Top 10 tech trends in 2026

Technology is changing fast, and with it comes new challenges and opportunities for businesses and job seekers. Every year, we see emerging tools and systems that shape the way we work, hire, and grow.

This year, we’ve looked at the latest insights from Gartner Top 10 Strategic Technology Trends for 2026 to explore where the industry is heading next. These trends highlight how artificial intelligence, data, and security continue to evolve, and what that means for employers building teams around them.

In this blog, we’ll explain what each trend means, why it matters in 2026, and how it’s likely to affect both hiring teams and candidates in the tech industry.

If you’d like to see how this compares with previous years, take a look at our top tech trends in 2025.

1. AI-native development platforms

Artificial intelligence is reshaping the way software is created. In 2026, development environments are becoming AI-native, meaning they are built around intelligent tools that plan, code, and test alongside engineers. For employers and developers, this shift is transforming what “software development” looks like day-to-day.

What is AI-native development?

AI-native development refers to platforms and workflows that have artificial intelligence built in from the start. These systems go beyond simple code suggestions to manage entire stages of the development cycle. They can translate product ideas into project tasks, write and review code, generate tests, and help teams deliver updates more efficiently.

The goal is to let AI handle repetitive or time-consuming work so that engineers can focus on solving complex problems. An AI-native environment can, for example, take a written feature request, build the first version of the code, test it for bugs, and prepare it for release. This combination of automation and oversight is changing what technical teams can achieve in shorter time frames.

Why it matters in 2026

In 2026, speed and precision are key priorities for most tech teams. AI-native platforms help companies ship products faster while maintaining code quality and compliance. The growing use of multiagent systems and domain-specific language models means these platforms can now understand context better, learn from previous projects, and adapt to new requirements.

For many organisations, adopting AI-native development is not about replacing developers. It is about scaling output, reducing bottlenecks, and making engineering work more data-driven. As the technology matures, businesses will measure success using metrics such as AI-assisted productivity, test coverage, and time-to-deploy.

What this means for hiring teams

Hiring teams will need to think differently about technical roles. Traditional job descriptions that focus only on programming languages or frameworks will start to feel outdated. New roles are emerging that combine software engineering with AI operations, governance, and product management.

When assessing candidates, look for people who can demonstrate how they have applied AI tools within real projects. Skills in retrieval-augmented generation (RAG), policy-as-code, and model evaluation are becoming essential. It is also worth looking for experience in testing and validating AI outputs, as these skills will be vital in maintaining quality and compliance.

What this means for candidates

For developers and engineers, this shift opens new career opportunities. Employers want people who understand how to work effectively with AI rather than compete against it. If you can show that you know how to collaborate with AI systems to improve speed, accuracy, or product quality, you will stand out.

Focus on building hands-on experience with tools such as GitHub Copilot Workspace or similar enterprise-level AI platforms. Learn how to audit and evaluate AI-generated code, and stay aware of data privacy and compliance principles. Being able to explain how you use AI responsibly and efficiently will make your skills more relevant to future employers.

Most popular job titles in AI-native development:

• AI platform engineer

• AI product manager

• Prompt and tooling architect

• ML evaluation engineer

• Policy-as-code specialist
  
  

2. AI supercomputing platforms

The growing demand for artificial intelligence has pushed computing power to its limits. As models become more complex, traditional cloud systems are no longer enough to handle the scale required. This is where AI supercomputing platforms come in. They provide the speed, efficiency and capacity needed to train and deploy advanced AI systems at scale.

What are AI supercomputing platforms?

AI supercomputing platforms are large-scale computing systems designed specifically for artificial intelligence workloads. They combine powerful processors, high-speed networking, and energy-efficient cooling to support the massive calculations needed to train and run AI models.

Unlike standard data centres, these platforms are optimised for parallel processing. They allow thousands of GPUs or specialised chips to work together on a single task, enabling faster model training and real-time inference. In short, AI supercomputing gives organisations the infrastructure to manage the most demanding AI operations efficiently and securely.

Why it matters in 2026

In 2026, the demand for AI computing power continues to grow as businesses move from experimentation to production. The introduction of more sophisticated models, such as domain-specific LLMs, is driving the need for greater capacity and reliability.

Energy use and sustainability are also key considerations. Many organisations are turning to liquid cooling and energy-aware design to manage costs and reduce environmental impact. AI supercomputing platforms are helping companies balance performance with responsibility, making large-scale AI projects more accessible to both public and private sectors.

