6 steps to assess the AI maturity level of your company

Artificial intelligence (AI) is no longer a dream of the future, but is already a reality in many companies. Nevertheless, decision-makers are faced with the question: how ready are we really for AI? Introducing and scaling AI without first assessing the situation is like flying blind. In fact, only 8% of companies have a fully consistent data structure - so the basis of any AI initiative is often missing. At the same time, although 65% of companies see AI as part of their long-term strategy, only 13% have defined clear, measurable goals for it. This discrepancy makes it clear that many AI projects fall short of their potential due to a lack of foundation and direction.

To remedy this, a structured AI maturity check is worthwhile. A proven approach is the empirically based Hexagon model from CorpIn GmbH. This model, developed on the basis of one of the most comprehensive AI studies in Switzerland(Swiss AI Report 2025 with 1,338 companies surveyed in collaboration with the University of St. Gallen), identifies six key fields of action. Based on these six dimensions, the AI readiness - i.e. the AI maturity level of your company - can be systematically assessed and increased in a targeted manner. In the following, you will find out which six steps are crucial for this and how you can proceed in each area to successfully anchor AI in your company.

Step 1: Data foundation - creating the basis for successful AI

Data is the raw material of AI. The quality of AI applications depends directly on the quality of the underlying data. Is your data valid, up-to-date and sensibly structured? Reliable analyses and well-founded decisions can only be made with clean, consistent data. In practice, there is often a need to catch up: just 8% of companies have a fully consistent data structure. Information is often stored in data silos or outdated systems - a risk that can slow down any AI strategy. A lack of data quality and availability are also among the most common AI hurdles (for 35% of companies). A robust data foundation is therefore the indispensable basis on which all further AI initiatives are built.

Recommended action: Carry out a thorough data audit. Identify which data sources are available, the condition of your data (completeness, accuracy, up-to-dateness) and where there are gaps or inconsistencies. Establish company-wide data standards and responsibilities for data quality (e.g. through data governance and clear owners for important data domains). Invest in data preparation and clean up duplicates or outdated entries. If data is stored in different locations, check the possibility of merging it in a central data platform or data warehouse. A solid data foundation not only creates trust in AI results, but also increases the agility with which new AI applications can be introduced.

Step 2: Strategic objective - anchoring AI in the corporate strategy

Without a clear strategy, the use of AI remains piecemeal. AI initiatives are only successful if they are strategically anchored. This means that they need support and commitment from the very top (C-level), clear objectives and continuous monitoring of the results. There is still a need for action here in many companies. According to the Swiss AI Report 2025, although two thirds (65%) of the companies surveyed consider AI to be an integral part of their long-term corporate strategy, only 13% work with clearly defined, measurable goals for AI projects. Over half do not currently measure the success of AI initiatives at all (51% do not use KPIs). As a result, AI projects fizzle out or fail to deliver real added value because there is no strategic framework that sets priorities and makes progress measurable.

Recommended action: Make sure that AI is firmly anchored in your business strategy. Define an AI vision that contributes to the overall goals of your company - be it increasing sales, gaining efficiency or improving customer loyalty. Set concrete goals and KPIs for each AI project (e.g. halve process time, increase customer satisfaction by x%) to make progress measurable. Report regularly to top management on successes and hurdles to ensure backing. Ideally, you should establish AI governance: a committee or responsible persons who steer strategies and projects, promote synergies between departments and ensure that all AI initiatives contribute to common corporate goals. A strategic compass gives your AI investments purpose and direction - and significantly increases the chances of success.

Step 3: Technical requirements - ensuring the right infrastructure and integration

Even the best AI idea will fail if the technical requirements are not met. A powerful IT infrastructure is the basis for the sustainable success of digital solutions and AI applications. This includes scalable systems, sufficient computing capacity and seamless integration into the existing IT landscape. In many companies, this is precisely where a key obstacle can be seen: 64% of companies are struggling with the lack of integration of AI into their existing systems. Almost half (40%) do not have end-to-end IT integration - specialized systems such as business intelligence or supply chain solutions are often isolated and not linked to AI solutions. This results in data silos, inefficient workflows and limited scalability of AI pilot projects.

Recommended action: Check the status quo of your IT architecture with CorpIn. Is your current hardware and cloud infrastructure ready to process large amounts of data and compute-intensive AI models? If not, plan appropriate upgrades or the use of cloud services in order to be able to scale flexibly. Also make sure that there are interfaces between your systems: AI solutions should be able to communicate smoothly with data sources, databases and applications (ERP, CRM, BI, etc.). Invest in a modern data infrastructure - such as data lakes or streaming platforms - to make data available in real time. It is also important to take a look at software tools: Does your team have suitable development environments, ML Ops tools and security solutions for AI? Examine your technology landscape and eliminate bottlenecks before rolling out AI on a large scale. This will help you create a stable technical foundation on which AI solutions can be implemented and scaled efficiently.

