What does AI mean for the organizational structure?
The use of Artificial Intelligence (AI) changes not only technology but especially collaboration, responsibilities, and decision-making paths. Successful AI projects in companies require clear responsibilities, suitable workflows, and an organization that supports changes. Good news for SMEs: Targeted adjustments to existing structures and processes are often sufficient – a complete overhaul is rarely necessary.
What does AI mean for the organizational structure?
Why is the organization important?
Smaller companies often coordinate AI through a responsible person, while larger ones benefit from a central AI team. Both work if responsibilities are clear and management actively supports.
Organization is not a one-time project but an ongoing process – closely linked to specific AI use cases. As requirements change, processes and responsibilities should be regularly reviewed and adjusted.
The organizational question becomes particularly relevant when you:
start first AI projects,
involve several areas (e.g., sales, customer service, IT),
change workflows through AI,
create new tasks or roles,
want to permanently anchor AI,
coordinate multiple AI projects simultaneously.
The earlier organizational questions are resolved, the easier it is to implement AI projects in practice.
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Maria Danninger
Accenture
Next Gen Robotics GTM Lead
"The next generation of AI robots combines advanced intelligence, innovative form factors, and autonomous learning ability to tackle increasingly complex tasks in dynamic environments. In digital transformation, they become a key driver of innovation and competitive advantage for companies. The BAIOSPHERE AI-COMPASS can also provide valuable insights and practical recommendations for action in this regard."
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Dr. Stefanie Demirci
VDI/VDE Innovation + Technik GmbH
Deputy Head of Digital Transformation Technologies
"For the successful use of AI applications in companies, a work culture open to AI innovations is essential. Ideally, this is characterized by cross-functional collaborations and the establishment of appropriate processes for data and risk management. However, it is also important to establish these structures before the introduction or development of the AI application, not after."
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Thomas Hahn
Siemens AG
Siemens Fellow and Member of the Bavarian AI Council
"Medium-sized businesses are the backbone of the Bavarian economy. With the BAIOSPHERE AI-COMPASS, the Bavarian AI Agency helps every company to independently form an opinion and strategy on the use of future AI technology. The compass helps with technical questions, organization, and regulation and also offers networking opportunities – whether for information exchange, advice, or testing."
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Kathrin Schwan
Accenture
Managing Director, Data & AI Lead Austria, Switzerland, and Germany
"AI is more than a "new" technology; it changes our previous way of working. This must also be reflected in the organization of every company – with new processes, procedures, and structures. Companies of all sizes will in the future have to ask themselves how to shape this change on different levels, to ensure their viability, both internationally and at the Germany location.The BAIOSPHERE AI-COMPASS can serve as an orientation aid here."
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AI Readiness Test
Test now!How do I proceed?
Prepare the organization for the introduction of AI in 5 steps:
Step 1 – Take stock of the organizational starting position
Clarify with your team: Is there a responsible person/an AI team? Are responsibilities and decision-making paths defined? Do departments work together? Example: A medium-sized company plans AI in customer service and realizes that sales, service, and IT have so far acted separately. Note how much time is lost due to coordination efforts – so you can see where there is organizational action needed.
Step 2 – Set a concrete, measurable goal
Precise instead of vague: Not "We want to better position ourselves for AI," but "A responsible person is named in 6 weeks, and sales, service, IT have coordinated the process for the first AI pilot project." This makes progress checkable and priorities visible.
Step 3 – Select use case and adapt processes specifically
Start small and specific: Choose a narrowly defined use case (e.g., AI assistant in a team) instead of overhauling the entire organization. Check which steps are changing and adapt processes where AI takes over or supports tasks. Consider new roles and competencies (see Talents & Competencies). Avoid cross-departmental large projects at the beginning – high costs, high risk.
Step 4 – Define responsibilities and organize collaboration
Determine who coordinates the use of AI (in SMEs often one person, in larger companies a central AI team). This entity plans the timeframe, collects feedback from the areas, and reports to management – which sets priorities and secures resources. Ensure regular coordination between sales, service, IT, and transparent information channels. This increases acceptance and accelerates decisions.
Step 5 – Evaluate after 4 to 8 weeks and further develop the organization
Plan a review appointment directly from the start: Was the goal from step 2 achieved? Have collaboration and decision-making paths improved? Record insights and decide: expand, refine or change course. Repeat this review regularly – AI continues to evolve, and so should your organization.
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AI Readiness Test
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