More than technology: How companies with vision, culture and expertise achieve AI maturity

Introduction

Media coverage of artificial intelligence is currently reaching unprecedented levels. Every day, success stories appear about chat-based assistants, image generators and autonomous software agents, whose performance leaps are setting new records every few weeks. At the same time, voices from politics and academia are warning of a loss of control, regulatory risks and massive job losses.

For companies, artificial intelligence is no longer a distant vision of the future, but a reality with a profound impact on business models, processes and collaboration within the organisation. For decision-makers, this creates a tension between fascination, uncertainty and the need to act. Those who implement AI too hastily risk getting bogged down in isolated solutions. Those who hesitate too long could lose their competitive edge. Key questions often remain unanswered in this context: How can a strategic entry be achieved? What skills are actually needed and how can they be developed? How can the use of AI be managed responsibly within the company?

SMEs face particular challenges: on the one hand, many companies have in-depth domain knowledge, strong customer relationships and a high level of vertical integration; on the other hand, budgets, personnel capacities and IT resources are limited. Historically grown system landscapes, heterogeneous data silos and a diffuse picture of actual data quality often make it difficult to implement AI initiatives quickly.

Without clear strategic guidance, these conditions often lead to costly dead ends: individual departments test AI licence software without involving IT, pilot projects fail due to data protection issues or an inadequate data basis, sceptics slow down AI initiatives for fear of reprisals, while enthusiasts secretly experiment with open-source AI models. Financial and human resources are wasted, insights remain isolated, and company-wide scaling of AI applications fails.

The answer lies in developing a holistic AI strategy. Such a strategy is much more than a purely technological roadmap. It is an organisational foundation that puts people at the centre and connects the areas of leadership, competence, organisation, culture and governance. It creates the framework for leveraging potential in a structured manner, managing risks and actively shaping the future viability of the company.

Develop a viable leadership vision

→ Strategic orientation, leadership skills, values, vision for the future, top-down commitment
What successful AI transformations have in common: a clear, value-based vision of the management that provides orientation, motivates and links all measures together.

A strategic AI transformation begins with a vision of the future that provides orientation and releases energy. Top management must specifically identify and actively communicate what contribution AI is expected to make to the business, when measurable results are expected and what values are essential in achieving this.

Specialised workshops for executives, in which the basic capabilities of AI systems are explained using industry-relevant use cases, are an effective way to create a sound basis for discussion. The aim is to enable senior management to see AI not as a threat, but as a powerful tool for achieving corporate goals.

Once this basic understanding has been established, the actual vision work can begin. In a strategic dialogue between management and the heads of the central business units, the identified potential is compared with the overarching corporate goals. This reveals which areas have the highest priority for AI initiatives.

Without this overarching direction, AI initiatives run the risk of remaining isolated and without long-term impact. A clearly formulated vision creates a meaningful framework: new AI use cases are then introduced not purely out of pressure to innovate, but because they make a measurable contribution to achieving the company's goals. Such a vision is particularly valuable in medium-sized companies – it reduces uncertainty, focuses resources on key challenges and ensures that technological developments are driven forward in a targeted and effective manner.

The vision also plays an important role as a point of reference for corporate culture. The use of AI challenges established processes, role models and responsibilities, and can therefore cause considerable uncertainty. A clearly formulated vision that focuses on responsible and human-centric AI applications creates security and promotes acceptance among employees.

Formulate a clear, value-based AI vision and embed it top-down so that prioritised initiatives contribute specifically to the company's goals and are supported by all employees.

Differentiated skills development as a foundation

→ Learning paths, qualifications, basic training, expert knowledge, community formats
How employees become familiar with AI through tailored learning opportunities – from entry level to specialisation – and thus actively shape change.

However, a vision remains ineffective if employees are unable to implement it. It is people who must learn to use AI sensibly, interpret its results and integrate it into their daily work. Comprehensive but differentiated skills development is therefore the second central pillar of a holistic strategy.

The level of knowledge among the workforce usually varies greatly: from employees who only know AI from headlines, to cautiously interested individuals, to experts. An effective learning concept differentiates these target groups, meets them at their level and offers individual learning paths.

