Gartner analysts will discuss how to prepare for the impact of AI. in all aspects related to data and analytics during the Gartner Data & Analytics Conference, which will be held on April 28 and 29 in São Paulo (SP)
O Gartner, Inc.., a business insights and technology company, announces key forecasts for Data & Analytics (D&A) for 2026 and beyond. Artificial Intelligence (AI) is expected to impact all aspects related to data and analytics, including leadership, governance, talent, market dynamics, the need for context, and the universe beyond text-based models.
“"The pace of change in data and Artificial Intelligence is so rapid that each year feels like a new chapter in a science fiction novel," he says. Rita Sallam, Vice President and Emeritus Analyst at Gartner. “By 2026, the boundaries between human, artificial, and organizational intelligence will continue to blur. Companies are relying on data in unprecedented ways, with AI systems not only supporting us but collaborating as partners. These predictions offer leaders a roadmap to prepare for the opportunities and challenges ahead.”
Gartner analysts will comment further on these predictions for Data & Analytics at the Gartner Data & Analytics Conference, which will take place on April 28 and 29 in São Paulo (SP).
By 2027, 75% of hiring processes will include certifications and proficiency tests in AI applied to the workplace during recruitment.
The urgency of an intentional strategy for an AI-driven workforce stems from the accelerated pace of AI innovation; leaders who fail to modernize their talent strategies in technology risk leaving their organizations permanently behind competitors who have successfully unlocked collaboration between humans and AI.
“D&A leaders should encourage rigorous, data-driven measurement of skills to reveal the gaps that separate their AI ambitions and the readiness of the IT workforce,” says Sallam.
By 2027, the use of Generative Artificial Intelligence (GenAI) and AI agents will create the first real challenge to conventional productivity tools in 30 years, triggering a market restructuring worth US$$ 58 billion.
Today, the development of new content increasingly begins with the GenAI Collecting vast amounts of information and synthesizing it in numerous ways, rather than starting with a blank slate. Editing often involves AI continuously rewriting the content, rather than the author doing it manually.
AI will continue to trigger new competition for productivity suites, as the value shifts to agentic AI experiences. D&A leaders should 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 all digital AI applications combined.
Applications of agentic AI in the physical world are producing vast amounts of trajectory data in logical, spatial, and multi-agent scenarios as they interact with their environments. This represents a unique opportunity for world models to learn patterns from the data and make accurate predictions and simulations.
By 2030, 50% organizations will use autonomous AI agents to interpret governance policies and technical standards in machine-verifiable data contracts, automating compliance and enforcement of governance policies.
By 2030, 50% of failures in the implementation of Artificial Intelligence agents will be due to the insufficient application, at runtime, of AI governance platforms for capacity control and interoperability between multiple systems. In the short term, ungoverned decisions using Big Language Models (LLMs) will cause financial or reputational losses for companies.
“"D&A leaders should experiment with agents of data governance in pipelines ”Low-risk solutions are needed to orchestrate and automate trading processes,“ says Sallam. ”They will need to validate that agents can correctly interpret the context and protocols in a controlled environment before attempting to expand further. Analytical workflows should also be redesigned to include a mandatory assessment step.”
By 2030, a new wave of unicorns will emerge, with US$$ 2 million in annual recurring revenue (ARR) per employee, boasting valuations of over US$$ 1 billion driven not by investor capital, but by extreme capital efficiency that produces valuation multiples based on performance, not promises.
Pioneering AI-native startups are achieving unprecedented growth efficiency by solving specific, underserved problems with proprietary Artificial Intelligence, embedding AI into workflows, and delivering simple, intuitive user experiences that drive rapid adoption, recurring use, and measurable business impact.
“Established companies across all sectors are being subjected to a new standard. D&A leaders can learn from these 'AI-first' startups, which grow and achieve profitability rapidly by focusing on fewer employees with significant stakes, opting instead for engineers.‘ full stack "And technology-agnostic, yet generalists capable of adapting quickly to new Artificial Intelligence tools. This approach allows companies (and teams) to grow efficiently with fewer resources," says Sallam.
By 2030, 60% of the organizations that achieve successful differentiation with AI will be led by executives who prioritize mastering human relational skills.
You Chief Data and Analytics Officers (CDAOs) with strong coalition-building and influence skills are advancing into more powerful roles in the C-suite, including that of Chief Executive Officer (CEO), as organizations recognize the value of human-led strategic vision to leverage AI.
By 2030, universal semantic layers will be treated as critical infrastructure, along with data platforms and cybersecurity.
Developing a universal semantic layer is now a mandatory task for D&A leaders who lead or support Artificial Intelligence initiatives. It is the only way to improve accuracy, manage costs, substantially reduce AI debt, align multi-agent systems, and prevent costly inconsistencies before they spread. D&A leaders must include semantic resources in the budget as a non-negotiable foundation.
By 2028, 50% of content risk-related functions will migrate from legal and cybersecurity areas 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 intelligently generate and select content and take responsibility for mitigating associated risks by incorporating built-in design controls from the design stage. This allows for faster and more responsible innovation within ethical and legal boundaries, especially when the AI model's decision must be based on user context.
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About the Conference Gartner Data & Analytics
Gartner analysts are providing additional insights into AI trends at the Gartner Data & Analytics Conference 2026, which is taking place this week in [location]. Orlando (United States). Other dates and locations include: April 28 and 29 inSao Paulo(Brazil), May 11-13London(United Kingdom), May 19-21Tokyo(Japan), June 16 and 17 inSydney(Australia) and September 21 and 22 inMumbai(India). Follow the news and updates from the conference atXandLinkedInusing #GartnerDA.
About O Gartner for Data & Analytics Leaders
The Gartner for Data & Analytics Leaders provides objective and actionable insights for CDAOs and Data & Analytics leaders, helping them accelerate their D&A strategy and operating model to increase business value. Additional information is available at [link to Gartner for Data & Analytics Leaders].https://www.gartner.com/en/data-analytics. Follow the news and updates from Gartner for Data & Analytics Leaders atXandLinkedInusing #GartnerDA.
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Gartner, Inc. delivers objective, actionable insights that drive smarter decisions and better performance for enterprises’ mission-critical priorities. To learn more, visit www.gartner.com.
