Between hysteria and euphoria: what AI really does to work
The honest answer is: AI is changing work – but it is not doing so in a one-dimensional way.
Both the dystopian vision of mass job losses and the promise of a fully automated utopia fall short of the mark. The reality is far more complex. In Enterprise Service Management in particular, it is already becoming apparent today that AI is less about replacing entire jobs and more about changing role profiles, responsibilities and ways of working.
Artificial intelligence primarily replaces repetitive tasks, not human labour. Service processes, knowledge management and cross-functional workflows are among the areas where AI is driving particularly visible changes. Above all, tasks that can be clearly described, are easily repeatable and are rule-based are being automated. At the same time, the focus is shifting to tasks that require ethical judgement, an understanding of context and communication skills.
Or to put it another way: whilst AI may relieve us of some of our work, it does not take on the responsibility.
This discussion is therefore not about a blanket reduction in jobs, but rather an intelligent shift in tasks, roles and skill sets. Anyone who today simply asks ‘Which jobs will disappear?’ is asking the wrong question. Rather, the issue is this: which work should be carried out by humans in future – and which is best suited to machines?
After all, numerous examples from the past have already shown that innovation does not have to be a ‘job killer’, but can offer new opportunities for existing roles. Many people still remember the bank tellers who were gradually replaced by self-service kiosks and ATMs – yet they were not made redundant, but successfully took on more responsible and less monotonous tasks, opening up new prospects.
New roles on the horizon: The top five future roles in the AI landscape
Analyses show that, with the increasing use of AI, new roles are emerging and existing ones are changing. In our view, five roles are particularly relevant, and these are already beginning to emerge in many organisations within Enterprise Service Management. The job titles may vary slightly.
1. AI Use-Case Owner / AI Product Owner & Value Lead
This role ensures that AI does not remain an end in itself. They prioritise use cases, are responsible for delivering business value and ensure that the technology creates genuine added value – in business, economic and organisational terms – whilst adhering to the company’s ethical values. They act as a bridge between business objectives and data/engineering implementation.
In practice, AI initiatives rarely fail due to a lack of ideas, but rather because of data availability, operational realities or a lack of scalability. This is why this role works closely with data, MLOps, architecture and governance functions, and this takes place within a currently highly agile context. Within the framework of Enterprise Service Management, this role is closely linked to the existing Service Owner.
2. AI Transparency Manager: Governance & Compliance
With increasing regulation (GDPR, EU AI Act, etc.), there is a growing need for clear responsibilities regarding the legal, ethical and organisational safeguards for AI.
In practice, this is rarely a single role, but rather a governance function: Legal, Compliance, Risk, Security, Data and AI Engineering, as well as line organisations, work together. There are often coordinating roles such as ‘AI Officer’ or ‘Responsible AI Lead’ – which currently have a predominantly technical focus – that centralise decision-making processes, documentation and control mechanisms. In terms of content, the organisation’s own regulations must also be taken into account, such as ethical guidelines, strategic directives and much more. Trust is not created by technical algorithms – but by demonstrating responsibility in practice.
3. Prompt & Interaction Designer
The quality of AI outcomes depends largely on the interaction. Dialogue design, prompt logic, guardrails and evaluation criteria determine whether co-pilots and agents operate reliably. In the field of AI ethics, this is also referred to as value-based engineering, which translates requirements into measurable system properties of the AI. Depending on the organisation, this essential skill set may be spread across several roles – such as product management, UX/service design, business analysis and engineering. What matters is not the job title, but the ability to design human–AI interaction in a systematic, reproducible, responsible and humane manner.
4. AI Knowledge & Information Management
AI is only as good as the usable knowledge base it draws upon. This role is responsible for the structure, timeliness and quality of knowledge sources – particularly in RAG and agent scenarios.
In practice, this means: clear ownership models, information architecture, metadata and taxonomy concepts, regulated ingestion and updating processes, quality metrics, and governance workflows that ensure compliance with data protection regulations such as the GDPR. Without these procedures, AI runs the risk of becoming an unreliable and untrustworthy basis for decision-making.
5. User Advocate for AI Change & Adoption Management
The biggest hurdle to AI roll-outs is rarely the technology itself. It is uncertainty, scepticism and a lack of direction. This role supports the change process, takes concerns seriously and ensures that AI is actually utilised in day-to-day work. Many organisations already have explicit roles for this purpose, often focusing on co-pilot, digital, workplace or process adoption. AI roll-outs are change projects – with everything that entails: communication, enablement, feedback and continuous learning. The role of traditional organisational change management is shifting significantly towards AI Change & Adoption Management, which is characterised by a more data-driven change lifecycle.
AI-related IT roles: in demand today – indispensable tomorrow
In the world of IT, entirely new disciplines do not emerge from thin air. Rather, existing roles are evolving – with clear prospects for the future:
AI Solution Architects, who integrate AI into existing IT and process landscapes
Data engineers and data stewards who ensure robust data foundations
MLOps and AI Operations specialists who operate, monitor and secure models
Automation and low-code experts who make AI productive
Security and risk specialists who ensure resilience, protection and the prevention of misuse
Following the implementation phase, the focus of these roles increasingly shifts from ‘integration’ to stabilisation, optimisation and scaling.
Surprisingly important: non-AI roles with a bright future
Paradoxically, it is precisely those roles that are not primarily technical that are gaining in importance:
Business analysts who identify meaningful use cases
Service and experience designers who shape human–AI collaboration
Project and programme managers who manage complexity
Organisational and process consultants who bring order to the system
Training and enablement specialists who empower people rather than overwhelming them
Because the more capable AI becomes, the more important the question becomes: How can people work effectively with it? What impact do autonomous and intelligent systems have on the various aspects of human wellbeing?
No need to rush! What organisations should consider calmly – beyond mere action for action’s sake
Five clear success factors can be derived from numerous projects and discussions:
Rethinking roles rather than cutting jobs
Work is changing – so roles must be adapted.
Develop skills in a targeted manner:
AI and data literacy are no longer specialist skills, but fundamental competencies.
Establishing ethical foundations, taking concerns seriously
Acceptance is built through transparency, participation and consideration of values.
View AI as part of the work system
Not as a peripheral tool, but as an integrated component of work.
Viewing transformation as an iterative task to be shaped by people:
AI is not a project with an end date – but a learning process on the path to the future.
Conclusion: Less fear, more shaping
AI will not do away with our work, but will provide a new, fresh context. However, it invites us to rethink work and to automate tedious tasks in the way we are already familiar with from numerous examples in everyday life. Companies that actively shape this transformation not only boost productivity but also create opportunities – for employees, managers and organisations as a whole. Or, to put it quite pragmatically: the future of work is not determined by algorithms, but by the way we utilise the possibilities offered by AI.
