Artificial intelligence, cybersecurity threats and cloud computing are changing technology careers. To remain valuable, IT professionals need more than product knowledge. They must understand how technology, people and business outcomes connect.
By Pentacept Editorial
There was a time when an IT professional could build an entire career around one product, system or technical specialism.
A network engineer managed the network. A support analyst resolved tickets. A system administrator maintained servers. A developer wrote code, while cybersecurity was often left to a separate security team.
Those boundaries have become increasingly difficult to maintain.
Cloud platforms have changed how infrastructure is delivered. Automation has reduced some forms of repetitive administration. Cybersecurity now affects almost every technology decision, while artificial intelligence is being embedded in software used across the workplace.
The result is not that every IT professional must know everything. That would be unrealistic.
What has changed is the need for a stronger set of transferable skills. Employers still need specialists, but they also need people who can learn unfamiliar technology, understand risk, automate routine work and explain technical decisions in business terms.
The World Economic Forum’s Future of Jobs Report 2025 found that AI and big data, networks and cybersecurity, and technological literacy were the three fastest-growing skill areas identified by surveyed employers.
The report also found that human capabilities such as analytical thinking, resilience, leadership and creative thinking would remain important.
Its findings were based on responses from more than 1,000 employers representing over 14 million workers across 55 economies. These are employer expectations rather than guaranteed labour-market outcomes, but they illustrate how quickly skills requirements are changing.
In the UK, a Skills England assessment of the digital and technology sector projects that employment demand across 30 priority occupations could grow by 239,000 jobs, or 27 per cent, between 2025 and 2035.
Skills England describes the projection as illustrative because rapid technological change and modelling limitations create substantial uncertainty. Even with that caution, the assessment suggests that technology careers are evolving rather than simply disappearing.
Against this background, these are five skills every IT professional should begin developing.
1. Artificial intelligence literacy and intelligent automation
AI literacy does not mean knowing how to build a large language model from the ground up.
For most IT professionals, it means understanding what AI can do, where it can fail and how to use it responsibly within a real organisation.
An IT support analyst might use AI to summarise an incident or search an approved knowledge base. A developer may use it to explain code, generate tests or identify possible errors. A project manager might use it to organise risks and actions, while a security professional could use it to review alerts or identify patterns.
The important skill is not simply writing prompts. It is knowing how to define a problem, provide the right context and evaluate the answer.
AI systems can produce inaccurate information with considerable confidence. They may omit important context, generate insecure code or recommend a solution that does not fit an organisation’s environment.
An IT professional should therefore learn how to:
- Break a complex problem into clear instructions.
- Compare AI output with reliable documentation.
- Check generated code, commands and configurations before using them.
- Recognise personal, confidential and security-sensitive information.
- Understand the difference between an AI assistant and an AI agent.
- Decide which parts of a task require human approval.
- Document how AI contributed to important work.
These responsibilities are consistent with the UK National Cyber Security Centre’s guidelines for secure AI system development, which emphasise secure design, development, deployment and operation.
AI agents make these controls even more important. Unlike a chatbot that produces an answer, an agent may be connected to tools and authorised to perform several related actions. It might search records, update a system and communicate an outcome.
That capability can save time, but it can also multiply an error. Giving an AI agent broad permissions without appropriate monitoring would be comparable to giving a new employee administrative access before completing identity checks, training and supervision.
The Linux Foundation’s 2026 State of Tech Talent Report found that 47 per cent of surveyed organisations perceived themselves as understaffed in AI and machine-learning engineering. The research also found that organisations were relying heavily on upskilling existing employees.
Not every IT professional needs to become an AI engineer. Ignoring AI completely, however, is becoming increasingly difficult to justify.
How to develop this skill
Begin with a low-risk work scenario. Use an organisation-approved AI tool to summarise public documentation, structure a troubleshooting guide or explain a non-sensitive technical concept.
Check every output against the original source. Record what the tool did well, where it failed and what additional information improved the result.
The objective is to develop informed judgement, not dependency.
2. Cybersecurity and risk awareness
Cybersecurity is no longer a skill required only by people with “security” in their job title.
A service-desk analyst verifies identities before resetting passwords. A developer must understand secure coding. A cloud engineer controls access and protects stored information. A project manager considers supplier risk, business continuity and data protection.
Nearly every IT decision can create or reduce security risk.
This does not mean every technology professional must become a penetration tester. It means that anyone working in IT should understand principles such as:
- Identity and access management.
- Multi-factor authentication.
- Least-privilege access.
- Secure configuration.
- Vulnerability and patch management.