For most businesses, AI supercomputing is becoming a competitive advantage. Those that invest early will have the resources to develop, train and deploy advanced models faster than their competitors.

What this means for hiring teams

For hiring teams, this trend brings new technical and operational requirements. Building or using AI supercomputing platforms requires a blend of skills in cloud engineering, high-performance computing and AI infrastructure management.

Hiring will increasingly focus on specialists who can optimise workloads across GPUs, manage data pipelines at scale and improve energy efficiency. Candidates with experience in MLOps, GPU scheduling, or cluster management will be highly sought after. There will also be a growing need for professionals who can bridge the gap between data science and infrastructure, ensuring that models are deployed efficiently and securely.

What this means for candidates

For candidates, AI supercomputing opens new career paths in infrastructure, operations and AI engineering. The market will need engineers who understand both hardware and software performance, as well as those who can build scalable systems that support AI workloads.

If you’re looking to future-proof your skills, it’s worth gaining experience with technologies like NVIDIA CUDA, distributed training frameworks and data pipeline orchestration tools. Familiarity with energy-efficient computing and cooling systems will also be an advantage as companies aim to balance performance with sustainability.

Candidates who can demonstrate hands-on experience with large-scale AI environments or high-performance computing setups will stand out in 2026.

Most popular job titles in AI supercomputing:

• MLOps engineer

• AI infrastructure engineer

• GPU systems architect

• Cloud performance engineer

• AI capacity planner
  
  

3. Confidential computing

Data security has always been a priority, but with the rise of artificial intelligence and cloud computing, protecting information during processing is now just as important as protecting it at rest or in transit. This is where confidential computing comes in. In 2026, it will become a key part of how businesses keep their data safe while using AI at scale.

What is confidential computing?

Confidential computing is a method of protecting sensitive data while it is being processed. It works by isolating the data inside a secure part of a computer’s hardware known as a Trusted Execution Environment (TEE). Within this space, the data is encrypted and cannot be accessed or viewed by anyone outside, including cloud providers or system administrators.

This means organisations can run workloads that involve personal, financial, or classified information in the cloud without exposing it to potential risks. It helps ensure that AI models and applications using confidential or regulated data remain compliant and secure from the inside out.

Why it matters in 2026

In 2026, more organisations are adopting cloud and AI systems that rely on large volumes of sensitive data. As data privacy regulations continue to tighten across the UK and Europe, confidential computing provides a practical way to protect information during use.

Businesses in healthcare, finance, government, and recruitment are already exploring confidential computing to support privacy-preserving analytics, machine learning, and cross-organisation data sharing. It enables collaboration between companies without compromising data security or breaching compliance laws.

As trust becomes a deciding factor for clients and users, confidential computing helps businesses prove that their systems are safe and compliant by design.

What this means for hiring teams

For hiring teams, confidential computing introduces a growing need for professionals who understand secure data handling in modern environments. Technical roles are expanding to include expertise in encryption, TEE deployment, and privacy engineering.

When recruiting, look for candidates who have experience working with secure enclaves, hardware-based security modules, and confidential virtual machines offered by providers like Microsoft Azure, Google Cloud, or AWS. You may also start to see demand for roles that combine cyber security and machine learning, as AI projects increasingly rely on protected data pipelines.

Hiring teams should also consider upskilling existing employees, as knowledge of confidential computing will soon become a baseline expectation for cloud and security specialists.

What this means for candidates

For candidates, confidential computing is an area worth learning about if you want to future-proof your career in cyber security, cloud computing, or data engineering. Understanding how secure enclaves and encrypted workloads operate can make you a stronger candidate for roles in both enterprise IT and AI-driven businesses.

If you are currently in a technical role, gaining hands-on experience with cloud security tools and compliance frameworks such as ISO 27001 or GDPR will help you stand out. For those in data roles, demonstrating awareness of how confidential computing fits into responsible AI practices will show employers that you can manage risk as well as performance.

Most popular job titles in confidential computing:

• Cloud security engineer

• Privacy engineer

• AI compliance specialist

• Confidential computing architect

• Security operations analyst
  
  

4. Multiagent systems

Artificial intelligence is moving beyond single-purpose tools. Multiagent systems represent the next stage, where multiple AI agents work together to plan, reason, and complete complex tasks. In 2026, these systems are helping businesses automate full workflows rather than isolated steps.