Step 4: Security & privacy - gaining trust through data protection and compliance

With the introduction of AI, there are not only opportunities but also risks. Technology brings with it responsibility - especially when it comes to sensitive data. Accordingly, data security, data protection and compliance are among the key issues for AI projects. Companies must ensure that data worthy of protection is consistently secured and only used lawfully. This includes compliance with data protection laws (e.g. DSG/DSGVO) as well as internal guidelines for handling data and AI models. The concern is justified: Every second company (50%) has concerns about data protection and security in AI initiatives. If these issues are neglected, there is not only a risk of legal consequences, but also a loss of trust - among customers, partners and employees.

Recommendation for action: Integrate security & privacy by design into all AI projects. This means considering security and data protection aspects as early as the development and introduction of AI solutions. Carry out risk analyses: What new risks arise from the use of an AI system (e.g. in data processing or decision-making) and how can you minimize them? Establish clear compliance guidelines for AI - for example, guidelines on which data may be used for models, how results are validated or how bias is checked. Create transparency for your stakeholders: Communicate how AI is used in the company and train your employees on data protection and security. If necessary, involve a data protection or ethics council to monitor the use of AI. By making security and privacy a priority, you create a basis of trust for all further AI activities.

Step 5: Cultural dimension - promoting acceptance and change in the workforce

The introduction of AI is not just a technology project, but above all a transformation process for people. The corporate culture plays a key role in determining whether AI innovations are accepted or rejected. Openness, curiosity and a willingness to learn are characteristics of a culture in which AI can thrive. However, change also understandably triggers fears - whether it be concerns about job losses or being overwhelmed by new technologies. In fact, 41% of companies report resistance or skepticism towards AI within the team. This shows that without appropriate cultural support, AI projects run the risk of being slowed down. A supportive working environment, on the other hand, in which mistakes are seen as learning opportunities and innovation is welcomed, acts as a catalyst for successful AI integration.

Recommended action: Rely on active change management. Communicate the goals of your AI initiatives transparently and at an early stage: Why is AI being introduced, what benefits does it have for the company and employees? Emphasize that AI is intended to provide support (e.g. take over monotonous tasks), not be a threat. Involve employees in pilot projects at an early stage and allow AI ambassadors or "power users" from the specialist departments to have their say and report positively on their experiences. Training and workshops to raise awareness can reduce fears and promote competence across the board. It is also important for management to set an example: if line managers are open about AI and promote its use, this signals support. Celebrate successes early on (and learn openly from failures) to show that AI is a shared learning process. In this way, you can gradually develop a corporate culture that sees change as an opportunity and supports AI innovations.

Step 6: Awareness & competence - building knowledge and developing specialists

The human factor remains the linchpin of every AI transformation. Even the best database and state-of-the-art technology are of little use if there is a lack of awareness and expertise in dealing with AI. Awareness means an awareness of AI - from management to each individual employee, there should be a basic understanding of what AI can (and cannot) do and where the opportunities and risks lie. Competence, on the other hand, means having the necessary skills to develop and implement AI solutions, or at least to work with them effectively. Many companies still have some catching up to do here: Only 9% of companies today offer mandatory and regular AI training for their employees. And 39% of companies cite a lack of internal expertise as one of the biggest hurdles in AI projects. As a result, there is a lack of specialists who can competently drive AI projects forward and a broad basic understanding that promotes acceptance and appropriate use.

Recommendation for action: Invest specifically in building AI skills. Start company-wide training programs that are tailored to different target groups - from overview events for top management (strategy, ethical implications of AI) to technical training for developers and analysts (e.g. machine learning methods, data engineering, MLOps). Promote a learning culture: establish formats such as Lunch & Learn, internal AI communities or hackathons where employees can exchange ideas and develop prototypes together. If there is a lack of internal know-how, bring in external expertise - for example by recruiting experienced AI specialists or collaborating with universities and consultancies. It is important that knowledge does not just remain with individuals: create awareness on a broad front by making successes visible and communicating them clearly. The more your employees know about AI and the more competent they are in dealing with it, the better opportunities can be identified and risks controlled. This lays the foundation for a learning organization that can grow with AI.

Conclusion - your path to an AI-ready company

AI readiness is the key to turning individual pilot projects into sustainable corporate success. Those who honestly assess their company's AI readiness along the six dimensions mentioned above will gain a clear picture of their own strengths and weaknesses - and thus valuable starting points for improvement. The "6 steps" presented here offer an action-oriented framework for closing gaps in a targeted manner: from the data foundation to strategy and technology through to security, culture and skills. It is important to take a holistic view of these areas, as they interlock like cogs in a clockwork mechanism. An excellent database alone is of little use if employees are not involved; similarly, an open culture will be of little use if there is a lack of technical resources. Only the interplay of all six factors leads to true AI excellence in the company.

In practical terms, this means that you should proactively tackle the assessment of your AI maturity level. Use proven models and tools - such as CorpIn's Hexagon platform, which is based on a scientific framework and allows you to compare your position with industry norms based on data. The Swiss AI Report 2025 also offers in-depth insights and benchmarks to guide you. Learn from the experiences of over 1,300 companies and incorporate these insights into your own strategy. By taking the right steps now, you will lay the foundation for AI not only to be implemented in your company, but also to be accepted and scaled profitably. This will make your company fit for the future - an AI-ready company that fully exploits innovation potential and stays ahead of the digital competition.  

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