gettyAI is rapidly taking over many of the time-consuming tasks involved in working with data, from organizing information and generating reports to spotting patterns and producing visualizations. As these technical processes become faster and more accessible, data analysts are being called on to contribute in broader ways by bringing business context, human judgment and strategic thinking to work that can’t simply be handed off to an algorithm.This evolution may change not only what analysts do each day but also where they fit within an organization and how accountable they are for the decisions AI helps inform. Below, members of Forbes Technology Council discuss how the data analyst’s role is changing and why that shift will matter as companies rely more heavily on AI-driven insights.Adding Decision Analysis And ArchitectureOrganizations are moving from data-centric to decision-centric as real-time decision-making becomes a critical competitive advantage. With this shift, data-focused roles are evolving to include decision analysts and decision architects. We’re already seeing this take shape across our customers as AI powers decision intelligence and business outcomes at scale. - Frederic Laluyaux, Aera TechnologyPrioritizing Interpretation Over ExecutionOne way the data analyst role is evolving is that AI now handles the manual work of pulling and cleaning data, so analysts focus more on interpreting results and asking the right questions. That shift matters because judgment, not technical execution, is becoming what separates a good analyst from a tool that just spits out answers. - Rahul Bhatia, HCL TechForbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?Designing Better Business DecisionsData analysts are moving from report builders to decision designers. AI can draft charts and summaries fast, but someone must frame the right question, test assumptions and turn insight into action with an owner. That shift matters because bad questions produce confident nonsense. Good analysts protect the business from pretty but wrong answers. - Maman Ibrahim, EugeneZonda Cyber Consulting ServicesGoverning AI-Ready Data WorkflowsAI isn’t replacing data analysts—it’s elevating them. As AI takes over routine pipeline monitoring and anomaly detection, analysts are shifting from data wranglers to orchestration stewards, ensuring AI workflows run on fresh, governed, production-grade data. That shift matters because AI ROI depends not on models alone but on ensuring model output drives reliable business process execution and results. - Charles Crouchman, Redwood SoftwareDetermining Which Insights MatterAs AI makes retrieval easy, the data analyst’s role shifts from finding information to deciding what matters. AI can surface patterns, summarize reports and generate dashboards. The analyst’s value increasingly comes from judgment: asking better questions, recognizing meaningful signals and helping organizations notice the insights that drive decisions. - Jeffrey Highman, TruaOrchestrating Insights Across The BusinessOne way the data analyst role is evolving is from report builder to insight orchestrator. AI can generate charts and summaries faster, but analysts must validate context, define business meaning, ask better questions and translate outputs into decisions. This shift matters because trusted judgment—not just dashboard creation—is becoming the real value. - Venkata Kondepati, AscenttShifting Toward Judgment-Centered WorkFor years, data analysts spent chunks of their day on tasks that never needed a human, like repetitive queries, manual aggregation and routine reporting. Now, with AI and automation doing the heavy lifting, the human in the loop finally gets to act like one—applying judgment, asking harder questions and turning insight into decisions that move the business forward. That’s the shift that really matters. - Alex Ford, Encompass CorporationTaking Accountability For AI-Generated AnalysisWith AI, it’s possible to produce an analysis in near-real-time and nearly free. The problem is that the analysis isn’t always true. That means that the analyst’s job is shifting from generating the answer to being accountable for it. After all, accountability is the one thing you can’t hand to a model. - Bruno Billy, APGARInterrogating AI Outputs Before ActingThe data analyst is moving from query writer to judgment layer. AI can generate the SQL, surface the pattern and build the dashboard. What it cannot do is decide whether the pattern is real, whether it matters and what action to take. The analyst’s value has shifted from extracting data to interrogating what the system surfaces. Analysts who just pull reports will be automated. The ones who become the judgment layer between AI output and business decisions will be harder to replace than ever. - Diptamay SanyalManaging Data Quality And PipelinesWith AI, I think the role of a data analyst may shift dramatically to managing the pipeline of the data coming into the data warehouse and/or data quality, cataloging and tagging. This will shift analysis to the actual business users. When a user can use AI to create visuals, dig into information, find inferences in data and then automate that into a daily agent, does the data analyst role really survive? - Marc Kermisch, ProtolabsTurning Analysis Into Business StrategyThe role of the data analyst is evolving from report generation to strategic interpretation. As AI automates data collection and basic analysis, the real value shifts to asking better questions, validating outputs and translating insights into business decisions. This matters because competitive advantage increasingly comes from judgment and context, not simply access to information. - Faustino Júnior, First.DoctorCombining Human Judgment With AI InsightThe role of the data analyst is evolving, moving deeper into creative thinking and exploring new possibilities. AI will expand the role of a data analyst to a data strategist in many ways. When we combine human understanding with artificial intelligence, we create a shared intelligence that allows an analyst to operate at peak performance with “superhuman” confidence (HU+AI). The shift encourages people to embrace the power of AI rather than fear change. - Sharon K Daniels, Arria NLG LimitedValidating Context, Assumptions And ImpactOne way the role is evolving is from reporting data to challenging it. As AI automates analysis and generates insights, data analysts increasingly become validators of context, assumptions and business impact. That shift matters because better decisions depend not just on faster answers but on ensuring the right questions are being asked in the first place. - Prajkta Waditwar, Box Inc.Translating Insights Into ActionData analysts are evolving into “data translators,” bridging the gap between raw AI-driven insights and actionable business decisions. This shift matters because AI can crunch numbers, but humans must interpret, contextualize and communicate the “why” behind the data to drive real impact. - Anil Jaiswal, U.S. BankMoving Into Data Product ManagementWith the rising importance of AI, data analysts are moving into the role of product managers. This brings data analysts to the forefront of product and service definition and development. That shift—from proactively shaping products to reactively reviewing data—is a foundational shift in the role, responsibilities and impact of data analysts in the world of agentic AI. - Mark Francis, CaregiverZoneCommunicating AI Insights To StakeholdersAs AI transforms how organizations handle data, data analysts are taking on more strategic roles, interpreting complex AI-generated insights, ensuring data quality, and communicating findings to stakeholders. This evolution is crucial because it empowers organizations to make informed decisions and fully leverage AI’s potential for business growth. - Will Conaway, Tuxedo Cat ConsultingTelling The Story Behind AI InsightsThe data analyst is shedding the spreadsheet and stepping into the role of AI storyteller, orchestrating models, challenging outputs and translating algorithmic whispers into human decisions. As GenAI automates the “what,” analysts own the “so what.” That shift matters profoundly because in a world drowning in data, wisdom still needs a human hand to steer it. - Bindu Madhavi Mangalampalli, CotivitiEnsuring Data Is Clean And ReliableAI now does the heavy lifting on data. So the analyst’s job shifts from making the numbers to making sure they can be trusted and are clean, ordered and reliable. That matters because AI repeats whatever you feed it. While good data helps you, bad data spreads the mistake. - Agustín Guerra, VangweAsking Better Questions And Catching AI ErrorsThe data analyst is no longer the one who finds the answer; AI does that in seconds. The new role is asking the right question and knowing when the machine is confidently wrong. As AI floods us with answers, judgment becomes the rarest skill. The analyst is becoming the translator between data and decision—the human who turns numbers into meaning. We don’t need faster answers. We need wiser questions. - Dr. Aditya Vikram Kashyap, Morgan StanleyShaping How AI Applies DataOne major shift is that data analysts are becoming workflow designers who shape how AI interprets and applies data, not just how it’s queried or visualized. As AI automates technical tasks, analysts focus more on context, guardrails and business logic. This matters because AI decisions are only as good as the workflows analysts define. - William Crane, OrbAid