What are multiagent systems?

A multiagent system is a network of independent AI agents that communicate and collaborate to reach shared goals. Each agent has its own role or capability, such as retrieving data, generating text, analysing information, or making decisions based on set rules.

Together, these agents can perform tasks that would be too large or complex for one model to manage alone. For example, a customer-service platform might use one agent to analyse a query, another to find the right data, and a third to generate a response that follows company policy.

In short, multiagent systems allow organisations to build AI ecosystems that operate more like teams than standalone tools.

Why it matters in 2026

In 2026, businesses are looking for AI that can work across departments and systems. Multiagent setups make that possible. They can coordinate across CRM, ERP, and analytics platforms, giving teams real-time insights and automating repetitive decision-making.

This approach also improves accuracy. By dividing work between specialised agents, each one can focus on what it does best, leading to faster and more reliable results. Multiagent systems will become central to enterprise AI projects, from financial forecasting and logistics to HR automation and content generation.

What this means for hiring teams

For hiring teams, this trend introduces demand for new hybrid skill sets. Employers will need engineers who can design, test, and maintain multiagent workflows. Roles will blend elements of software engineering, AI orchestration, and system architecture.

When hiring, focus on candidates who understand prompt coordination, API integration, and agent supervision. Look for experience with frameworks such as AutoGen, LangChain, or crewAI, as these tools are shaping how multiagent systems are built and managed.

It is also worth paying attention to candidates who have worked on monitoring or observability for AI systems. Multiagent environments require careful oversight to ensure reliability and compliance.

What this means for candidates

For candidates, multiagent systems represent a growing area of opportunity. If you have experience building workflows that connect multiple AI tools or APIs, you are already developing the right skills.

Invest time in learning how agents communicate and share data, and how to design systems that include human oversight. Employers will be looking for professionals who can balance automation with accountability.

Developers, data scientists, and AI specialists who can explain how they have tested or audited multiagent systems will have a clear advantage when applying for roles in 2026.

Most popular job titles in multiagent systems:

• AI orchestration engineer

• Multiagent systems architect

• Workflow automation specialist

• AI reliability engineer

• Agent-based software developer
  
  

5. Domain-specific language models (DSLMs)

Artificial intelligence is becoming more specialised. Instead of relying only on large general-purpose models, many organisations are now building domain-specific language models that are tailored to their industry or function. In 2026, these models are helping businesses gain accuracy, reduce costs and improve reliability across key processes.

What are domain-specific language models?

Domain-specific language models (DSLMs) are smaller AI models trained on targeted data sets related to a specific industry, task or area of expertise. Unlike general models that try to handle every topic, DSLMs focus on one subject and learn its vocabulary, context and rules in depth.

For example, a DSLM in healthcare can interpret clinical notes accurately, while one built for legal services can summarise case files without misunderstanding terminology. By narrowing their focus, these models deliver faster responses, lower operating costs and improved data privacy.

Why it matters in 2026

In 2026, DSLMs are becoming a practical choice for businesses that need AI systems they can trust. They are cheaper to train and easier to govern than very large models, making them suitable for sectors that handle sensitive or regulated information.

The technology is also driving efficiency. Teams are starting to create their own in-house DSLMs for customer support, finance, recruitment and data analysis. This gives organisations more control over their outputs and allows them to meet regional compliance requirements such as GDPR. As the tools to train and deploy DSLMs become more accessible, adoption across UK and European markets is expected to accelerate.

What this means for hiring teams

Hiring teams will see new technical roles appear around model training, evaluation and governance. Companies will need engineers who can fine-tune models using internal data, as well as specialists who can measure and monitor model accuracy over time.

When recruiting, look for candidates who can demonstrate experience in data curation, model evaluation, and MLOps for small models. Roles may also require familiarity with open-source frameworks and an understanding of how to align AI outputs with organisational policies. DSLMs create opportunities for data-driven roles across industries, from financial services to retail and healthcare.

What this means for candidates

For candidates, DSLMs offer a chance to develop valuable niche expertise. Employers will be looking for people who understand both the data and the domain. If you work in a sector such as law, healthcare or marketing, combining your subject knowledge with AI skills can make you a strong candidate.

