It's a New Year and often time to think about your future; where do you see It's a New Year and often time to think about your future; where do you see yourself in 1, 5, or even 10 years’ time? How is the technology landscape changing?
What does this mean for you? Are you concerned about how AI might impact your own career or employment opportunities? Now, more than ever, is a great time to think about what technology skills you should consider investing your time in learning for 2026.
It’s an exciting time to be in technology, so now is the best opportunity to make that investment in your future.
Programming and Development
Perhaps the first thing to do is to talk about programming; 2025 has been described by various commentators as “the year that changed programming forever!”
Perhaps we will see exactly how true that statement is in 2026, but there can be no doubt that the programmer’s toolbox has been hugely updated with the advent of AI Coding assistants.
In some ways, this is just another step in the evolution of how we create programs. The furore around AI coding assistants is akin to several fundamental shifts that the average computer programmer might have seen across the last 70 or so years: we have had the shift from machine code to higher-level languages, from simple text editors to Integrated Development Environments (IDEs), from compiled languages to interpreted languages, and so on. Each one has brought with it those who said, "That's the end of coding as we know it." Perhaps they have been correct in a way, but they have failed to see this as part of an evolution of how we create programs. Do AI Assistants mean the end of coding? Perhaps, but this does not mean it is the end of programming per se, or of the developer.
Instead, developers, programmers, and software engineers need to shift the way they work from writing routine code to focusing on higher-level concepts, issues, and ideas. However, it also means that they need to develop knowledge, skills, and experience with these AI Assistants, so that coding with them becomes as natural as using an IDE is now.
Upskilling on the use of AI Assistants—how to maximize their benefits but also understanding what the developer now needs to focus on—will be paramount. You should learn to effectively augment your coding skills with Large Language Models (LLMs) such as ChatGPT, Copilot, Gemini, or DeepSeek Coder. Therefore, being comfortable with AI Pair Programming will be a boost.
Cloud Computing
Cloud Computing is not going away, so upskilling in cloud technologies will be essential for anyone working in the IT sector, even if you are not directly managing systems in the cloud. Understanding how they work, how applications are deployed, what technologies are suitable for the cloud, and of course, how security works in the cloud, are all necessary skills.
In addition - and we are going to keep coming back to this topic - AI will have a significant impact on cloud-based systems. For example, so-called AI-native architectures deployed to cloud infrastructure will become more and more significant. In many cases, this drives the demand for multi-cloud and multi-vendor solutions which aid scalability and avoid vendor lock-in. Thus, being familiar with more than one cloud architecture will be very important.
As well as being able to host AI systems, AI will impact the cloud systems themselves, with solutions being used to configure and monitor systems, dynamically manage shifting compute demands, provide enhanced security, and avoid wasted expenditure on unnecessary cloud infrastructure. Once again, having skills and knowledge within this area will be essential for many organizations.
Data Science and Analytics
As in almost every technology sector in 2026, core data science fundamentals (such as programming in R (Rlang) and Python, statistics, and database access) will need to be blended with emerging technologies utilizing AI. In addition, cloud infrastructures as well as what has been called MLOps (or Machine Learning Operations) will be of increasing importance.
In terms of traditional programming skills, Python and its associated libraries such as Pandas, NumPy will continue to be key. For statistics and mathematics, probability, linear algebra, and calculus are key. In terms of Machine Learning, regression, classification, and clustering algorithms alongside deep learning are essential. For data visualization, skills in tools such as Tableau and Power BI will continue to be very useful.
However, the most exciting area for 2026 in data science is probably around the use of further AI and generative AI tools. The combination of a chatbot experience with Data Science analytics will open up far more flexibility and potential than the average dashboard has so far been able to offer.
MLOps represents a set of practices that automate the entire Machine Learning cycle, from data gathering through to deployment and integration into existing pipelines. It applies DevOps principles to ML, focusing on collaboration, automation (CI/CD), continuous integration, testing, monitoring for model quality, handling data drift, and ensuring governance.
