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AI Maturity Assessment for Manufacturing Enterprise: state of play

AI Maturity Assessment for Manufacturing Enterprise: state of play

Artificial intelligence (AI) is revolutionizing how society functions and is opening up new business opportunities for manufacturing companies.

Access to AI tools in organizations is increasingly facilitated by the availability of big data, digitization, access to efficient and cost-effective computing capabilities, and the development of new algorithms that are increasingly within reach of non-expert users.

Therefore, it is essential for organizations to understand their possibilities to benefit from AI, their knowledge-technical-business gaps, and to put in place the proper corrective measures to fill such gaps: to this end, AI Maturity Assessment methods and tools have been recently developed, as an extension-customization of Digital Maturity Models (DMMs).

DMMs offer organizations a simple but effective way to measure their capabilities in a given area and contribute to organizational transformation and the development of organizational competencies by initiating a transformation process.

It may be useful to point out that maturity models are different from surveys. A survey responds to the need to provide a picture of the situation to a stakeholder, usually, an institutional one, on the largest and most representative sample of a population of organizations to help the stakeholder understand the sector and decide on actions.

A maturity model, on the contrary, investigates specifically (in a more or less in-depth way) the organizational maturity and the level of technological development of an organization concerning a discipline. It pursues informative purposes (increase skills, provide experience, raise awareness) and has the ultimate aim of identifying improvement paths for the organization, starting from a quantification of the current maturity. Maturity model output is generally in a (radar) chart form; this quantitative aspect is fundamental to defining the starting point of a development path. It is also helpful for comparison with other organizations and summary evaluations of an entire sector.

In order to be effective, assessment methods and tools must be sufficiently complete for the subject matter and have inner coherence between questions and in the graduation of the scale of values of the answers, at least tentatively.

Most assessment tools can be found on the web and be filled directly, without assistance. There is also a smaller number of more complex tools that require the presence of a professional both in the investigation phase and in the processing of the information collected. In those cases, the assessment is carried out on-premise. It can be followed by developing a roadmap along the most promising or important lines to improve the organization’s competitiveness according to its strategic objectives, identifying opportunities and costs. The following steps would be a gradual integration of the developed solutions into the business processes, up to developing new business models in the most successful cases.

The market offers a wide variety of models assessing the maturity of organizations in terms of digitalization or Industry 4.0. The number of maturity assessment models related specifically to AI adoption, on the contrary, is still limited and of relatively recent development. We describe here two of the most significant ones.


VTT AI Maturity Tool

This tool was developed in 2019 by the VTT Technical Research Centre of Finland Ltd. (VTT) and the University of Oulu, based on previous works on the subject. It is a quick self-assessment web tool evaluating the AI maturity level of organizations like SMEs, Large enterprises, the Public sector, and Digital Innovation Hubs.

Essentially, it answers the questions “How prepared is your organization for the use of AI?” and “Where is your organization, compared to other tool respondents?”

It consists of 12 questions, two for each of the following dimensions: Strategy & Management, Products and Services, Competences and Cooperation, Processes, Data, and Technology.

The VTT model doesn’t require a high level of AI literacy to answer and provides as output a radar chart that visualizes the organization’s maturity at a glance to document the current state of AI and serve as a starting point for subsequent developmental actions.


AppliedAI Maturity Model

The appliedAI MM was developed by UnternehmerTUM GmbH in 2021. Unlike VTT and most digital maturity assessment models in use that focus on processes, appliedAI is more focused on how AI is implemented in the organization, irrespective of the processes it is applied to. It too is a self-assessment web tool devoted to SMEs, Large enterprises, the Public sector, and DIHs, but it requires at least a medium level of AI literacy to respond.

It consists of more than 100 questions, divided into nine areas: AI Ambition and Steering (referring to AI Vision and Strategy), Use cases (concrete, systematic and widespread applications of AI in the organization, consistent with Vision and Strategy), organization (how the organization creates the necessary structures for effective collaboration, adoption, control and feedback of AI solutions), Expertise (skills enhancement, training, motivation; collaboration with external experts), Culture (how the organization creates an AI-oriented culture), Technology (presence in the organization of infrastructures, technologies, and procedures that support the adoption of AI solutions), Data (how the organization is doing in terms of data - ingestion, cleaning, storing, quality, access…), Ecosystems (mainly partnerships), Execution (effective and efficient application of AI models to business processes).

By its nature, this tool is aimed at the entities that have already adopted AI solutions to reveal areas to be addressed and provide insightful recommendations of action toward higher levels of AI maturity.


AI’s importance in organizations is rapidly developing and spreading AI maturity assessment tools. It will be up to the organization to identify appropriate tools and partners to undertake an effective roadmap for AI adoption. For instance, in the Digital Manufacturing Platform cluster, the Connected Factories “AI for Manufacturing” new pathway is a tool to position industrial cases in a 5-levels scale of AI-enabled autonomous behaviors, from human to machine-controlled systems. STAR will be present on June 13th afternoon (Brussels) to present and demonstrate the first results of such an assessment (by invitation only) Connected Factories European workshop on the AI for manufacturing Pathway.


The STAR ICT-38-2020 project is one of the H2020 innovation actions aiming at applying highly innovative AI models and solutions to Manufacturing, mainly focussing on technological enablers and pillars for Cyber Security, Safety, and Explainable / Trustworthy AI. STAR provides a set of three industrial pilots: PHILIPS Consumer Lifestyle (Human-Cobot Collaboration for Robust Quality Inspections); IBER-OLEFF plastic components (Human Centred Artificial Intelligence for Agile Manufacturing 4.0), and DFKI SmartFactoryKL (Human Behaviour Prediction and Safe Zone Detection for Routing). Their maturity regarding the STAR advanced AI solutions will be carefully assessed, and gaps identified to maximize the impact of STAR AI solutions.