2026年五大科技趋势报告（英）.pdf

Top Tech Trends of 2026Table of contentsWho should read this report and why?03IntroductionThis report is designed for C-suite executives and business and innovation leaders.The report presents our convictions regarding what will be the most impactful technological trends of 2026.It offers valuable insights into the trends we see dominating the tech landscape,while looking back on the accuracy of our predictions for 2025.Data from comprehensive surveys of industry executives,the investor community,and in-depth discussions with experts support our predictions.The insights we derive from this analysis will help technology and business leaders in establishing sound strategies and impactful investments.The year of truth for AI06AI is eating software10The rise of intelligent ops14Cloud 3.0 all flavors of cloud18The borderless paradox of technological sovereignty22Emerging signals to watch by 2030 and beyond26Concluding remarks2902Top Tech Trends of 2026|CapgeminiIntroductionAfter several years of extraordinary acceleration across AI,cloud,data,and automation,2026 marks a shift toward strengthening,upgrading or rebuilding the foundations that will support the next decade.Across industriesleaders recognize that progress cannot rest on fragmented pilots or loosely connected digital initiatives.The era of experimental AI is giving way to the need for solid AI foundations:reliable data,clear governance,scalable architectures,and systems designed for safety,trust,and measurable outcomesThe organizations able to move from isolated models to integrated,enterprise-wide intelligence will be those that generate lasting value.At the same time,the global environment is forcing companies to rethink resilience and business continuity at a much deeper level.Rising dependencies on critical technologies(from semiconductors and cloud services to AI models and compute infrastructure)have become strategic risk factors rather than purely technical choices.This is driving a dual movement:a renewed push for architectures that can withstand disruption,and a search for greater control over the layers of technology that matter most.Cloud strategies are evolving accordingly,with hybrid,multi-cloud,and sovereign options emerging not as exceptions but as mechanisms to secure continuity,reduce concentration risk,and safeguard data and operations.Sovereignty is part of this shift,but the underlying theme is broader:organizations are redesigning their foundations to remain open,scalable,and globally connected,while ensuring that no single dependency can compromise their ability to operate.Our Top Tech Trends for 2026 reflect this shift toward structural rebuilding,pointing to a single message:technology leadership in 2026 is no longer about experimentation,but about constructing the durable foundations that will enable true value to be extracted from innovation.As every major technological shift has shown,it is the strength of these foundations,not the novelty of individual tools,that determines who captures long-term advantage.This report aims to help business and technology leaders make the right strategic choices at a moment when those foundations are being rebuilt.Pascal BrierGroup Chief Innovation Officer,Member of the Group Executive Committee,Capgemini03Top Tech Trends of 2026|CapgeminiLooking back to the top tech trends of the last two years20252024Generative AI:From copilots to reasoning AI agentsGenerative AI:Small will be the new bigCybersecurity:New defenses,new threatsQuantum technologies:When cyber meets quantumAI-driven robotics:Blurring the lines between humans and machinesSemiconductors:Moores Law isnt dead,but it is changingNuclear:The surge of AI driving the clean tech agendaBatteries:The power of new chemistryNew-generation supply chains:Agile,greener and AI-assistedSpace tech:Addressing the Earths challenges from outer space04Top Tech Trends of 2026|CapgeminiThe top tech trends of 2026The year of truth for AI06Top Tech Trends of 2026|CapgeminiThe year of truth for AIAfter a period of unprecedented investment and experimentation,AI has become the defining technology of the decade.Yet the pace of investment has outstripped the speed at which organizations have been able to deploy it at scale and extract measurable value.Many enterprises now find themselves with sophisticated models,agents,and prototypes that remain unintegrated,under-utilized,or disconnected from real business outcomes.This gap has generated some skepticism and a sense of some form of AI hype.Beneath the noise,however,something more consequential is taking shape.The structural foundations of AI are maturing.Organizations that have moved beyond pilots are already seeing tangible results,with early adopters reporting productivity gains of 718%across core digital and software operations1.Crucially,these gains are not merely absorbed as efficiency:half of organizations reinvest the time saved into developing new features,while nearly as many channel