How companies can embed AI across people and organisational culture
AI has moved beyond the hype to become a reality in everyday work. Yet for it to truly become part of the fabric of organisations, the technology must be systematically embedded across people and culture, writes Dharshana Sellappah, Co-Lead for Organisational Transformation at Detecon.
AI is rapidly becoming embedded in the business world. Recent research from McKinsey & Company shows that more than nine in ten employees already use generative AI in their work.
However, clear rules, responsibilities and the necessary qualifications for its safe use are often lacking. Companies must therefore clearly define how they use AI, what data can be used, and who makes decisions and bears responsibility in the event of errors.
At the same time, many employees are concerned about how their data is handled and question who is liable for incorrect decisions made by AI systems.
The widespread use of AI, combined with unclear responsibilities, increases the pressure on organisations. Companies must provide guidance, clarify accountability and ensure acceptance. Structured change management provides the framework to analyse the impact on processes and roles and to guide employees effectively through the transformation.
Don’t overlook the focus on people
When implementing AI, many companies initially focus on technical aspects such as tool selection, system integration and IT security. Yet clear guidelines for daily work often remain unclear – for example regarding permissible use cases, data handling, and review and approval processes. This uncertainty can lead to acceptance issues and resistance among employees, ultimately reducing the intended benefits.
Efficiency gains only materialise when organisations not only develop the technology but also strategically evolve roles, processes, skills and collaboration.
A key success factor is fostering strong AI engagement among employees. Companies must involve employees early in the design process – for example through workshops, internal idea-generation initiatives or innovation labs. Continuous communication is equally important to ensure transparency: What goals does the company pursue with AI? What rules apply? What specific changes will occur in everyday work? This clarity strengthens trust and acceptance.
It is also crucial that companies manage their AI transformation as an iterative process. The first step is an assessment of digital maturity across teams, departments and hierarchical levels. Based on these insights, organisations can identify gaps between their current state and their objectives, and prioritise areas for action.
These insights can then be translated into a focused transformation roadmap, which should be managed through regular feedback sessions and measurable KPIs. These KPIs should capture not only hard metrics such as productivity gains, but also softer factors such as employee satisfaction and the development of an innovation culture.
Leaders as change enablers
Leaders play a central role in successful implementation. They prioritise use cases for their teams, translate strategic requirements into concrete working practices and ensure implementation in day-to-day operations. They also create space for co-creation and decide which suggestions are incorporated into processes, roles and routines. Crucially, leaders must take concerns seriously and establish clear guidelines for working with AI.
Three levers are particularly important:
Translation into practice: Which use cases are relevant for the organisation or the respective team? Which processes will change? And what does “good AI use” look like in everyday work?
Empowerment rather than tool training: Beyond technical training, organisations should also strengthen the skills that determine the quality of AI use. These include critical thinking, sound judgement, creativity and contextual understanding.
Embedding application: Leaders must create the conditions for learning and application to take hold in everyday work. This means reducing uncertainty, providing clear guidance and ensuring time, structure and clear responsibilities are in place.
To ensure training is both effective and efficient, the next step is to develop a clear competency perspective. Which skills already exist? Which will be needed in the future? And where are the gaps? This targeted shift in competency profiles – often referred to as the “skill shift” – becomes the next key element in the transformation process.
Actively shaping the skill shift
As AI adoption grows, the competency requirements of many roles are changing. Companies therefore face the challenge of supporting employees in developing these skills in a targeted way.
The starting point is a systematic skills analysis that captures the current level of competence and identifies which capabilities will be required in the future. Based on this, organisations can define development goals and learning pathways that align role requirements with individual potential – ideally through close collaboration between managers and HR development teams.
In practice, a combination of learning formats has proven effective. Traditional training programmes can be supplemented – or partly replaced – with more practical approaches such as on-the-job learning, project-based work or use-case-driven training. Peer learning and knowledge-sharing platforms can provide additional support by enabling the exchange of experiences and best practices in everyday work.
However, training alone is not enough to deliver a true skills shift. Companies must embed learning as a continuous process. Timing is particularly important: early training initiatives build confidence and can increase acceptance of new systems, as teams begin to see AI as a concrete opportunity for development rather than an abstract threat. This becomes a key success factor for the entire transformation.
Conclusion
The use of AI will continue to grow in the coming years. This makes it even more important to manage the associated changes systematically and with a human-centred approach – with clear responsibilities, well-defined guidelines and continuous learning embedded in daily work. Only then can AI be implemented across the organisation quickly, reliably and as a genuine competitive advantage.