Beginners are given low-threshold access to the topic of AI through short, interactive training formats with practical relevance. Participants acquire essential basics – from an initial technological classification to prompting basics and data ethics. Advanced learners first deepen their know-how in advanced or specialised courses on topics such as model fine-tuning, retrieval-augmented generation or prompt optimisation. They then network in self-organised expert circles to exchange best practices and solve project-specific challenges together.

Formats such as a weekly "AI learning community," a monthly "AI demo day" where pilot users present their results, or an internal discussion forum where questions can be asked openly connect the various competence groups. Such experience reports are particularly important for beginners because they break down barriers. An internal AI knowledge repository collects records of digital information events and exchange formats, best practice guidelines and lessons learned in order to preserve knowledge and make it accessible to everyone.

Qualify your employees through structured, target group-specific competence development with practical basic courses, in-depth specialised training and accompanying community formats to use AI responsibly and effectively in everyday work.

Learning community and psychological safety

→ Error culture, knowledge transfer, trust, incentive systems, collaborative learning
Why an open learning culture with mutual trust, visible successes and constructive handling of uncertainty is the true driver of innovation.

However, an effective learning ecosystem needs more than just content – it thrives on a culture in which knowledge is shared openly and intrinsically. Almost every company has people who secretly use new AI tools without official permission from management to increase efficiency in their daily work, build individual expertise and develop advanced use cases – the so-called "secret cyborgs". Their pragmatism is worth its weight in gold, because it shows where AI is already delivering real benefits. But fear of consequences under employment law means that their results often remain invisible. When knowledge is not shared, parallel worlds emerge in which mistakes are not discussed, potential and risks are not identified, and successes are not scaled up. A learning-oriented culture must therefore reward curiosity and create formats for exchange.

Collaborative knowledge work and rapid prototyping can only emerge when people share time and ideas that were previously considered a personal competitive advantage. A modern incentive system resolves this conflict of objectives by rewarding visible contributions to collective learning progress as well as measurable business results.

Psychological safety also plays a central role in this context: open communication about which experiments are expressly desired and a constructive culture of error, in which failures are seen as learning opportunities, create trust. Managers can also make a significant contribution in this context by transparently disclosing their own learning curves and mistakes. This creates a culture of knowledge sharing that promotes the speed of innovation within the company and strengthens cohesion.

Encourage employees to openly share their experiences with AI and promote a transparent culture of error to create a learning ecosystem in which individual knowledge becomes accessible, psychological safety grows and AI competence can be scaled sustainably within the company.

AI lab as an organisational driver

→ Coordination, use case management, best practices, governance, agile implementation
The role of an interdisciplinary AI lab as a central catalyst for scalable, measurable and trustworthy AI initiatives.

Building on the AI vision that has been formulated, an organisational engine is needed to coordinate, accelerate and professionalise AI activities throughout the company. An AI lab offers exactly that: an agile, interdisciplinary unit that systematically identifies use cases, provides tools, plans cross-functional exchange formats and serves as a sparring partner or multiplier for the specialist departments.

Transparency is crucial for acceptance: the lab also publishes progress reports, demonstrates prototypes and obtains feedback from future users, so that every team automatically benefits from the latest best practices. At the same time, the lab monitors regulatory developments, ensures governance standards and adapts policies as necessary.

Set up a transparent, interdisciplinary AI lab that pools expertise, shares best practices and securely transforms AI initiatives from vision into measurable business results.

Change management & early HR involvement

→ Role change, communication, participation, learning paths, cultural work, employee retention
How close integration of HR and change management reduces fears, promotes acceptance and secures the social side of transformation.

The introduction of AI is fundamentally changing job profiles, workflows and career paths: automated processes and data-driven decisions can bring significant efficiency gains and relieve employees of routine tasks. At the same time, however, there is growing concern that familiar activities will become less important and jobs could be at risk. Professional change management that is closely integrated with the human resources (HR) department from the outset is therefore not an optional accompanying programme, but a decisive factor for the success of the transformation process. The role of the HR department goes far beyond organising training courses. It is the strategic partner of senior management with regard to the social and cultural aspects of the transformation.

