Tech Trends 2026

Intelligence is leaving the screen. Robots are evolving from pre-programmed machines into adaptive systems that learn and operate in complex, dynamic environments. Amazon’s AI-coordinated warehouse robots and BMW’s autonomous factory vehicles exemplify this shift—embodied intelligence solving real-world problems with unprecedented capability.

Physical AI refers to artificial intelligence systems that enable machines to autonomously perceive, understand, reason about, and interact with the physical world in real time. 

Unlike traditional robots that follow preprogrammed instructions, these systems perceive their environment, learn from experience, and adapt their behaviour based on real-time data. The combination of advanced manufacturing infrastructure, component commoditisation, and open-source development is making physical AI systems practical for everyday industrial use.

Autonomous digital workers—AI agents that can independently make decisions and complete tasks—promise transformational productivity gains. Yet only 11% of organisations have agents in production, while 38% are piloting. The problem isn’t technology; it’s strategy.

Most organisations are automating broken processes rather than redesigning operations for AI-first workflows. They’re trying to fit AI into human-designed work, when success requires reimagining work for a “silicon-based workforce.” Leading organisations are developing hybrid human-digital teams, recognising that AI agents and human workers have fundamentally different skill sets and capabilities.

Here’s the paradox: token costs for AI inference have dropped 280-fold in two years, yet enterprise AI bills are exploding into tens of millions monthly. Usage has grown faster than costs have declined, exposing critical infrastructure limitations.

Existing cloud and on-premises strategies are insufficient for large-scale AI deployment. Organisations must shift toward hybrid infrastructure strategies: cloud for elasticity, on-premises for control and consistency, and edge computing for immediacy. The focus is on optimising compute resources to balance cost, performance, and scalability in the era of “inference economics.”

AI is restructuring tech organisations from the inside out. CIOs are becoming AI evangelists, orchestrating human-agent teams. Organisations are adopting modular architectures, embedded governance, and perpetual evolution cycles. The goal: embed AI into core processes and enable continuous adaptation.

This isn’t incremental change—it’s holistic transformation affecting people, processes, and technology simultaneously. Organisations are redefining roles, adapting leadership structures, and reimagining how work gets done. Success requires courage to redesign, velocity of execution, and the ability to connect technology investments to tangible business outcomes.

AI presents a dual challenge: it’s both a powerful enabler and a target for cyber threats. As AI becomes more powerful, it also becomes more attractive to malicious actors. Organisations must secure AI across four domains: data, models, applications, and infrastructure. Simultaneously, they must leverage AI for cyber defence at machine speed.

This is the cybersecurity paradox. Organisations must navigate the tension between protecting AI systems and exploiting AI’s power for threat detection and response. Success requires comprehensive security strategies that address data integrity, model tampering, application vulnerabilities, and infrastructure threats—while using AI itself as a defensive weapon.

While five dominant technology trends are reshaping enterprise operations, Deloitte identifies eight emerging “signals”—mini-trends at earlier adoption stages—that warrant strategic monitoring. These signals represent the next wave of technology maturation.

According to Deloitte’s Tech Trends 2026, some of these signals may mature into dominant forces. Others may fade. But all of them reflect the same underlying reality: the pace of technological change has fundamentally shifted, and early recognition of emerging patterns offers strategic advantage. Organisations should focus on sensing and responding to these signals rather than predicting their trajectory with certainty.

The eight technology signals

• Foundation models — Large-scale AI systems trained on massive datasets. Deloitte identifies questions about performance plateau, energy costs, and optimisation strategies as key considerations. The focus is shifting from model size to deployment optimisation and fine-tuning approaches.
• Synthetic data — AI-generated data used for training models. Deloitte notes that synthetic data is predicted to constitute a significant portion of AI training data, with performance limitations and quality considerations that organisations must address.
• Neuromorphic chips — Brain-inspired processors designed for energy efficiency. Deloitte identifies neuromorphic computing as an emerging hardware innovation expected to see widespread adoption, particularly for edge AI and IoT applications.
• Edge AI & on-device processing — Running AI directly on devices (phones, cameras, sensors) to reduce latency, improve privacy, and cut costs. Deloitte identifies edge AI as a critical technology for real-time, private, and cost-effective AI applications.
• AI-native wearables — AI-powered wearables beyond smartphones. Deloitte notes that market growth and adoption remain uncertain, with consumer preferences and privacy considerations as key factors.
• Biometric authentication — Using biometric data for security. Deloitte identifies increasing adoption due to security needs, though privacy and security challenges persist as ongoing considerations.
• AI agent privacy trade-off — Personal AI assistants require extensive personal data, raising privacy and security concerns. Deloitte identifies this as a critical design challenge for organisations deploying AI agents.
• GEO (Generative engine optimisation) — The shift from traditional SEO to AI-driven content optimisation for search and marketing. Deloitte identifies this as a fundamental change in digital marketing strategies driven by AI capabilities.