Top tech trends shaping 2026   

In 2026, technology is undergoing a major transformation. Organisations are rapidly turning innovation into operational reality, from agentic AI to quantum computing.   

Will agentic AI finally scale beyond pilots? Are voice assistants ready to handle complex enterprise workflows? And will quantum computing deliver measurable business value?   

We reached out to some of our SMEs (subject matter experts) across FDM for their insights on the top tech trends to look out for in 2026.  

What are the most popular tech trends for 2026?   

• LLM-powered voice assistants   

• Vibe coding   

• Low-code/no-code  

• Quantum computing   

• AI risk management   

• Agentic AI 

• AI-driven cybersecurity   

LLM-powered voice assistants at scale   

The evolution of enterprise voice AI   

Voice AI is surging, driven by large language models. The global voice AI market is projected to reach $45 billion USD by 2030, growing 22% annually.   

In 2026, voice assistants will no longer be limited to simple commands. They are becoming the primary interface for complex, multi-step tasks across customer service, HR, IT support, and sales operations.   

Why LLM-powered voice assistants matter   

The key innovation lies in real-time pipelines that combine speech-to-text, LLM reasoning, and text-to-speech. These systems can:   

• Understand conversational context   

• Retain memory across interactions   

• Integrate with CRM, scheduling, and knowledge platforms  

• Adapt tone and personality to brand identity   

Klaudio Marashi, FDM Skills Lab Global Head Coach, explains, “The next wave of LLM-powered voice assistants isn’t about better conversation, but about autonomous, task-capable voice agents that understand context, execute workflows, and reduce human intervention.”   

This evolution elevates how organisations can use voice assistants to scale and enhance service delivery, operations, and decision-making. According to research by NextLevel.AI, by 2026, 80% of enterprises plan to integrate AI-driven voice technology into customer service operations, connecting them with existing CRM platforms, scheduling systems, and knowledge bases.   

Vibe coding    

What is vibe coding?   

Vibe coding is a fundamental shift that blends logic, creativity, and intuition. It challenges us to bring human-centred thinking into everything we build and uses AI to enhance, not replace, the developer’s role.   

How AI coding tools are changing productivity   

What if you could write functional code without memorising syntax, debugging endlessly, or even being a seasoned programmer?   

Developers report productivity increases of 3-5x for common tasks, with 84% already using or planning to use AI coding tools in 2026. Vibe coding uses natural language processing (NLP) and AI models to generate functional code from plain language prompts. AI tools like Anaconda Assistant, Jupyter AI, GitHub Copilot, Amazon CodeWhisperer, and Replit Ghostwriter are changing the coding landscape. 

However, code generated by AI can contain hidden security vulnerabilities, create technical debt at scale, and produce applications that are difficult for other developers to maintain or understand.   

Gangotri Bhatt, Director of Skills Lab, UK and EMEA, shares, “Imagine coding that feels like a conversation rather than a command line. That’s the essence of vibe coding, where creativity meets technology, enabling developers to focus on innovative ideas rather than syntax. By lowering technical barriers, vibe coding opens the door for people from diverse, non-traditional backgrounds to contribute to tech, fostering inclusion and driving fresh perspectives in the industry.”   

Low-code and no-code  

Why low-code adoption is surging   

The low-code/no-code market has reached a tipping point. Gartner forecasts that 75% of new enterprise applications will be built using low-code platforms by 2026, with the market exceeding $30 billion USD in value.   

Organisations report up to a 90% reduction in development time, average annual savings of $187,000 USD, and rapid return on investment.   

Rim Almaliki, Director of FDM’s Skills Lab, North America, says, “2026 will be the year low-code platforms truly democratize technology. As integration becomes seamless, businesses will empower teams to build solutions faster without deep coding expertise, accelerating innovation and reducing time-to-market.” 

AI-powered low-code platforms   

Modern platforms like Microsoft Power Platform, Mendix, OutSystems, and Retool now embed AI to generate application logic, recommend data models, and automate testing and optimisation. 

The implications are significant. Business analysts can now build applications that would have required a development team two years ago.  

Quantum computing   

The hybrid approach   

Rather than replacing traditional computing, organisations are adopting hybrid models. Cloud providers such as IBM, AWS, Microsoft, and Google now offer quantum-as-a-service platforms, enabling experimentation without large capital investment.   

The security imperative   

Quantum computing presents significant cybersecurity challenges. As quantum systems approach the ability to break existing encryption standards, organisations must begin transitioning to post-quantum cryptography.   

Governments and enterprises are accelerating the adoption of quantum-resistant algorithms to future-proof sensitive data and infrastructure.   

AI-driven cybersecurity  

The evolving threat landscape   

AI is now both our most powerful defensive tool and the most sophisticated attack vector. Threat actors are deploying autonomous AI agents capable of adaptive exploitation and deepfake-driven social engineering at a huge scale.   

Deepfakes and voice cloning   

Sawan Joshi, FDM Group Director of Information Security, warns, “Phishing is no longer about poorly written emails or suspicious links. It has evolved into something far more personal, convincing, and dangerous. The combination of deepfakes, voice cloning, and highly organised cyber-criminal gangs has shifted the battlefield from technology alone to human trust.”   

Deepfake videos and AI-generated voices are now sophisticated enough to pass casual verification. Attackers don’t just pretend to be a CEO or a family member—they sound like them, pause like them, and send video messages looking exactly like them. Cloning tools are increasingly used in targeted attacks where a single successful con can result in six- or seven-figure losses.   

Security measures that matter in 2026   

Technology still plays a role, but awareness and process matter just as much:   

• Assume identity can be faked: Voice and video are no longer proof. Treat them as signals, not verification.   