- Encryption and data protection.
- Phishing and social engineering.
- Logging and monitoring.
- Incident identification and escalation.
- Backup, recovery and business continuity.
- Third-party and supply-chain risk.
The strongest security professionals do more than recognise technical weaknesses. They understand which business services depend on a system, how an incident could affect people and which controls are proportionate to the risk.
The 2025 ISC2 Cybersecurity Workforce Study was based on responses from 16,029 cybersecurity practitioners and decision-makers across several regions.
ISC2 found that skills shortages had direct operational consequences. Its analysis of the findings reported that 88 per cent of respondents had experienced at least one significant cybersecurity consequence associated with a skills deficiency within their team.
The Linux Foundation’s 2026 research also found that 40 per cent of surveyed organisations perceived themselves as understaffed in cybersecurity and compliance.
These findings should not be interpreted to mean that completing one cybersecurity course guarantees employment. They demonstrate that organisations need people who can combine technical understanding with practical risk management.
How to develop this skill
Start with the systems you already support.
Learn how users are authenticated, how access is approved, where logs are stored and what happens when a device or account is compromised. Review a real security incident or a publicly available case study and identify which controls failed.
If you work in support, learn identity security and incident escalation. If you write software, study secure development and common application vulnerabilities. If you work in cloud services, focus on permissions, network controls, secrets, logging and configuration.
Cybersecurity becomes meaningful when it is connected to the work you already do.
3. Cloud computing and infrastructure fundamentals
Learning cloud computing should involve more than memorising service names before a certification examination.
Cloud platforms have made infrastructure faster to deploy, but the underlying principles have not disappeared. Applications still depend on computing, storage, networking, identity, databases, availability and recovery.
An IT professional who knows how to select options in a cloud portal but does not understand networking, permissions or resilience can create an expensive and insecure environment very quickly.
A useful cloud foundation should include:
- Virtual machines, containers and serverless computing.
- IP addressing, DNS, routing, firewalls and load balancing.
- Storage types, databases and backup.
- Identity and role-based access control.
- Availability zones, regions and disaster recovery.
- Monitoring, logging and performance.
- Infrastructure as code.
- Cloud costs and resource optimisation.
- The shared-responsibility model.
- Hybrid and multi-cloud environments.
The Linux Foundation’s 2026 State of Tech Talent Report found that 29 per cent of surveyed organisations perceived themselves as understaffed in cloud computing. It also identified perceived shortages in platform engineering and financial operations, commonly known as FinOps.
That last point is important. Cloud knowledge is no longer limited to deploying infrastructure. Employers increasingly need professionals who can explain what a service costs, whether the design is efficient and how usage can be controlled.
An organisation can move a poorly designed system into the cloud and end up with the same design problems and a larger bill.
How to develop this skill
Choose one major platform, such as Microsoft Azure, Amazon Web Services or Google Cloud, and build a small environment.
Create a virtual network, deploy an application, configure access, enable logging and design a backup process. Then remove the environment and recreate it using Terraform or another infrastructure-as-code tool.
Do not stop after achieving a working deployment. Ask:
- Is it secure?
- Is it resilient?
- Can another person understand it?
- How will it be monitored?
- What will it cost?
- How will it be recovered?
Those questions turn a laboratory exercise into professional learning.
4. Scripting, automation and data literacy
Repetitive technical work should not always remain manual.
IT teams regularly create user accounts, analyse logs, move files, check services, prepare reports and update devices. Performing these tasks manually may be manageable at a small scale, but it consumes time and increases inconsistency as an organisation grows.
An IT professional does not need to become a full-time software developer to benefit from automation.
Depending on the environment, useful skills may include:
- PowerShell for Microsoft and Windows administration.
- Python for automation, data processing and integration.
- Bash for Linux and cloud environments.
- SQL for querying structured data.
- APIs for connecting systems.
- Git for version control.
- JSON and YAML for configuration.
- Excel or business-intelligence tools for analysis and reporting.
The aim is not to automate everything. It is to recognise tasks that are repeatable, rules-based and suitable for automation.
A good automation process should be understandable, tested and reversible. It should include appropriate logging and error handling. It should not allow a script or AI-generated command to make uncontrolled changes across a production environment.
Data literacy is equally important.
IT professionals are surrounded by data, including incident volumes, device performance, response times, vulnerabilities, service availability, user behaviour and project-delivery measures.
Knowing how to collect, question and explain that information enables a professional to move beyond completing tasks and begin improving services.
Reporting that a team closed 1,000 tickets says very little by itself. A stronger analysis might identify why password incidents increased, which business unit was most affected and whether a self-service change reduced demand.
