Gartner：2026年数据分析和人工智能规划指南（英文版）（13页）.pdf

1、Sumit Agarwal,Georgia OCallaghan,Christopher Long,Wilco van Ginkel,Maryam Hassanlou,Zain Khan,Cuneyd Kaya,Ral Arrabales2026 Planning Guide for Analytics and Artificial IntelligenceGartner InsightsGartner,Inc.|G00837539Page 1 of 282026 Planning Guide for Analytics and ArtificialIntelligence13 October

2、 2025-ID G00837539-30 min readBy:Sumit Agarwal,Georgia OCallaghan,Christopher Long,Wilco van Ginkel,Maryam Hassanlou,Zain Khan,Cuneyd Kaya,Ral ArrabalesInitiatives:Analytics and Artificial Intelligence for Technical Professionals;Architect,Implement and Scale Data and Analytics Solutions;Generative

3、AI Resource Center In 2026,organizations must maximize business value whileaccelerating AI and analytics implementations.To achieve this,data and analytics technical professionals should utilize allavailable data,design modular and flexible architectures,andimplement robust governance to ensure trus

4、t.OverviewKey FindingsThe convergence of analytics and AI enables organizations to analyze structuredand unstructured data through conversational interfaces.While this approach istransformative,it depends on the maturity of AI agents,large language model(LLM)technologies and semantic architectures t

5、o deliver reliable analytical outcomes.More organizations are deploying generative AI(GenAI)use cases in production.Most implementations focus on productivity gains,but face challenges with useradoption and achieving measurable returns on investment.Heightened risk perception and regulatory scrutiny

6、 around GenAI have increasedorganizational concerns and delayed approvals and implementations.Risks stemfrom the technologys black-box nature and concerns around safety,data privacyand reliability.The pressure to leverage data for competitive advantage and better decision making,alongside rapidly ev