2026年顶级科技趋势

Who should readthis report and why? Table of contents This report is designed for C-suiteexecutives and business and innovationleaders. The report presents ourconvictions regarding what will be themost impactful technological trendsof 2026. It offers valuable insights into Cloud 3.0 –all flavors of cloudThe borderless paradox oftechnological sovereignty22Emerging signals to watchby 2030 and beyondConcluding remarks29 03Introduction The year oftruth for AI06AI is eating10 softwareThe rise ofintelligent ops14 executives, the investor community, andin-depth discussions with experts supportour predictions. The insights we derive fromthis analysis will help technology and business Pascal Brier Group Chief Innovation Officer,Member of the Group ExecutiveCommittee, Capgemini Introduction At the same time, the global environment isforcing companies to rethink resilience andbusiness continuity at a much deeper level.Rising dependencies on critical technologies(from semiconductors and cloud services toAI models and compute infrastructure) havebecome strategic risk factors rather thanpurely technical choices. This is driving a dualmovement: a renewed push for architecturesthat can withstand disruption, and a search for After several years of extraordinaryacceleration across AI, cloud, data, andautomation, 2026 marks a shift towardstrengthening, upgrading or rebuilding the open, scalable, and globally connected,while ensuring that no single dependencycan compromise their ability to operate. toward structural rebuilding, pointing to a singlemessage: technology leadership in 2026 is nolonger about experimentation, but aboutconstructing the durable foundations that progress cannot rest on fragmented pilots orloosely connected digital initiatives. The eraof experimental AI is giving way to the needfor solid AI foundations: reliable data, cleargovernance, scalable architectures, and systemsdesigned for safety, trust, and measurable it is the strength of these foundations, not thenovelty of individual tools, that determineswho captures long-term advantage. This reportaims to help business and technology leaders The top techtrends of 2026 The year oftruth for AI The year oftruth for AI After a period of unprecedented investmentand experimentation, AI has become thedefining technology of the decade. Yet thepace of investment has outstripped the speedat which organizations have been able todeploy it at scale and extract measurablevalue. Many enterprises now find themselves This is why 2026 emerges as the year of truth forAI. Short-term hype fades, but what remains is anecosystem increasingly grounded in operational value,enterprise architecture, and sustained productivity.As with past technology waves, real growth begins of 7–18% across core digital and softwareoperations1. Crucially, these gains are not merelyabsorbed as efficiency: half of organizationsreinvest the time saved into developing newfeatures, while nearly as many channel it into and function. Large models are becoming moremodular, agents are moving from novelty toolsto workflow orchestrators, and AI is shiftingfrom peripheral experimentation to deeperintegration within enterprise cores. Adoptionreflects this transition. Today, roughly 46% ofthe software workforce uses generative AI must confront their true AI readiness, starting withdata foundations and infrastructure. The agenticwave is accelerating, but not all agents are built toscale; hastily assembled “toy agents” risk renewingdisappointment. Differentiation no longer comesfrom the models themselves, which are rapidly more consequential is taking shape. Thestructural foundations of AI are maturing.Organizations that have moved beyond pilotsare already seeing tangible results, with What to look out for After years of hype and fragmented pilots, AIcan no longer be innovation theater. Investmenthas outpaced value delivery, and 2026 is themoment when organizations must move fromproof-of-concept to proof-of-impact. The nextwave of AI is not about specific tools or modelreleases; it is about embedding intelligence intothe fabric of operations, processes, and society As general-purpose LLMs commoditize,enterprises prioritize smaller, fine-tunedmodels tailored to finance, healthcare, retail,or industrial operations. These models relyon improved retrieval, vector databases,and continuous fine-tuning pipelines, giving Organizations increasingly turn fromexperimental AI agents toproduction-gradeagentic systemsbuilt to operate within realenterprise architectures. This shift favorsplatforms that integrate directly with existingdata pipelines, identity layers, workflowengines, and business applications. Early proofs of concept give way to robust At the same time, the pressure to demonstrateconcrete ROI accelerates investment inAI observability, evaluation, and valuemeasurement.Companies establish internalevaluation suites to test model behavior,monitor agent decisions, and assess reliability