Artificial Intelligence (AI) is playing an increasingly important role in areas such as IT Service Management (ITSM). Many organizations want to implement AI applications as quickly as possible to keep up with competitors, drive innovation, or simply work more efficiently.
Since the way companies use AI is now an important differentiating factor in competition, accurately evaluating AI maturity is of central importance. This article explains what exactly lies behind it and how organizations can improve their own AI maturity.
Numerous companies deploy AI tools before they are actually ready for them. According to a recent study by EasyVista (parent company of OTRS), the vast majority of companies already use AI in ITSM. However, only twelve percent have a fully mature ITSM framework, which is essential as a foundation for AI applications. The opposite can also be the case, although significantly less often: Some companies hesitate even though they are absolutely ready and could benefit considerably.
What Is AI Maturity?
AI maturity indicates where a company stands organizationally, technologically, and culturally in its handling of Artificial Intelligence and what the next meaningful steps are.
The central question is: What prerequisites must be established to successfully implement AI initiatives?
The most important areas of action and assessment dimensions are:
- Strategy and governance
- Data management and knowledge
- Technology and integrations
- Processes and workflows
- People and culture
- Value contribution and scaling
An AI maturity model is recommended not only for large enterprises but is also highly relevant for mid-sized companies, as it highlights the status quo, development potential, and the next logical steps.
As a general rule, AI can be beneficial across all areas of a company and achieve significant impact when it is used methodically, analytically, in a structured way, and with a focus on value. However, the prerequisites must first be in place: AI does not improve poor processes – it multiplies them.
The EasyVista study results also demonstrate that AI adoption and AI maturity are not the same thing. It turns out that satisfaction across all areas of use is significantly higher than actual usage. This suggests that companies have a positive attitude toward AI applications in ITSM but are prevented from using them profitably due to practical obstacles. The study also shows that AI maturity is an immensely important factor in enabling the meaningful and successful use of various AI applications in ITSM.
AI Maturity Model
Whether an organization is ready to implement a specific AI application cannot always be clearly determined in advance. While practical experience often provides the most reliable insights, a pure trial-and-error approach involves risks and costs.
To assess AI maturity as reliably and risk-free as possible, an appropriate model is needed. Such a model measures AI maturity across different dimensions and serves as a tool for systematically evaluating an organization’s level of maturity.
ITSM is one of the areas particularly well suited for the use of AI and where many organizations have already gained experience. Therefore, taking a closer look at AI maturity is especially worthwhile here.
The following maturity matrix shows the dimensions and assessment levels for AI in ITSM:
Legend:
1 Initial:
No systematic AI usage
2 Exploratory:
Initial experience and pilot projects
3 Established:
Structured implementation and productive usage
4 Integrated:
AI is part of the service organization
5 Transformational:
AI shapes processes, decisions, and value creation
Background: It is important to understand that AI maturity is not intended solely for companies that are not yet using AI applications. Rather, it serves as an indicator of the extent to which an organization is prepared to introduce additional AI applications.
Based on such a matrix, an overall score can be determined through an honest assessment across the individual dimensions.
There are two options:
- Add the individual values to obtain a total score between 6 and 30.
- Divide the sum of the individual values by the number of dimensions to obtain the arithmetic mean.
In the following, we use the average value because it is more common and more flexible. For example, it enables meaningful comparisons even when the number of dimensions differs.
How High Should AI Maturity Be?
The level of AI maturity required to be considered “AI-ready” depends on the specific use case. In principle, a company does not need to have a high maturity level across all dimensions in order to successfully introduce AI applications.
For most ITSM use cases, an average maturity level of approximately 2.5 to 3 is sufficient to be considered ready.
Examples of such use cases include:
- Ticket summaries
- Ticket classifications
- AI-generated responses
- Real-time translations
- AI chatbots
- Sentiment analysis
While these use cases are exciting, they aren’t plug-and-play. They require a sophisticated level of maturity in a few key dimensions. In particular, scores in Data Management and Knowledge as well as Process and Workflow Maturity should not be below 3.