To prepare, focus on learning how model fine-tuning works and how evaluation metrics are used to measure accuracy and fairness. Familiarity with frameworks such as Hugging Face, OpenAI Finetuning API or similar platforms will help you stand out. Being able to explain how you improved a model’s performance or reduced its error rate can make your experience more tangible to employers.

Most popular job titles in DSLMs:

• AI model engineer

• Data and machine learning engineer

• Model evaluation scientist

• Domain data specialist

• AI governance analyst
  
  

6. Physical AI

Artificial intelligence is moving beyond screens and into the physical world. Physical AI brings together robotics, machine learning and computer vision to create intelligent systems that can move, sense and act in real environments. In 2026, this area is expected to see major growth as industries look for new ways to automate physical tasks safely and efficiently.

What is physical AI?

Physical AI refers to the use of artificial intelligence within machines that can interact with the real world. This includes robots, autonomous vehicles, drones, and industrial machines that can make decisions based on data from their surroundings.

These systems combine sensors, cameras, and AI algorithms to understand context, navigate spaces, and perform actions such as picking, assembling or inspecting. Unlike traditional automation, physical AI adapts in real time, allowing it to handle tasks that require perception, coordination and learning.

Why it matters in 2026

In 2026, physical AI is becoming more practical and affordable. Advances in hardware, simulation tools and robotics-focused AI models are helping companies move from pilot projects to production-scale automation.

Sectors such as logistics, manufacturing, retail and healthcare are seeing particular benefits. Robots that can sort parcels, assist in warehouses or support clinical operations are reducing labour shortages and improving safety. As costs fall and performance improves, more businesses will adopt physical AI to boost productivity and reliability.

For many organisations, the next stage of digital transformation will involve automating both digital and physical processes together.

What this means for hiring teams

Hiring teams will need to plan for new technical disciplines as robotics and AI converge. Companies will be looking for professionals who can design, build and maintain intelligent systems that operate safely in physical spaces.

Key skills include robotics software development, computer vision, sensor integration, and AI control systems. Experience with frameworks such as ROS (Robot Operating System) or simulation tools like Gazebo and Isaac Sim will be highly valued.

It will also be important to assess candidates’ understanding of safety standards such as ISO 10218 or ISO 13849, as compliance and risk management will remain central to physical AI deployments.

What this means for candidates

For candidates, physical AI offers opportunities across engineering, automation and operations. Whether you have experience in robotics, software development or mechanical systems, employers will be looking for people who can connect AI models with real-world outcomes.

Building hands-on experience with robotics toolkits or participating in automation projects can make your CV stand out. Learning how AI interacts with sensors and hardware will also help you position yourself for emerging roles.

For those interested in career growth, this is an area where demand is expected to increase steadily through the rest of the decade, especially across manufacturing, logistics and smart infrastructure.

Most popular job titles in physical AI:

• Robotics software engineer

• Computer vision engineer

• Automation systems specialist

• AI controls engineer

• Robotics technician
  
  

7. Generative video

Generative video is changing how content is created, shared and consumed. What once took entire production teams can now be achieved in hours using AI-driven video tools. In 2026, this technology is being adopted across marketing, education, and internal communications to produce high-quality visual content at scale.

What is generative video?

Generative video is a type of artificial intelligence that creates video content automatically from text, images or prompts. Using AI models trained on video, audio and motion data, these systems can generate realistic clips, animations or explainer videos based on simple written instructions.

This includes everything from marketing campaigns and training materials to product walkthroughs and recruitment videos. Businesses can use generative video to create visual content faster, reduce costs, and maintain consistent messaging without needing a full production studio.

Why it matters in 2026

In 2026, generative video is becoming a core part of how brands communicate. Marketing teams are using it to personalise campaigns, sales teams are producing interactive product demos, and learning departments are creating training materials that adapt to each user.

The rise of short-form and AI-assisted video means companies can reach audiences more effectively across digital channels. As generative models improve in realism and compliance, they will reduce the need for manual editing and filming, freeing creative teams to focus on storytelling and strategy.

With the demand for video content continuing to grow across sectors, generative video is helping businesses stay visible, relevant and consistent.

What this means for hiring teams

For hiring teams, the rise of generative video is expanding the skill sets needed in marketing and creative departments. Employers will be looking for professionals who understand both content strategy and how to work with AI video tools such as Runway, Synthesia and Pika Labs.

