Changing roles: Why cloud and AI are transforming our organisational models

Why roles are changing radically right now

Cloud and AI are developing rapidly, but many organisations are still working with role models from the host and silo world. While technology releases are possible every hour, outdated organisational structures, responsibilities and a lack of skills are slowing down real progress. This blog post shows why we need to rethink roles now and how clear objectives, governance and reskilling ensure that people control the new technology instead of being replaced by it.

Important to note: Staff reductions are not a natural law of transformation. They are often the result of delayed adaptation. Those who set the course early enough can mitigate the change.

Below, we highlight how organisations can close the “skills gap” through clear development paths and create structures in which cloud and AI are not seen as a threat, but as an integral part of new, future-proof profiles.

From traditional silos and role models to data-driven product teams

A look at our organisational and team structure shows what such structures can look like in practice: We are moving away from traditional functional silos towards a logic that focuses on the joint product. In the past, the specialist department defined requirements, IT implemented them and security ultimately gave the go-ahead. Today, these boundaries are blurring. Cloud platforms and AI tools relieve us of routine tasks and give us new freedom in which teams take on real end-to-end responsibility.

At the same time, the new organisational logic offers the opportunity to shape one's own role more actively.

It's about gradually integrating responsibility for issues such as costs (FinOps) or reliability (resilience) into everyday life. This is precisely where the added value lies: we no longer work side by side, but jointly design a living product, supported by AI agents that act like small additional teams.

So the key question is not which tool we buy, but how we create a culture in which everyone takes responsibility and helps shape change. This cultural change is particularly evident in our role models and job profiles.

Business and IT roles in transition – three profiles in a reality check

When products, teams and responsibilities change, job profiles cannot remain the same. Anyone working in a company today as a product owner, engineer or in security & governance will need a different understanding of their role in 2030 in order to remain relevant.

Let's take a reality check at these three core roles – with an honest look at the current situation in 2026 and the target situation in 2030.

From product owner to AI product owner

• CURRENT 2026: Today, in many companies, the product owner is primarily a translator between the specialist department and IT – strong in coordination and stakeholder management, but often weaker in data and technology issues. JIRA backlogs are also maintained manually, and AI use cases tend to run on the side.
• TARGET 2030: In 2030, this will evolve into an AI Product Owner who translates business problems into targeted data- and AI-based solutions, uses AI agents as a matter of course for analysis and backlog work, and has a thorough understanding of the rules of AI governance and data protection. Value is no longer created through ticket management, but through the orchestrated interaction of people, data and AI.

If we do not actively develop this, the manually managing PO will compete directly with AI assistants in 2030, and they have a clear advantage in terms of speed, consistency, and ‘backlog diligence.’ Value is created where we translate the possibilities of cloud & GenAI into concrete, regulation-compliant products with business added value. This is the new core task of the AI PO.

From developer to cloud engineer & platform engineer

• CURRENT 2026: Today, the classic developer implements features in the specialist system, while infrastructure, operation, security and costs are usually handled by other teams. CI/CD and automation are available, but not always used consistently, and GenAI tools for code or testing are more experimental than standard.
• TARGET 2030: In 2030, developers will be cloud-native engineers who work on platforms, use infrastructure as code, deploy AI agents on a daily basis for code, refactoring and testing, and share responsibility for FinOps, resilience and observability with the team. Development will then mean thinking about features, operations and quality together from the outset.

If we don't do this, teams will remain in classic ‘JIRA ticket factories’ in a platform world and will be overtaken by cloud-native competitors with AI-supported delivery pipelines in terms of time to market, quality and costs.

From security gatekeeper to embedded security & AI governance

• CURRENT 2026: Today, security acts as a gatekeeper at the end of the process in many organisations: concepts are reviewed, checklists are worked through, approvals are granted – often manually and based on documents, cloud and AI risks are addressed reactively on a case-by-case basis, and teams tend to see security as a hindrance.
• TARGET 2030: In 2030, security will be integrated early on into product teams as embedded security & AI governance, working with automated controls, continuous compliance and policies as code, and establishing clear guidelines for the responsible use of GenAI. Late-stage control will thus become an early enabler that brings together speed and regulation.

