2026年行业数据与AI终极指南：从炒作到商业价值，驾驭数据、生成式AI与AI智能体

TABLE OF CONTENTS Introduction3Navigating the AI Adoption Landscape: Challenges and Considerations5Transforming Business Functions Across Industries with AI7How AI Can Power Success in Seven Industries9Financial Services10Advertising, Media and Entertainment13Healthcare and Life Sciences15 INTRODUCTION The rapid emergence of agentic AI over the past year is perhaps one of thebest demonstrators of how fast AI — and the need for strong data practicesto support it — is moving. Generative AI, on the other hand, was the excitingnew tool a few years ago and has progressed from experimental hype to beingembedded across business functions. In just the past two years, organizationsfrom every sector went from scrambling to figure out how best to capitalize Not only that, but organizations that are further along in their AI adoption areusing AI agents across their operations. These are sophisticated models capableof performing complex, multi-step tasks independently, with little or no human The potential uses for and value of AI, including new agentic capabilities, arevast and span virtually every major industry. In this guide, we will explore myriadways that organizations in a range of industries are leveraging data and AI to Healthcare:Using vast patient datasets to reveal patterns, predict healthoutcomes, and enable more precise diagnoses and personalized treatments, Financial services:Rapidly analyzing extensive market data to identifyemerging trends, inform strategic investment decisions for maximizing of early adopters worldwide report that their genAI investments have already paid for themselves. Retail:Transforming customer data into highly personalized shoppingjourneys, boosting customer satisfaction and fostering lasting loyalty, Public sector:Enhancing the ability to predict disease outbreaks anddisaster impacts, facilitating the swift and accurate deployment of Manufacturing:Employing AI-driven visual inspection systems to detectunusual patterns and deviations in production, identify quality issues and Advertising, media and entertainment:Extracting deep insightsfrom unstructured data to pinpoint customer behaviors, sentiments Telecommunications:Proactively identifying and resolving networkissues and service disruptions to enhance service quality, reliability In the next few years, many organizations will roll out new AI use cases,citing the potential for significant returns, the competitive pressure to NAVIGATING THE AI ADOPTION LANDSCAPE:CHALLENGES AND CONSIDERATIONS The ESG research confirms the acceleration of AI adoption. In fact, 57% of the 3,324 organizations surveyed are currentlyusing commercial or open-source gen AI solutions, and 98% of organizations are planning to increase their investments in AI initiatives in 2025. FOUNDATIONAL DATA HURDLES But before we dive into industry exploration, we have tonote that the adoption journey is not without its challenges.Companies have to navigate the considerable and fluctuatinggovernance, security and ethical considerations that come withit — not to mention organizational hurdles, data issues and the if the data isn’t varied or comprehensive enough, the scope andaccuracy of AI models will be limited. And managing sensitiveinformation requires robust security and compliance measures, A recurring theme across all forms of AI adoption is thecritical role of data: “There is no AI strategy without a datastrategy” — but many organizations struggle with fundamentaldata readiness. The research highlights that even among early These data challenges frequently lead to extended deploymenttimelines, with 77% of surveyed organizations reporting that Only 11% Other key data-related challenges include the management,quality, sensitivity and diversity of data for AI use. For example,tasks like data labeling and preparation are often arduous andslow. Problems with accuracy, bias, relevance and timeliness of businesses report that more thanhalf their unstructured data is readyfor use in LLM training and tuning.¹ •Human-AI collaboration and handoffs:Defining when andhow AI agents should hand off tasks to humans especiallyin high-stakes scenarios (for example customer service GEN AI: BEYOND THE HYPE THE EMERGING CHALLENGES OF AI AGENTS While gen AI has demonstrated ROI, its implementation comeswith its own set of complexities: The AI evolution toward autonomous agents brings •Cost overruns:Despite positive returns, 96% of earlyadopters report that one or more components of their gen AIsolutions have exceeded initial budget expectations, primarily •Accuracy and trust:Autonomous AI agents, particularly data-focused ones, require precise data handling. Inaccuracies orflawed reasoning can render entire workflows unreliable and •Transparency and explainability:AI agents can functionas “black boxes” making it difficult for users to understandtheir decision-making processes. This lack of transparency •Integration complexity:Integrating AI agents sea