it into workforce upskilling.This marks a shift from experimentation to value compounding.At the same time,AI itself is evolving in form and function.Large models are becoming more modular,agents are moving from novelty tools to workflow orchestrators,and AI is shifting from peripheral experimentation to deeper integration within enterprise cores.Adoption reflects this transition.Today,roughly 46%of the software workforce uses generative AI tools;by 2026,that figure is expected to reach 85%2,signaling a move from early adoption to default capability.This is why 2026 emerges as the year of truth for AI.Short-term hype fades,but what remains is an ecosystem increasingly grounded in operational value,enterprise architecture,and sustained productivity.As with past technology waves,real growth begins once organizations recognize that value does not lie in isolated use cases but in enterprise-wide systems that evolve and scale over time.Reaching that future requires discipline.Organizations must confront their true AI readiness,starting with data foundations and infrastructure.The agentic wave is accelerating,but not all agents are built to scale;hastily assembled“toy agents”risk renewing disappointment.Differentiation no longer comes from the models themselves,which are rapidly commoditizing,but from architecture,integration,orchestration,and the ability to turn AI into durable,compounding business value.07Top Tech Trends of 2026|Capgemini08Why it mattersAfter years of hype and fragmented pilots,AI can no longer be innovation theater.Investment has outpaced value delivery,and 2026 is the moment when organizations must move from proof-of-concept to proof-of-impact.The next wave of AI is not about specific tools or model releases;it is about embedding intelligence into the fabric of operations,processes,and society(and making it work for everyone).Leaders who succeed will build the capabilities,governance,and human-AI chemistry required to deliver measurable outcomes at scale,while laying the foundations for the larger-scale transformation that will follow.What to look out forOrganizations increasingly turn from experimental AI agents to production-grade agentic systems built to operate within real enterprise architectures.This shift favors platforms that integrate directly with existing data pipelines,identity layers,workflow engines,and business applications.Early proofs of concept give way to robust orchestration frameworks that coordinate multiple specialized agents,each with defined roles,evaluation loops,and governance controls.Momentum also builds around modular and domain-specific models.As general-purpose LLMs commoditize,enterprises prioritize smaller,fine-tuned models tailored to finance,healthcare,retail,or industrial operations.These models rely on improved retrieval,vector databases,and continuous fine-tuning pipelines,giving organizations tighter control over accuracy,provenance,and performanceespecially in regulated environments.08Top Tech Trends of 2026|Capgemini“Generative AI has attracted unprecedented levels of investment and attention.As we move into 2026,the conversation is shifting decisively toward value creationmoving beyond experimentation to measurable business impact.”Mark RobertsHead of AI Futures Lab,CapgeminiAt the same time,the pressure to demonstrate concrete ROI accelerates investment in AI observability,evaluation,and value measurement.Companies establish internal evaluation suites to test model behavior,monitor agent decisions,and assess reliability against business outcomes.Dedicated“AI value offices”or governance teams emerge to oversee performance at scale,drawing on telemetry,productivity insights,and financial impact.Finally,the rapid adoption of AI-augmented engineering and operations signals that intelligence is becoming embedded across the software development lifecycle and core business workflows.Code generation pipelines,automated testing agents,self-optimizing data workflows,and AI copilots for operations move from experimentation to standard practice.These shifts reinforce the broader transformation described across this report:AI is no longer an add-onit is becoming a structural capability of the modern technology stack.Top Tech Trends of 2026|Capgemini09AI is eating software10Top Tech Trends of 2026|Capgemini本报告来源于三个皮匠报告站（）,由用户Id:349461下载,文档Id:1047092,下载日期:2026-01-15AI is eating softwareFor more than two decades,software has powered digital transformation.After the era when“software ate the world,”we now enter a new phase where“AI is beginning to eat software itself”.What started as isolated AI tools(code completion,automated testing,prompt-based generation)has evolved into a fundamentally new software development paradigm,where humans and AI continuously conceptualize,design,build and refactor systems together.AI-native development is no longer experimental.Large enterprises,in particular,are moving first:three-quarters of organizations with more than$20 billion in