When recruiting, focus on candidates who can plan and produce campaigns that combine human creativity with AI efficiency. Roles that bridge creative direction, digital marketing and AI production will become more common. Technical knowledge of scripting, editing and visual storytelling will remain important, but so will the ability to interpret data and measure campaign performance across digital platforms.

What this means for candidates

For candidates, generative video opens up new ways to work in content creation and marketing. You do not need to be a videographer to create high-quality visual content anymore, but understanding how AI tools work will set you apart.

If you are in marketing, design or communications, learning to use AI-driven platforms can help you produce campaigns faster and more efficiently. Employers will value candidates who can show examples of AI-assisted work, whether that’s product videos, recruitment campaigns or social media content.

As AI continues to change how brands tell their stories, combining creative thinking with technical awareness will be key to career growth.

Most popular job titles in generative video:

• AI content producer

• Creative technologist

• Digital marketing manager

• Video production specialist

• Brand content strategist
  
  

8. Digital provenance

As artificial intelligence continues to produce more content and data, knowing where that information comes from is becoming critical. In 2026, digital provenance will be essential for building trust in AI-generated materials and for proving the authenticity of digital assets.

What is digital provenance?

Digital provenance refers to the ability to verify the origin, history and ownership of digital content such as data, images, video and code. It uses cryptographic signatures, metadata, and blockchain-based records to confirm where content was created, how it has been changed, and who has handled it over time.

This process helps organisations track digital assets accurately and prevent misuse or manipulation. It also provides assurance that AI-generated materials, including images or text, meet ethical and legal standards. In short, digital provenance makes it possible to trace the full journey of data and content, from creation to publication.

Why it matters in 2026

By 2026, digital provenance is becoming a requirement rather than an option. As generative AI tools produce vast amounts of content, the risk of misinformation, copyright disputes and deepfakes is increasing. Governments and regulators are already introducing standards that require companies to label or authenticate AI-generated media.

For businesses, digital provenance builds confidence in what they create and share. It supports compliance with intellectual property and data protection laws, while also protecting brand integrity. In industries such as recruitment, finance, media and software, it helps maintain transparency and accountability across every digital process.

What this means for hiring teams

Hiring teams will see growing demand for professionals who can manage and implement provenance systems. Companies will need specialists who understand data governance, cryptography, and content authentication frameworks such as C2PA (Coalition for Content Provenance and Authenticity).

When hiring, look for candidates who have experience working with data supply chains or security systems that track digital assets. Roles that connect compliance, data management and technology will become increasingly important. Organisations that adopt digital provenance early will also find it easier to demonstrate compliance and build customer trust.

What this means for candidates

For candidates, digital provenance offers opportunities across technology, data and creative fields. Professionals who can prove expertise in managing trusted data sources or securing digital content will be in high demand.

If you are in a technical role, learning about encryption, metadata standards and digital signatures can strengthen your CV. Those working in marketing, media or design can benefit from understanding how content credentials are applied to AI-generated materials. This knowledge shows that you are aware of data ethics and digital trust, which are becoming core priorities for employers in 2026.

Most popular job titles in digital provenance:

• Data governance lead

• Cryptography engineer

• Content authenticity specialist

• Compliance and risk manager

• Blockchain data analyst
  
  

9. Preemptive cyber security

Cyber threats are becoming more complex and more frequent. Instead of reacting after an attack, businesses in 2026 are shifting towards preemptive cyber security, where prevention and prediction are the main priorities. This approach focuses on identifying risks before they turn into incidents, using AI, automation and continuous monitoring to stay one step ahead.

What is preemptive cyber security?

Preemptive cyber security refers to a proactive approach to digital protection. It uses real-time analytics, machine learning and automated threat detection to identify suspicious activity before it causes harm. These systems analyse patterns across networks, devices and applications to predict potential breaches or vulnerabilities.

Rather than waiting for alerts, preemptive security platforms act in advance, closing gaps, isolating threats and protecting critical data automatically. This approach reduces response time and limits the potential impact of attacks on both data and operations.

Why it matters in 2026

By 2026, the cost of cyber crime continues to rise, and regulations around data protection are becoming stricter. Businesses can no longer afford to take a reactive approach. AI-driven security systems that monitor, predict and defend are now essential for maintaining resilience.

The move towards preemptive strategies is also linked to the wider use of AI and automation across industries. As companies deploy more connected systems and digital tools, the attack surface increases. Preemptive cyber security helps manage this complexity by learning from past threats and continuously improving defences.