It is advisable to use these values as an initial point of reference and then incorporate qualitative factors – such as a positive attitude within the company toward the respective application – into the evaluation of AI maturity. For example, an AI application cannot compensate for an insufficiently developed knowledge base and will only scale poor processes and workflows rather than optimize them.
Best Practices: How to Improve AI Maturity
Working on AI maturity may initially sound counterintuitive to many people: After all, AI is supposed to deliver the decisive leap forward. However, for that to happen, the right prerequisites must first be established. In other words, things may need to become more difficult for a short time – if AI maturity is not yet high enough – so that they can become easier, more efficient, and more successful later.
A range of approaches can help achieve this. The following best practices provide several compelling ideas from a broad spectrum of possible initiatives:
#1: Invest in Typical Human Capabilities
It may seem somewhat paradoxical, but especially in times of intensive AI usage, employees and their capabilities should remain the focus. Ideally, a strengths-based human-machine collaboration emerges that requires important human capabilities for the AI era.
AI applications should not replace human work but rather form a harmonious symbiosis with it. Since AI primarily takes over routine-based tasks, other human capabilities and strengths become even more important.
These include, for example:
- Emotional intelligence
- Creativity
- Decision-making
- Critical and unconventional thinking
#2: Thoroughly Evaluate AI Benefits
The benefits of Artificial Intelligence are diverse. However, it is not enough to implement AI applications and hope for quick wins. Instead, a solid strategy is required that defines the most important individual goals and weighs them against potential challenges.
For example, improving efficiency is a sensible objective in most cases, but it is formulated too broadly. In ITSM, a more concrete target would be to reduce MTTR by X percent through intelligent automation or to increase customer satisfaction by X percent by improving service quality through automatic ticket routing. The more precisely goals are defined, the more effectively value can be realized.
Tip: Several goals should be defined for each AI application, and they should be clearly trackable and measurable.
#3: Perfect and Standardize Processes
Before AI can realize its potential, well-developed processes are required. If processes are chaotic, unstructured, and lacking clear standards, AI can only replicate that situation. Therefore, clear standards and established processes are needed from which AI can learn.
#4: Create a Clear AI Roadmap
From the initial vision to becoming a strategic success factor, a clear direction is needed to guide AI adoption. AI should not be used simply because it dominates the discussion and serves as a competitive factor. While both are true, approaches are needed that clearly define strategic objectives, KPIs, milestones, and key work steps.
An AI roadmap provides direction and helps companies generate sustainable value.
#5: Test AI Flexibly
Particularly when AI maturity is still low, it is worthwhile to initially gain targeted experience with AI usage on a limited basis. Companies often make the mistake of investing early in extensive AI packages even though they do not actually need many of the included functions.
It is advisable to look for options that allow specific AI functions to be tested as needed and without long-term commitments. Credit-based services, for example, are well suited for this purpose.
OTRS AI Services: An Approach Aligned with Your Maturity Level
Flexible AI services are available for OTRS. Users benefit not only from intelligent automation and improved service quality but also from extensive control and transparency.
Conclusion: AI Maturity as a Valuable Enabler
Artificial Intelligence has become a key success factor in many business areas. Consequently, the pressure to implement new AI technologies on a broad scale is increasing. As a result, many companies implement AI applications prematurely and derive only limited benefits from them.
The reality is that clear processes, a well-thought-out strategy, and carefully defined goals are required first in order to use AI applications successfully and in a value-driven manner.
AI maturity is therefore a crucial factor on the path toward sustainable, responsible, and future-oriented use of new technologies. Determining AI maturity can serve as a useful indicator. Based on this, it is beneficial to define clear benchmark values – such as sufficient process maturity, a detailed roadmap, and robust data and knowledge management.