Capgemini unveiled today its ‘TechnoVision Top 5 Tech Trends to Watch in 2026’, highlighting the technologies that are expected to reach an inflection point in the next year. While artificial Intelligence (AI) and generative AI (Gen AI) remain central, their influence now extends across software development, cloud architectures, and enterprise operations. These trends reflect a shift toward deeper integration, resilience, and tangible business value.

“Last year, Capgemini’s Top 5 Tech Trends notably predicted the rise of AI robotics, a vision that became reality both in the market and at Capgemini with the launch of our AI Robotics & Experiences Lab and current experimentation with Orano,” explains Pascal Brier, Chief Innovation Officer at Capgemini and Member of the Group Executive Committee. “As we look ahead to 2026, AI moves beyond experimentation and enters a phase of maturity. The upcoming year will see AI become the backbone of enterprise architecture, reshape software lifecycle development, and redefine cloud consumption. At the same time, enterprise systems are undergoing a fundamental shift toward intelligent operations, while tech sovereignty emerges as a strategic priority, driving organisations to build resilient interdependence.”

Technologies to watch in 2026

The year of truth for AI: AI is, without doubt, the defining technology of the decade, but the pace of investment has outstripped the speed at which organisations have deployed and extracted value from it. Taking stock of where some of their AI experimentations failed to deliver the expected outcomes, business leaders now understand that the issue didn’t come from the technology itself but from the business approach and methodology.

Full-scale deployments will take time, and long-term value will not come from isolated AI use cases but from enterprise-wide implementations. As the true growth phase begins, an AI ecosystem rooted in operational value and enterprise architecture is emerging, starting with data foundations and infrastructure, and focusing on “Human-AI chemistry.” 2026 will be the moment to shift from proof-of-concept to proof-of-impact, ensuring AI delivers measurable outcomes, trust, and collaboration at scale, while laying the groundwork for larger-scale transformation to follow.

Why it matters: The pace of AI development shows no signs of slowing down, and the options available on the market continue to expand. Meanwhile, after years of fragmented pilots, 2026 will be a year of meaningful progress, where organizations will invest in data and AI readiness and, more importantly, in “Human-AI chemistry” – shifting away from hype to leverage the transformative potential of AI.

AI is transforming software: Software has already dominated the world, and now AI is transforming software itself. AI is reshaping the software development lifecycle across industries, shifting from writing code to expressing intent. After years of automation and DevOps-driven acceleration, AI is increasingly generating and maintaining software components. From now on, developers will specify outcomes, while AI generates and maintains parts, shortening delivery cycles and enhancing quality.

However, governance and oversight remain essential to prevent hallucinations, security vulnerabilities, and silent errors. This new era of “Rebuilding software” across the full value chain aligns with becoming an AI-Native Business, operating on adaptive platforms rather than static ones. This approach creates opportunities to develop more adaptive, sovereign systems, reducing dependence on Software as a Service providers and enabling differentiation through tailored products at competitive prices.

Why it matters: In 2026, this shift will increasingly redefine roles, making human supervision and quality control essential for trust and resilience. Organisations will begin rebuilding their applications and need to focus on reskilling their software development workforce soon. The new currency of expertise will instead lie in systems thinking, AI and agents orchestration, and managing complex, autonomous process and tool chains.

Cloud 3.0: All flavours of cloud. Cloud is entering its next stage, where hybrid, private, multi-cloud, and sovereign architectures are no longer niche but essential for AI at scale. They are becoming the operational backbone for AI and agentic workloads. AI cannot scale and achieve the right performance on classical public cloud alone, which drives the adoption of other cloud models.

Indeed, agentic systems depend on scalable and low-latency infrastructures, with edge and cloud functioning as a unified intelligent platform. Furthermore, large-scale outages and geopolitical pressures accelerate diversification and resilience strategies. While hybrid platforms become mainstream, organizations will redesign architectures for performance, portability, sovereignty, and strategic autonomy to ensure business continuity.

Why it matters: Cloud 3.0 will expand the options for organizations to customize their cloud usage to meet various needs, especially concerning asset redundancy, criticality, and latency. However, while this can enhance resilience, it may also increase complexity in management, pressuring cloud providers to improve interoperability within their multi-vendor strategies. In the Cloud 3.0 era, organizations must equip themselves with the right skills, agile governance, and an adaptive mindset to confidently operate across diverse cloud environments.

The rise of Intelligent Ops: Enterprise systems are evolving from static systems of record into living engines of intelligent operations – it’s a ‘Copernican Revolution’ where processes become the focus, rather than bolt-on applications. With the promise of agentic systems, businesses can rethink and redesign their processes to make them self-improving, adaptable, and agile. Companies are now looking to orchestrate entire processes, not isolated steps, to run connected operations that break silos to create integrated value chains and enable organisation-wide optimisation.

AI agents embedded in core processes are starting to monitor activity, optimise execution, resolve exceptions, and orchestrate workflows across finance, supply chain, HR, and customer service. Automation will shift to Human-AI co-steering, where AI proposes and executes while humans supervise and govern. Oversight will become a design principle to ensure trust and resilience. Intelligent operations will enable businesses to move from reactive to proactive, reducing inefficiencies and improving agility. Apps and operations will continue to evolve rather than remain static, predefined, or manually maintained.

Why it matters: In 2026, organizations will shift from pilots to full production, from fragmented automation to complete end-to-end value chains, but success will rely on the reliability and scalability of AI agents and the effectiveness of Human-AI collaboration.

The borderless paradox of tech sovereignty: Amid geopolitical uncertainty, tech sovereignty has moved from a policy concept to a strategic priority. Nations and enterprises now seek control over critical technologies in a world that remains deeply interconnected. The result is a new paradox: sovereignty is no longer defined by isolation, but by resilient interdependence. Since full tech autonomy does not exist, organisations will focus on risk mitigation and selective control over key layers. Securing business continuity will become the primary imperative through diversified suppliers and sovereign alternatives. Sovereign and multi-clouds, regional AI models, open platforms and new chip ecosystems are also emerging to offer choice and strategic flexibility.

Why it matters: In 2026, the race for control over the critical stacks of the digital value chain will continue, from semiconductors to data storage, all the way through to AI models, while most hyperscalers and large cloud providers are likely to launch sovereign cloud offers. This will have a profound impact on how companies mitigate risks and ensure resilience.