Roles such as AI governance lead or AI risk officer are becoming increasingly important. Those who do not actively develop this governance and the outlined target profiles will face an unattractive choice in 2030: slow down innovation – or run into technical, regulatory and personnel risks.

Skill transition: from role gap to development opportunity

After the reality check of the roles, the practical question arises: how do we get from today's profile to the 2030 target without losing people along the way? Instead of just inventing new job descriptions, we are refining existing profiles and highlighting how their maturity in terms of AI and cloud can be developed step by step.

To this end, an as-is/to-be role map is created: tasks, responsibilities, stakeholders and professional, technical and social skills are described in detail and transparently for each role. Based on already visible developments, such as Jira/Confluence agents that relieve product owners and business analysts of routine work, role-specific upskilling and reskilling plans are derived. It is determined which technological developments are relevant for the organisation and to what extent the respective roles should master them in the future. This creates a clear picture of where skills need to be developed. At the same time, decisions are made about which job profiles will still be needed in the long term and which will be phased out in the future. This results in a career path matrix that shows how someone in role A can develop into roles B, C or X – including the measures necessary to achieve this.

A concrete example: The evolution of the product owner to AI product owner

The steps to an AI-enabled role model:

• Starting point: Traditional product owner

  • Strong in moderation, prioritisation and stakeholder management
  • Predominantly manual backlog maintenance
  • Works with selected KPIs, but is only involved to a limited extent in the company's data landscape and AI initiatives
• Stage 1: Data-informed product owner

  • Deepens their data and KPI skills in order to confidently handle the growing volume and complexity of company data
  • Uses GenAI tools specifically to support research, formulation of user stories and acceptance criteria
  • Develops these skills through compact, practical training courses on prompting and AI-supported documentation (protocols, summaries, backlog building blocks)
• Level 2 / Target vision for 2030: AI Product Owner

  • Systematically translates business problems into data- and AI-based features and use cases
  • Works routinely with AI agents for backlog analysis, hypothesis formation, and impact assessment
  • Is familiar with the guidelines of AI governance and data protection and takes them into account in prioritisation and product strategy

Each target role includes clearly described learning steps, appropriate training opportunities and a realistic development period. Skill matrices make it clear which skills are required today and which competencies should be developed next. Employees need concrete opportunities for learning and experimentation – such as short, practical formats, lab settings, communities of practice or shadowing in AI use cases.

These development roadmaps are incorporated into employee reviews, in which, in addition to current performance, planned role development, learning goals and possible project changes are agreed upon. This makes it clear that the organisation is actively investing in the further development of its roles. It is crucial that this change is seen as an opportunity to further develop one's own role. Managers create the framework for this by prioritising learning time, enabling experimentation and openly addressing how profiles should change by 2030. In this way, the role target becomes lived practice and the skill gap becomes a manageable transition to the cloud and GenAI working world.

Conclusion: We must develop a target vision for 2030 NOW

The examples in the reality check show that roles are already changing. The question is whether we control this change or are driven by it. We know from our projects that around 70 per cent of the success of cloud and GenAI transformation does not depend on technology, but on organisation, people and culture – in other words, on whether roles and skills are suited to the new world.

So that you don't wait until the pressure to change becomes a crisis, we offer concrete starting points with which we accompany you at adesso:

• AI readiness & skills check: Together, we analyse how fit your key roles are for cloud & GenAI and where the most urgent need for action lies.
• Technology Impact Simulation & Mini-Transformation: We simulate what happens when AI agents are used productively: Which processes and roles change, and how does this affect personnel and organisational structures? We test these scenarios with you in a selected product or specialist area.
• Gap analysis for 3–5 key roles: Together with HR, IT and the specialist department, we will refine your core roles for 2030 and derive a pragmatic roadmap for your organisation of the future.

Our claim at adesso: We are already developing and testing the skills your organisation will need tomorrow, right here at your side. If you want to actively redefine role models rather than just manage them, let's talk – and together develop a target vision that will make your organisation competitive, resilient and attractive to talent in 2030.