2026年软件领域人工智能与分析趋势状况报告

Table of Contents Methodology 03 TREND 03 Context is the Trust, security,and compliance are Automation movesfrom testing to 16 05 What this meansfor product and TREND 05 The interface fades AI talent continuesto evolve and be on 24 34 This year, 85% of organizationscite innovation as a core strategic Each year, we publish this report to help product anddevelopment leaders make sense of the fast-movingtrends shaping the software industry—especially for thosebuilding in HR tech. As AI continues to rewrite each andevery one of our playbooks, the pressure to keep pace You may notice something different this year: we’vedropped the “gen” from “genAI.” Why? Because AIhas continued to evolve—unsurprisingly—and it nowencompasses far more than just generative use cases.From automation to agentic systems to predictive insights, There’s still no roadmap for what’s next—and that’s whatmakes this era so challenging and so full of promise. AI’sacceleration hasn’t slowed down, but its applicationhas matured. There has never been a more exciting timeto build software. Vendors are now moving past early For all software vendors, the balancing act continues:navigating resource constraints while trying to delivermore innovation, faster. The stakes are high. Customerswant real-time insights, not dashboards that gather dust tomake decisions when it actually matters. Security concerns But where there’s pressure, there’s momentum. In 2026, AIwill be more than a headline—it will be the engine behindthe software that wins. And winning means deliveringexperiences that users actually adopt, building intelligence In this report, we explore how product and developmentleaders and managers are responding to this pivotalmoment. Whether it’s operationalizing AI, rethinkingdata infrastructure, or refining AI for what matters, we’vecurated the most pressing trends shaping the year ahead. Visier surveyed 788 respondents in product and development globally, conducted in July 2025. Our primary data has been supplemented by secondary research from Deloitte, PwC, Gallup,Gartner McKinsey and Company, Boston Consulting Group, Josh Bersin Company, IDC, and more. OVERVIEW OF VISIER’S STATE OF AI AND ANALYTICS SURVEY •788 respondents•60% North America, 14% Europe, 6% Middle East/Africa, 18% South America, 3% Asia Pacific •42% Executives (Directors and above)•48% Managers and practitioners Major Findings The surge in Agents moving The innovation– Data: thecritical barrier Despite 79% claiming tohighly value innovation,only 5% have reachedmaturityin AI agentdevelopment 51% building agentsthat trigger workflows(embedding automation 57% of respondentscite data quality issuesas the top barrier for AI,analytics and beyond. 65% of resources are being funneleddirectly into AI projects–higher than last yearwith investments inagentic AIexpected toincrease 20%in the AI is no longer just aboutinsights—it’s aboutaction. More than half ofproduct teams are alreadyexperimenting with agenticAI that doesn’t just analyzebut actually executestasks within workflows. But there’s a catch: whileinvestment is accelerating,the data foundation isn’tkeeping up. Poor dataquality continues to blockteams from realizing thepromise of agentic AI.Without clean, contextual,and trustworthy data, The ambition is there, butexecution lags behind.Nearly every companytalks about innovation,yet only a small fractionhave moved beyondexperimentation to mature,production-ready AI agents.This gap highlights both AI is no longer a sideproject—it’s the centerpieceof product roadmaps.Development leaders areunder pressure to show ROIquickly, and much of thatinvestment is flowing intoagentic AI: software agentsthat can act autonomously, TREND 01 Context is thecurrency of AI Why generic models are dead Key takeaways Knowledge AugmentedGeneration (KAG) andsemantic layers areemerging as the bridge Vendors who ground AIin context-rich, trusteddata will prove ROI fasterand win adoption. AI models withoutdomain-specific datarisk delivering shallow The context crisis: why AI falls short The dirty secret of the AI revolution is that mostimplementations deliver remarkably generic insights. AnHR AI might recommend “better communication” insteadof recognizing that your remote employees in the Pacifictimezone consistently miss critical decisions made during In our 2026 State of AI and Analytics survey, 57% ofrespondents indicated data quality issues as one of the topbarriers: employees often receive outputs that are technicallyfluent but irrelevant to their workflow. Informatica’s globaldata-leader survey also shows 42% cite data quality as ablocker. The issue isn’t performance. It’s grounding. A large This context gap isn’t just frustrating—it’s expensive.McKinsey finds inaccuracies are the most recognized andexperienced gen-AI risk and 47% of organizations reportat least one negative consequence from gen-AI use (e.g.,incorrect or biased outputs). Users quickly le