annual revenue have already piloted or scaled generative AI for software3 engineering.Enterprises are moving beyond AI-assisted coding to fully autonomous software development ecosystems,deploying self-directed testing frameworks,intelligent code-generation agents,continuous auto-refactoring engines,and agentic build-and-release systems that collaborate,learn,and optimize with human interventions.The impact is transformative:delivery cycles accelerate,technical debt is resolved earlier,and developers shift from manually writing code to expressing intent and orchestrating intelligent pipelines.In 2026,this shift becomes structural.Developers increasingly describe outcomes in natural language or high-level specifications.AI generates the design,implements it,tests it,secures it,optimizes it,integrates it and continuously refactors it as requirements evolve.A new model emerges where AI-assembled components adapt in near real time and software becomes an evolving service rather than a static asset.At this point,the traditional concept of an application will begin to fade.Users express goals,and AI agents dynamically assemble,run,and maintain the underlying logic.The visible app layer shrinks;behind it,composable AI services evolve autonomously,self-testing,self-healing,and updating themselves as new conditions arise.This transition brings enormous opportunity.It also demands a fundamental unlearning of old software habits.AI-generated code is powerful but not infallible,making governance,validation,and architectural oversight more critical than ever.The skills that once differentiated developers(package configuration,front-end coding,manual quality assurance)lose importance.The new currency of expertise becomes systems thinking,AI orchestration,architecture,and the ability to manage complex autonomous toolchains.11Top Tech Trends of 2026|Capgemini12Why it mattersThis shift is not only technological;it is strategic.As enterprises reach the limits of traditional DevOps,AI-native development introduces a new pathway to speed and agility.Agentic pipelines can generate,test,and refactor software continuously,shrinking quality assurance cycles and enabling near-real-time adaptation.This is a foundational element in building AI-native businesses.When software evolves automatically rather than through manual releases,systems become adaptive,allowing organizations to respond to market changes far faster than static architecture currently allows.At the same time,AI-generated software opens new options for digital sovereignty.By lowering the cost and effort required to design,maintain,What to look out forEnterprises begin deploying agentic build systems that generate,test,secure,and refactor code continuously.These systems go far beyond todays code assistants and act as full orchestration engines,translating intent into production-ready code and maintaining it as requirements evolve.Early adopters already use them to reduce technical debt and compress release cycles dramatically.and evolve software,AI-native development reduces the economic barriers that have historically pushed organizations toward large,standardized SaaS platforms.This makes it viablewhere strategic control,regulatory constraints,or data locality matterto replace monolithic SaaS with tailored systems whose codebase,data flows,and evolution remain under direct organizational control and aligned with strategic autonomy goals.Finally,this shift frees human talent for higher-value work.Automation of routine software development lifecycle tasks redirects engineers focus toward architecture,product logic,and governance,provided they are able to build on traditional practices and master new AI-driven toolchains.Together,these forces redefine how software is built,how fast it evolves,and how enterprises differentiate in an AI-driven era.12Top Tech Trends of 2026|CapgeminiAnother signal of this shift is the rise of dynamic,composable services assembled automatically by AI.Instead of treating applications as fixed assets,enterprises move toward adaptive Service-as-Software models where components are assembled,optimized,and updated in near real time.This enables greater differentiation and supports the evolution toward sovereign,custom-built systems.Finally,AI-governed development environments become essential to manage this transition.Evaluation frameworks,behavioral monitoring,lineage tracking,and architectural guardrails mature to ensure autonomous toolchains remain reliable,controllable,and aligned with enterprise requirements.We also see rapid advances in autonomous quality assurance and reliability pipelines,where test generation,regression detection,vulnerability scanningand dependency management are handled end-to-end by AI.As manual QA becomes impractical at the speed AI enables,these reliability pipelines form the backbone of trustworthy AI-generated software.“The fundamentals of software creation are being rewritten.We are entering an era of AI powered software