AI is changing what ICT professionals do. Are you ready for that shift?
Artificial intelligence is not replacing them, but it is fundamentally changing how they work and which skills are required.
Find out what this means for your organisation or training programme.
A structural shift with clear opportunities
The impact of AI on ICT roles is profound and lasting.
Less focus on execution.
Greater emphasis on management, quality control and AI integration.
Stronger collaboration between IT and business.
Increased need for ‘human skills’ such as communication, critical and problem-solving thinking, and a learning mindset.
Greater focus on reliability, security and governance.
A more explicit focus on business value.
This shift presents enormous opportunities. Without adaptation, quality issues, poor AI choices and skills mismatches will quickly arise.
Five ICT profiles undergoing major transformation
This shift affects all ICT profiles. For five of these, we have outlined the evolution in detail.
Definition: Designs, builds, tests and maintains software applications, individually or as part of a team.
Related roles: Software Engineer, Application Developer
From: writing and debugging code yourself
To: AI-driven engineering
Key developments:
- Less building yourself, more controlling, monitoring and integrating AI
- Testing, reviews, security and maintainability are becoming more critical than ever
- Greater focus on system design and architecture
- Understanding of business logic and problem definition becomes more important
- Integrating AI into applications is becoming standard practice
- Mindset: curious, critical, flexible and responsible
Definition: Analyses complex data to gain insights and build predictive models; combines statistics with machine learning.
Related roles: Advanced Data Analyst
From: building and analysing models
To: integrating, validating and operationalising AI solutions
Key developments:
- Focus on data readiness and building reusable data assets
- Greater integration of existing AI and foundation models
- Responsible AI use: explainability, quality, bias and privacy
- Stronger focus on business value and selecting the right use cases
- Working in multidisciplinary teams
- Critical thinking as a key differentiator
Definition: Manages cloud infrastructure, implements cloud solutions and ensures optimal performance and security.
Related roles: Cloud Operations Engineer, Cloud Infrastructure Engineer
From: managing cloud infrastructure
To: running AI-ready platforms and ensuring reliability
Key developments:
- AI assistance in scripts, Infrastructure as Code and operations, with a focus on control and reliability
- Running AI workloads
- AI-supported observability
- FinOps for AI: GPU/TPU optimisation
- Extended security responsibilities
Definition: Manages technical products and works with engineering teams and stakeholders.
Related roles: Product Owner
From: managing, prioritising and delivering requirements
To: steering AI roadmaps, creating value and managing risks
Key developments:
- Use of AI to accelerate roadmapping, scenario planning and prioritisation
- Use of AI-powered prototyping and design tools to rapidly validate UX and workflows
- Greater accountability for the AI roadmap, KPIs and focus on real value over proof-of-concepts
- Governance embedded: risks, ethics and security included in product decisions
- Strengthened bridge role between business and engineering, with early involvement and faster iterations
Definition: Manages IT systems and infrastructure and ensures continuity and security.
Related roles: SysAdmin, IT Administrator, Network & Systems Administrator
From: manually managing systems
To: managing AI-supported operations and ensuring governance
Key developments:
- Less manual work, more automation
- AI-supported monitoring and triage
- Extended security responsibilities
- Greater focus on standardisation and reliability
- Key role in enabling AI tools
Are you an Agoria member or a training provider, and would you like to receive the detailed role analyses and competency evolutions for these 5 ICT profiles?
What does this mean in practice?
Companies
- Upskilling in AI-supported workflows
- Strengthening architecture, validation and governance
- Facilitating cross-functional collaboration
- Building AI-ready infrastructure and platforms
Training providers
- Integrating AI-supported development practices into curricula
- Strengthening critical thinking and responsible AI use
- Including AI infrastructure and operations
- Linking data and artificial intelligence to concrete business cases
Artificial intelligence creates opportunities for stronger ICT roles. Organisations and training programmes that adapt today will make the difference tomorrow.