Identify who is affected by AI projects, what concerns exist and what opportunities are emerging. Resistance is rarely expressed openly. Indicators include low participation rates in AI training courses or exchange formats, increasing speculation about job cuts, delays or a lack of data provision. A direct approach paired with concrete training offers and exchange formats that replace fears with actionable skills is effective. This creates a communication cascade: from top management kick-offs to departmental dialogues to employee FAQs. Use clear messages about why AI is being introduced, what goals are being pursued, and what support is being offered.

The HR department defines new competence profiles, establishes learning paths and anchors the use of AI in target agreements. Flexible training budgets should enable employees to book specialised courses spontaneously when a project requires it. At the same time, HR remains the guardian of the culture: workshops should be held to align corporate values with AI ethics guidelines, and the works council and data protection officer should also be involved from the outset.

Integrate HR and change management from the outset, identify those affected and their concerns, communicate clearly and create targeted learning paths to ensure acceptance, culture and success for your AI transformation.

Data protection & governance

→ EU AI Act, data ethics, protective measures, role rights, policies, building trust
Why technological excellence without clear governance is futile – and how data protection, accountability and compliance enable innovation.

Hardly any other area currently causes as much uncertainty as the careful handling of data, especially when cloud-based AI services are used. Often, a careless prompt is enough to feed trade secrets, customer information or personal data into an external model in an uncontrolled manner. The more powerful AI models become, the greater the risks to data protection, copyright and corporate reputation.

Understanding risks

• Confidential data can inadvertently find its way into external systems.
• Model leaks enable attacks that reconstruct training data.
• Regulatory pressure is increasing due to the EU AI Act and stricter data protection guidelines.

A preventive risk register helps to prioritise countermeasures and make progress transparent.

Technical and organisational measures

• Involve data protection officers in all AI projects at an early stage – they assess risks, support protective measures and ensure compliance with legal requirements.
• Define roles and access rights strictly – each actor only receives the access they need for their task, rather than blanket authorisations.
• Audit logs store interactions and allow subsequent analysis.
• Prompt policies establish guidelines to prevent data leaks.
• Implement enterprise LLMs – these ensure contractually guaranteed data isolation so that information is neither shared externally nor used for model training.

Companies need a tangible AI governance framework that not only protects company and customer data, but also strengthens employee trust in new AI-supported processes. However, for this trust to develop, governance must not be perceived as a bureaucratic hurdle, but rather understood as an enabler that specifically supports innovation. The key to this lies in close cooperation with the specialist departments: AI governance regulations should be developed jointly and continuously adapted to new requirements. Lean approval processes, digital workflows and clear points of contact ensure that teams know what they need to request from whom and why.

It is essential that both managers and employees understand the purpose of AI governance: it serves as preventive risk protection – for example, against claims for damages, loss of trust or fines that make projects significantly more expensive in retrospect than an early data protection review.

Establish a practical AI governance framework that ensures responsible data handling, meets regulatory requirements and strengthens the trust of employees and stakeholders.

Conclusion

The successful use of artificial intelligence in SMEs requires much more than technical solutions – it starts with a clear strategic vision and ends, not least, with a vibrant learning culture. Without guidance and a sense of responsibility, well-intentioned AI initiatives risk fizzling out in the chaos of isolated individual projects. Systematic competence building, psychological safety and transparent governance lay the foundations for sustainable transformation. It is essential to involve employees, take fears seriously and actively create learning opportunities. An AI lab as an organisational driver and the early involvement of HR ensure that the AI strategy can be integrated into existing structures. Ultimately, it is crucial to understand AI not only as a technology, but as a cultural change – one that arouses curiosity, connects people and strengthens future viability.

noventum consulting – your partner on the path to AI maturity

As a strategic partner, we support you in integrating AI into your business models, processes and organisation in a structured and effective manner. Whether you are just entering the world of AI or want to further develop existing initiatives, we create a holistic approach to anchor AI in your company in a sustainable and value-adding way through a tailor-made AI roadmap and sound advice on use cases, governance, compliance and change management.