2026年数据与人工智能趋势报告

Report Overview Introduction: The (Great) DisconnectThe widening gap between AI ambitions and operational reality across industries Top Data & AI Trends for 2026 AI success in 2026 will be driven by data infrastructure, not new modelsWhy modern data infrastructure, not the latest AI model, delivers the highest ROI for enterprises1. Organizations are moving from broad experimentation to specific, high-valueuse casesThe shift from hype to focus: proven, measurable applications replacing unfocused experimentation2. AI is evolving from proof-of-concept to enterprise-wide deploymentAI moves from testing and pilots to strategic, production-ready adoption across industries3. Companies are rethinking short-term, tech-first AI strategies that fail to scaleHow organizations are learning from early mistakes4. Semantic modeling, conversational intelligence, and governance are becomingcritical differentiators5. Enterprises are prioritizing data lifecycle management, modernization, andhuman capability6. High-performing companies are aligning data, people, and purpose to scale AIresponsibly7. Industry-Specific Trends for 2026 Sector forecasts for airlines, retail, media, healthcare, and technology, and how AI is reshapingoperations, innovation, and talent How to Prepare for 2026Key actions to build readiness and avoid common pitfalls Conclusion: The Base Determines the FutureThe three paths ahead and why strong foundations decide which one you take 2026 Trends Report: Data & AI Expert insights from DataArt on where AI actually works,the common failures holding companies back, and thepriorities that will define success over the next 18 months. Introduction: The (Great) Disconnect This report synthesizes findings from comprehensive interviews withDataArt’s senior data, AI, and technology leaders conducted in Septemberand October 2025. Throughout this report, you’ll find direct insights from theseexperts — practitioners who architect data platforms, deploy AI solutions, andguide enterprise transformation daily. Their perspectives reflect real-world A fundamental gap exists between what organizations expect from AI andwhat it actually takes to deliver. Decision-makers pursue transformational wins This divide manifests across industries. Companies announce AI initiativesin press releases while finance teams manually copy data between systems.Executives champion data-driven decision-making, then override analyticswhen results conflict with intuition. Technology departments operate as off-shore service providers rather than strategic partners, creating fragmented The cultural sector, for instance, reveals this mismatch quite vividly. Or-ganizations enthusiastically adopt AI and extended reality tools for visitorengagement, yet 82% of cultural institutions surveyed lack the data govern-ance frameworks and staff skills for production deployment. The music industry While companies claimto be embracing cut-ting-edge innovation,many of their workflowsremain untouched by it.It’s not about adoptingGenAI, but more about Financial services tells a similar story. GenAI dominates conversationsabout productivity, potential layoffs, and competitive advantages. Yet actualimplementation primarily occurs within technology teams and advanced an-alytics groups. Core business functions in finance, risk, and fund management What passes for AI adoption in most enterprises amounts to employeesusing ChatGPT for search and email generation.Real adoption means com-panies use AI to automate processes, enable new capabilities, and serve their The distinction between transformation and modernization gets lost. Many companies talk about transformation when they are actually doing moderni-zation. They claim to use technology to reinvent their business through AI, dataplatforms, and automation. In practice, most effort goes into fixing legacysystems, cleaning data, or integrating tools that do not talk to each other. That Investments that companies have made in their cloud computing platformsare driving the most business results right now. This is because of the maturityand scale of where these projects can impact businesses — a scale that AI, at True transformation happens when technology actually changes howdecisions are made, how people work, and how customers experience the Top Data & AI Trends for 2026 I’m a strong believerthat improving datamanagement is core toeverything our clientsdo. You can’t go far ifyour enterprise datais siloed, difficult tomodify, hard to access,governed in a way that AI success in 2026 will be driven by data infra-structure, not new models1 If 2025 was the year of AI experimentation, 2026 will be the year of foun-dational reckoning. The highest-ROI technology investment right now is datainfrastructure, not the latest AI model. Building proper pipelines, establishing aclear company-wide approach to data management, and making data highly available and as close to real-time as possible represents t