabilities with external insight, ensuring the assessment is done correctly and that the resulting action plan is actionable. Especially for startups and businesses without extensive in-house AI expertise, partnering with such experts can jump-start your progress toward high AI maturity.
7 Key Differences Between High and Low AI-Maturity Companies
Not all organizations reap the benefits of AI equally; low AI-maturity companies differ markedly from high AI-maturity ones in several ways. Here are seven top differences, as identified by research and the Gartner survey, and why they matter:
1. Project Longevity and Sustainability:
High-maturity companies sustain AI projects far longer than low-maturity firms. Gartner’s survey found 45% of high-maturity organizations keep AI initiatives operational for 3+ years, versus only 20% of low-maturity organizations. Unfortunately, less mature organizations often struggle to move beyond short-term experiments, resulting in AI projects being abandoned early.
2. Trust and Adoption of AI:
In high-maturity environments, business users trust AI outputs and are eager to adopt AI solutions, whereas low-maturity cultures are skeptical or slow to embrace them. According to the survey, 57% of business units in high-maturity organizations trust and are ready to use new AI solutions, compared to a mere 14% in low-maturity firms. The low adoption rate in immature companies shows that even if they build AI tools, people may not use them due to lack of confidence or understanding, severely limiting the technology’s impact.
3. Leadership and Organization:
A striking 91% of high-maturity organizations have dedicated AI leadership roles (such as a Head of AI or Chief AI Officer), providing vision and oversight to AI initiatives. Low-maturity companies typically lack this kind of formal leadership focus on AI – AI projects might be scattered or driven bottom-up by enthusiasts without executive ownership. The presence of a dedicated AI leader or team in advanced organizations ensures coordination, consistent investment, and that AI efforts align with business strategy.
4. Strategic Project Selection:
High-maturity firms choose AI projects based on clear business value and technical feasibility, guided by a strategic roadmap. They tend to start projects that solve high-impact problems and are realistic given the data and skills available.
In contrast, low-maturity firms often struggle with finding the right use cases. In fact, 37% of low-maturity organizations say identifying appropriate AI use cases is a top barrier. They may chase trendy ideas or attempt projects misaligned with their capabilities, leading to failures or disillusionment.
5. Governance and Best Practices:
Robust governance and engineering practices underpin high-maturity AI efforts. These companies establish standards for data quality, model evaluation, security, and ethical use of AI from the start. Low-maturity organizations often lack such rigor; their AI experiments might not follow consistent best practices, which can lead to issues or stakeholder mistrust. This difference means high-maturity organizations can scale AI more efficiently and reliably, while others hit roadblocks when trying to go beyond one or two pilots.
6. Measurement of ROI and Impact:
Around 63% of high-maturity organization leaders conduct financial risk analysis, perform ROI analysis, and concretely measure customer impact for AI initiatives, which in turn helps them sustain and scale successful projects.
Low-maturity organizations, on the other hand, often do not rigorously measure outcomes – they might deploy an AI pilot without clear KPIs or fail to analyze its business impact. As a result, they struggle to prove the value of AI to leadership, making it harder to justify further investment.
7. Focus of Key Challenges:
The obstacles faced by high- vs low-maturity organizations tend to differ, reflecting their stage in the journey. For example, security and scaling concerns dominate in high-maturity firms – 48% of high-maturity leaders cite security threats as a top barrier in AI projects. In contrast, low-maturity firms are still figuring out basic viability – their top challenge is often identifying viable use cases (37% cite this) or gathering quality data.
Additionally, high-maturity companies more often centralize AI resources, including AI strategy, data, and infrastructure, to drive consistency, while low-maturity companies may still be decentralized and inconsistent in their approach.
Check out how Amzur’s AI expertise helped a food retail client in reducing waste and quality management.
Stay Ahead With AI Maturity Models:
Artificial Intelligence maturity is a journey, not a destination. As the Gartner findings suggest, reaching a higher maturity level enables companies to sustain AI initiatives for the long haul and derive real business value, while those at lower maturity risk false starts and unrealized potential. By assessing your AI readiness across strategy, data, technology, people, and processes, you can create a clear roadmap to advance your capabilities.
For startups and businesses, the message is clear: don’t treat AI as a one-off experiment. Instead, cultivate it as a core competency, much like the high-maturity organizations do with executive support, proper planning, and an eye on long-term impact.
Still need expert support in assessing your AI maturity and AI readiness assessment? Get in touch with Amzur’s AI strategists today.
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Director ATG & AI Practice