This trend is particularly relevant for sectors such as finance, healthcare, government and technology, where the protection of sensitive data is critical to trust and compliance.

What this means for hiring teams

For hiring teams, this shift means looking for professionals who combine technical security expertise with analytical and AI skills. The traditional boundaries between cyber security, data science and operations are starting to overlap.

Roles will increasingly focus on threat intelligence, automated response systems, and security analytics. When recruiting, look for candidates who can use tools like SIEM, SOAR and EDR to detect and respond to incidents automatically. Candidates with experience in predictive modelling or AI-driven monitoring will be especially valuable.

It is also important to build teams that understand regulatory requirements, as compliance will remain a key focus for UK and European organisations.

What this means for candidates

For candidates, preemptive cyber security represents one of the most in-demand career areas of 2026. Employers will be looking for people who can think ahead, not just respond to alerts.

If you work in cyber security, develop skills in automation, data analysis and machine learning. Understanding how to interpret threat intelligence and apply it in real time will set you apart. For those entering the industry, certifications in cyber analytics or cloud security can provide a strong foundation.

Being able to demonstrate proactive thinking and familiarity with AI-assisted tools will help you position yourself for emerging roles in both enterprise and consultancy environments.

Most popular job titles in preemptive cyber security:

• Cyber threat intelligence analyst

• Security operations centre (SOC) engineer

• AI security analyst

• Incident response manager

• Security automation specialist
  
  

10. Geopatriation

As global tensions, data-sovereignty rules and AI governance tighten, organisations are re-evaluating where their digital operations are based. In 2026, geopatriation describes the move to keep technology, data and infrastructure closer to home. Businesses are choosing regional data centres and sovereign AI solutions to strengthen compliance, security and resilience.

What is geopatriation?

Geopatriation is the process of bringing digital assets, cloud services and AI operations under national or regional control. It focuses on using in-country data storage, local cloud providers and sovereign compute systems to ensure data stays within legal and geographic boundaries.

The goal is to protect sensitive information and reduce reliance on overseas technology partners. This approach also helps organisations meet privacy regulations such as the UK Data Protection Act and the EU AI Act, both of which prioritise transparency, data residency and accountability.

Why it matters in 2026

In 2026, data sovereignty becomes a critical business issue. Many governments are introducing rules that require specific types of data to be stored or processed within their borders. Public-sector projects, financial institutions and healthcare providers are leading the adoption of geopatriation to guarantee compliance and protect national interests.

For private companies, this trend is about control and continuity. Using local infrastructure reduces exposure to international disruptions and improves trust among clients who want assurance that their data is handled securely. As AI expands across industries, keeping sensitive information within domestic systems will be a core part of risk management and brand reputation.

What this means for hiring teams

For hiring teams, geopatriation introduces new demands across cloud engineering, security and compliance. Employers will need specialists who can design and maintain infrastructure that meets regional hosting and data-residency requirements.

When recruiting, look for candidates who understand sovereign cloud architecture, data-residency design, and cross-border compliance. Experience with providers offering local hosting options or national data zones will be a strong advantage. Roles that connect technology and regulation will become increasingly important, particularly for organisations delivering services to the public sector or handling regulated data.

What this means for candidates

For candidates, geopatriation presents new opportunities in cloud architecture, governance and cyber security. Professionals with experience in secure infrastructure, compliance frameworks and localisation strategies will be in high demand.

If you work in cloud or data engineering, learn about local hosting options, compliance certifications and how to architect systems for regional requirements. For project managers and consultants, understanding how to balance performance with compliance will make you valuable to clients adopting sovereign solutions.

Candidates who can demonstrate knowledge of both the technical and legal aspects of data sovereignty will stand out in 2026.

Most popular job titles in geopatriation:

• Cloud architect

• Data-residency engineer

• Compliance and governance manager

• Security operations specialist

• Public-sector delivery manager

The technology trends shaping 2026 highlight a clear shift towards smarter, safer and more specialised use of artificial intelligence. From AI-native development and supercomputing to preemptive cyber security and digital provenance, these innovations are changing how companies operate and how teams are built.

For employers, staying competitive means keeping pace with these changes and hiring people who understand them. For candidates, it means developing skills that combine technical knowledge with adaptability and curiosity. The organisations and professionals who evolve with these trends will be best placed to succeed in the years ahead.

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