Top Tech Trends To Watch In 2026

A sneak peek into the coming year’s tech trends can help you spot the opportunities and threats, and plan for success…

The forecasts are rolling out, and 2026 looks set to be another year headlined by AI. However, it is also clear that AI cannot continue its run without more processing power, reliable telecommunication, sustainability initiatives, renewable energy, and other supporting enablers. As a result, we are also going to see every tech sector upping its game in the coming year. Here is our take on the likely technology trends of 2026, based on research reports from trusted sources like Gartner, McKinsey, and Deloitte, alongside expert opinions shared through EFY magazine and industry conferences.

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Artificial Intelligence – more real than it sounds

• The focus is shifting from text-based large language models (LLMs), which function like conversational search engines, to AI agents that can understand, plan, decide, collaborate, and execute tasks autonomously! Deloitte predicts that the autonomous AI agent market could reach $8.5 billion by 2026 and $35 billion by 2030.
• Using disparate AI models for varied functions often increases complexity, botches up customer experience, and slows AI adoption among mid-sized organisations. With the emergence of data platforms (data productisation) and open frameworks like the model concept protocol, models and agents are now able to safely and seamlessly access shared context and metadata across APIs, products, and applications. This trend will continue into 2026.
• We see many open-weight AI models emerging – there will be more in the coming year. Organisations will lean towards open, composable AI frameworks, to prevent vendor lock-in, and improve fit, flexibility, and scalability.
• Domain-specific language models, which are trained for a particular industry or function, are likely to be a major trend in 2026. These models are small and economical, yet more accurate and effective, because they are an expert in one, rather than a jack of all trades.
• Model engineers will attempt to bring down latency and cost of inference.
• We are likely to see more compact AI models that can run on regular central processing units (CPUs) or affordable application-specific integrated circuits (ASICs) instead of relying on expensive graphics processing units (GPUs).
• There will be a growth in AI-native development platforms, which will enable small- and medium-sized businesses to quickly and economically build software systems using generative AI (gen AI).
• The use of gen AI in entertainment and gaming is expected to grow in 2026. Apart from being used in the backend, for visual effects and such, AI is also being used by creators, especially in China and India, to create engaging micro-dramas.
• Likewise, AI will continue to be used extensively to fabricate ‘real-time’ data to test systems. Together with virtual reality (VR) and augmented reality (AR), it will be used for training and experimentation, as it will provide immersive, real world scenarios without physical risks. In healthcare, these technologies will be used for training as well as treatments.
• The use of AI in advanced research projects, such as study of protein structures, climate change, energy management, and drug discovery, is also expected to go up.
• That said, Deloitte predicts that by 2026, the use of AI within search will be three times greater than any standalone AI tool.
• CXOs will have to focus on building stronger governance frameworks.
• Organisations will shift manpower around and invest in AI fluency training, as AI takes up more of the grunt work. Engineers and developers need to skill up on prompt engineering, orchestration layers, and model fine-tuning to fit into new roles like prompt engineers, model trainers, and output editors.
• Mathematicians will be in demand – as a lot of research is happening to improve AI models.

Processors – AI Chips, Quantum Chips, and more!

• AI chips are coming out in all shapes and sizes, literally speaking, from AI personal computers to AI supercomputers. There will be an increased focus on developing highly-customised chips for AI training and inference at scale.
• On one hand, we will see an increasing demand for more powerful GPUs (especially Nvidia’s). On the other hand, we will see a growth in ASICs optimised for inference only, as they will be more accessible. Deloitte predicts that while the market for inference-optimised chips will grow to over $50 billion in 2026, combined training/inference AI chips in data centres will continue to dominate the $200 billion AI chip market.
• Big tech companies will continue to work on their in-house chips to reduce dependency on Nvidia.
• We will also see better quantum computing chips this year, with improved error correction and scalability.
• There is also buzz about industry-specific quantum computers on the anvil.

Cloud Computing – AI’s best friend