Security
The huge juggernaut that is AI continues to thunder across the technology world, and those concerned with security are being dragged along in its wake. This is not going to change in 2026, and the security of these systems will only become an increasingly important factor within any organization.
Being prepared to adapt to the new AI-centric world will be a key feature of the successful security-focused individual in 2026. This means not just ensuring that these AI-based systems are secure, but that they are also trustworthy. If those AI systems possess a learning component, then there will be a need for continuous validation (rather than the "one-off" testing of more traditional systems).
Those working in the security of AI systems will therefore have to be not only technologists but also auditors and adversarial thinkers to consider what it means to be a "secure" AI system.
Knowledge of traditional infrastructure security, including cloud-based systems, will still be required. However, understanding new protocols and their implications will be vital. For example, Model Context Protocol (MCP) is used to enable a supply chain of data to an LLM. This LLM supply chain may be made up of plugins, third-party components, or datasets, and can make an LLM susceptible to vulnerabilities that can affect the integrity of the training data, models, and host platforms. This can result in biased outputs, security breaches, or system failures.
Building appropriate skills should also involve knowledge of the OWASP Generative AI Security Project.This is a global open-source initiative dedicated to identifying, mitigating, and documenting security and safety risks associated with generative AI technologies.
It includes the Top Ten security risks for LLMs which anyone working on these systems should be aware of.
DevOps and Automation
There is a general need for senior DevOps people, senior cloud specialists, security specialists, and Site Reliability Engineers (SREs). Thus, developing your skills in any of these areas will be of significant benefit in 2026.
During 2025 (and of course before that) there have been significant outages that have impacted organizations and people's ability to do what they need to do. In many cases, these outages have been identified as having been caused by issues in deployment or infrastructure management. These outages have both a reputational and a financial impact. As customer tolerance for such outages decreases, the need for SREs - who blend software engineering and system administration to help build and manage highly reliable, scalable, and efficient systems - will only increase. Such engineers bring skill sets from a variety of disciplines together, including automation tools such as Kubernetes and Docker, systems administration, and operating systems.
The need for DevSecOps will be a significant force behind the need for enhanced skills within the DevOps sector; the need for better and faster security will not go away. Thus, being able to adapt to this developing skillset will be an important differentiator in 2026.
Finally, AI-Driven Automation - which combines AI with traditional automation to handle repetitive tasks - will continue to develop in 2026. Again, knowledge of the tools and techniques involved will be important if you wish to move into this area.
Large Language Models (LLMs)
LLMs are the core aspect of the artificial intelligent agents being used in everything from your online banking website to your Python IDE, search engine results, and messaging apps! They are everywhere. It seems only a few years ago there were a few people muttering about ChatGPT and its potential, and now you can’t get away from it!
Approaching LLMs, there are two potential avenues regarding employability: one is to understand and be able to work on how LLMs work; the other is how to apply LLMs to particular tasks. Both have significant value, but they involve different skillsets (although understanding both sides of the coin is beneficial).
Exploring running LLMs locally, understanding concepts like embeddings, Retrieval-Augmented Generation (RAG) allowing real-time external data such as company documents to be accessed), as well as core frameworks such as LangChain or Ollama will be very useful.
Summary
Continuous learning is key to all of the above. Whether that is from books, online courses, interactive training, or in-person classes, keeping up to date with technology will be extremely important. It is worth noting that the rate of change in technology currently has never been higher, with new tools, technologies, frameworks, and approaches appearing all the time.
Obtaining certifications and course accreditations is one way to show what you have achieved. Another is to be able to show hands-on experience, if not with your current employer, then using known projects potentially on sites such as Kaggle or through your own personal experiments.
Adaptability will be a very important attribute over the coming year. Everyone needs to embrace new tools (and AI tools in particular) as well as new ways of working as these evolve and mature.