Gartner Announces Top Predictions for Data and Analytics in 2026

"The pace of change in data and artificial intelligence is so rapid that each year feels like stepping into a new chapter of a science-fiction novel," said Rita Sallam, Distinguished VP Analyst at Gartner. "In 2026, the boundaries between human, machine, and organizational intelligence will continue to blur. Businesses rely on data in unprecedented ways, with AI systems not just supporting us, but collaborating as partners. These predictions offer leaders a roadmap to prepare for the opportunities and challenges that lie ahead."

By 2027, 75% of hiring processes will include certifications and testing for workplace AI proficiency during recruiting.

The urgency for an intentional AI-driven workforce strategy stems from the breakneck pace of AI innovation; leaders who fail to modernize their tech talent strategies risk leaving their organizations permanently behind competitors who have successfully unlocked human-AI collaboration.

"D&A leaders should encourage rigorous, data-driven measurement of skills to surface deficits that stand between their AI-ambition and IT workforce readiness," said Sallam.

Through 2027, GenAI and AI agent use will create the first true challenge to mainstream productivity tools in 30 years, prompting a $58 billion market shakeup.

Developing new content today increasingly begins with GenAI taking vast amounts of content and synthesizing it in myriad ways, rather than starting with a blank canvas. Editing frequently involves having AI continually rewrite content rather than the author doing it manually.

AI will continue to trigger new competition for productivity suites as value shifts to agentic AI experiences. D&A leaders must demand tools built for today, such as new user interfaces, plug-ins, document types, and formats.

By 2029, AI agents are projected to generate 10 times more data from physical environments than from all digital AI applications combined.

Agentic AI applications in the physical world are producing vast amounts of trajectory data across logical, spatial and multiagent scenarios as they interact with their environments. This presents a unique opportunity for world models to learn patterns from the data and make accurate predictions and simulations.

By 2030, 50% of organizations will use autonomous AI agents to interpret governance policies and technical standards into machine-verifiable data contracts, automating compliance and governance policy enforcement.

By 2030, 50% of AI agent deployment failures will be due to insufficient AI governance platform runtime enforcement for capabilities and multisystem interoperability. In the near-term, ungoverned decisions using LLMs will cause financial or reputational loss for enterprises.

"D&A leaders should experiment with data governance agents in low-risk pipelines to orchestrate and automate negotiation processes," said Sallam. "They'll need to validate that agents can correctly interpret context and protocols in a controlled environment before trying to scale further. Analytic workflows should also be redesigned to include a required evaluation stage."

By 2030, 60% of organizations achieving successful differentiation with AI will be led by executives who prioritize mastery of human relational skills.

CDAOs with strong coalition-building and influence skills are advancing into more powerful C-suite roles, including CEO, as organizations recognize the value of human-led strategic vision in leveraging AI.

By 2030, universal semantic layers will be treated as critical infrastructure, alongside data platforms and cybersecurity.

Developing a universal semantic layer is now a must?do for D&A leaders either leading or supporting AI. It is the only way to improve accuracy, manage costs, substantially cut AI debt, align multiagent systems, and stop costly inconsistencies before they spread. D&A leaders must budget for semantic capabilities as a nonnegotiable foundation.

By 2028, 50% of content risk roles will migrate from legal and cybersecurity to AI engineering to address the inherent risk caused by siloed assurance processes.

Risk mitigation functions are increasingly being integrated into AI engineering, data science and software development processes. These teams are expected to design systems that generate and curate content intelligently and assume responsibility for mitigating the associated risks by building in embedded controls by-design. This enables faster, responsible innovation within ethical and legal boundaries, particularly where the AI model's decision must be based on the user's context.

• ข่าวo:member
• ข่าวo:loc
• ข่าวen
• ข่าวo:it