This is where technical work begins to influence operational decisions.
How to develop this skill
Select one repetitive task you currently perform and document every step before attempting to automate it.
Build the solution in a test environment. Add validation, error handling and logging. Ask someone else to review it and write instructions that another team member can follow.
For data literacy, obtain a non-sensitive dataset and use SQL, Python, Excel or a reporting tool to answer a practical question.
Do not begin by trying to create an impressive dashboard. Begin with a clear decision that the data should support.
5. Communication and business problem-solving
Technical ability can help an IT professional enter the room. Communication and business understanding often determine how much influence that person has once inside.
Technology teams do not exist merely to operate technology. They support services, employees, customers, patients, students, revenue and organisational objectives.
A technically correct solution can still fail if it does not address the real problem, if users cannot understand it or if decision-makers cannot see its value.
IT professionals should be able to:
- Ask useful questions before proposing a solution.
- Explain technical risk without unnecessary jargon.
- Write clear emails, reports and technical documentation.
- Listen to users rather than dismissing their concerns.
- Present alternatives, costs and consequences.
- Communicate confidently during incidents.
- Manage expectations and disagreements professionally.
- Translate technical work into measurable business outcomes.
Consider the difference between these two statements:
The server experienced resource saturation caused by an unoptimised process.
And:
The application slowed down because one process consumed most of the available memory. We have contained the immediate issue and are testing a permanent change to prevent it from happening again.
Both statements may be technically correct. The second helps a non-technical audience understand what happened, what has been done and what comes next.
The World Economic Forum’s skills outlook identifies analytical thinking as a leading core skill. It also expects creative thinking, resilience, leadership and social influence to remain important alongside AI, data and cybersecurity skills.
This is not evidence that technical knowledge matters less. It shows that technical knowledge becomes more useful when professionals can apply and communicate it effectively.
How to develop this skill
After completing technical work, practise explaining it in three ways:
- To a technical colleague who needs implementation details.
- To a manager who needs to understand the risk, cost and expected outcome.
- To an affected user who needs to know what will change.
Participate in meetings, write knowledge articles and volunteer to present completed work. Ask for feedback on clarity, not only technical accuracy.
Communication improves through use, not intention.
Certifications can help, but evidence of ability matters
Certifications remain useful. They can provide structured learning, introduce industry terminology and help candidates pass an initial recruitment screening.
Collecting certificates without building practical ability, however, can create a false sense of readiness.
An employer may be interested in a cloud certification, but the interview will eventually reveal whether the candidate understands networking, permissions and recovery. A cybersecurity certificate may open a conversation, but the candidate must still explain how they would respond to a real incident.
Evidence of learning may include:
- A properly documented home laboratory.
- An automation script with error handling.
- A cloud environment built using infrastructure as code.
- A security risk assessment.
- A technical article or presentation.
- A data-analysis project.
- A record of improvements delivered in an existing role.
Certificates show that someone completed a learning programme. Practical evidence demonstrates how that knowledge can be applied.
Do not try to learn everything at once
The five skills in this article are broad. Attempting to master all of them simultaneously would probably result in shallow learning and unfinished courses.
A better approach is to build a strong foundation and then develop depth in the area most relevant to your career.
A service-desk analyst might begin with PowerShell, identity security and clear incident communication. A developer may prioritise secure coding, cloud deployment and AI-assisted development. A project manager might concentrate on technology risk, data analysis and communicating business outcomes.
The right learning path depends on the destination.
What should remain consistent is the habit of continuous learning. Tools and product names will change. Some technologies that dominate today will eventually be replaced.
The ability to understand a problem, learn the relevant system and make a sound decision will remain valuable.
The future belongs to adaptable professionals
Technology careers are not becoming less relevant. They are becoming less forgiving of professionals who stop learning.
AI will automate parts of technical work. Cloud platforms will abstract more infrastructure. Security expectations will increase, and businesses will continue demanding faster results from technology teams.
The answer is not to chase every new tool or collect every available certification.
It is to develop a combination of technical depth, automation ability, security awareness, data confidence and communication.
An IT professional who can use AI responsibly, understand cloud infrastructure, protect information, automate repetitive work and explain the business impact of a decision offers more value than someone whose knowledge is limited to following instructions within one product.
The tools will continue to change. The real career advantage is learning how to change with them.
Editorial note: This article combines professional analysis with findings from international and UK workforce research available as of 3 August 2026. Survey findings describe respondents’ experiences and expectations and should not be treated as guarantees of employment.