• Use out-of-band verification: Any urgent or sensitive request should be confirmed through a second, known channel.   

• Establish “no-exception” rules: Especially for payments, credential resets, or data sharing—urgency is a red flag.   

• Train for conversation, not clicks: Modern phishing happens in chats, calls, and meetings, not just in inboxes.   

• Limit public oversharing: The less context attackers have, the harder it is to sound “local” and legitimate.   

Klaudio Marashi notes the strategic shift: “Preemptive Cybersecurity powered by AI shifts traditional Detection and Response toward anticipatory, predictive and proactive defence. These solutions leverage AI and machine learning to forecast, disrupt, and neutralise malicious activity before it can impact systems.”   

This approach denies access through proactive controls, deceives threat actors with advanced deception and moving-target techniques, and disrupts malicious activity using automated, real-time defences.  

AI risk management and governance   

Why AI risk is now a board-level issue   

The rise of agentic AI introduces a critical challenge. While many organisations can monitor AI behaviour, fewer than 40% can effectively stop or contain autonomous agents when issues arise.   

Building effective AI governance frameworks   

In response, organisations are formalising AI governance through:   

• AI-specific identity and access management   

• Continuous behavioural monitoring   

• Kill-switch capabilities for autonomous systems   

• Compliance alignment with regulations such as the EU AI Act   

Yves Laffont, Head of Risk, Regulation & Compliance at FDM Group, comments, “In 2026, compliance excellence will be defined by how well organisations centralise risk and financial crime controls into an integrated operating model, rather than continuing to firefight fragmented processes. Regulatory momentum under mandates like the GENIUS Act and DORA shouldn’t be viewed as a burden, but as a strategic lever that brings compliance and digital transformation closer together. As payments modernise and cross-border activity increases, organisations will need AI-driven AML (Anti-Money Laundering), automated evidence packs, and governance models that can scale with complexity. Those that plan ahead, instead of relying on reactive fixes, will be far better positioned to turn regulatory change into long-term competitive advantage.” 

Agentic AI 

What is Agentic AI?

Agentic AI refers to autonomous systems that can plan, reason, and take action across multiple tasks without constant human oversight. Unlike traditional AI models that provide recommendations, agentic AI executes end-to-end workflows independently.   

Why agentic AI is accelerating in 2026   

The biggest shift in 2026 is the move from experimental AI agents to production-grade deployments. Gartner predicts that 40% of enterprise applications will embed AI agents by the end of 2026, up from less than 5% in 2025.   

Agentic systems are evolving into digital workers capable of managing security threat triage, IT operations, finance workflows, and customer service processes. However, adoption remains uneven. While pilots are widespread, only around 11% of organisations currently run agents in production environments. The complexity of agentic systems nearly doubled as a cited barrier. Organisations are now contending with what it takes to run them. 

This gap reveals the growing importance of governance frameworks, secure architectures, and clear accountability models for autonomous AI. As organisations scale these systems, they’re discovering that technical capability alone isn’t enough—successful deployment requires clear decision boundaries, and fail-safe protocols to ensure agents operate within intended parameters.   

The future of AI and technology   

What is the next big thing in AI?   

The next major leap in AI is the convergence of agentic systems, multimodal intelligence, and real-time decision-making. Rather than isolated tools, AI will operate as interconnected ecosystems capable of reasoning across text, voice, vision, and data streams simultaneously.   

By 2026, organisations will increasingly deploy AI systems that:   

• Collaborate with humans as co-decision makers  

• Adapt dynamically to changing environments  

• Learn continuously within governed boundaries  

This shift will redefine productivity, placing greater emphasis on orchestration, trust, and accountability rather than raw automation.   

Fabian Fertig, FDM Senior Delivery Consultant, adds, “AI agents will increasingly evolve into specialised, task‑specific services that can operate autonomously or in close collaboration with users. In domains such as compliance and financial analysis, these agents can deliver substantial time savings and reduce manual workload.” 

What will technology look like in 2026?   

AI will be embedded invisibly across workflows, voice will become a default interface, and automation will operate in the background.   

Organisations will prioritise resilience, security, and explainability alongside innovation. The most successful businesses will be those that balance advanced technology with human judgment, ethical frameworks, and strong governance.   

Balancing technical and human expertise   

As hiring accelerates in 2026, demand will grow for skills in agentic AI, voice interface design, quantum computing, and AI security. However, technical expertise alone will not be enough.   

Rim shares, “The pace of change is accelerating, and while technical skills matter, the ability to learn quickly and apply new tools—especially in areas like AI-driven development and low-code platforms will set professionals apart. Focus on building problem-solving skills and a strong foundation in data literacy, as these will remain critical regardless of the technology stack.” 

Summary   

LLM-powered voice assistants, vibe coding, low-code platforms, agentic AI, and quantum computing are set to reshape industries in 2026. As AI moves from pilots to production, organisations must strengthen governance, cybersecurity, and talent strategies. Success will depend on blending technical innovation with human insight, risk management, and ethical leadership.   

The organisations that will thrive are those that approach these technologies not as isolated capabilities but as interconnected elements of a broader transformation strategy. They will invest equally in technology infrastructure and human capability, in automation and oversight, in innovation and responsibility.   

How FDM can support   

Through our practices and Skills Lab, we develop consultants who excel in technology, communication, and leadership. Our continuous upskilling model ensures long-term impact for clients navigating complex digital transformation.   

Whether you’re deploying agentic AI systems, building quantum-ready infrastructure, or strengthening your cybersecurity posture, FDM consultants bring the technical expertise and strategic thinking needed to turn emerging technologies into business advantage.   

Find out how our consultants can help bridge your tech skills gap and accelerate your transformation projects.