Through 2027, GenAI and AI agent use will create the first true challenge to mainstream productivity tools in 30 years Deepak Seth, Sr Director Analyst at Gartner, showcased Gartner’s top D&A predictions for 2026.
FinTech BizNews Service
Mumbai, 23 September, 2026: In 2026, the lines between human intelligence, machine intelligence, and organizational intelligence continue to blur, stated Deepak Seth, Sr Director Analyst at Gartner, who showcased on Tuesday Gartner’s top data and analytics (D&A) predictions for 2026 at the “Gartner Data & Analytics Summit”.
Key Takeaways of the session on Day 2 of the Summit:
“By 2027, 75% of hiring processes will include certifications and testing for workplace AI proficiency during recruiting.”
“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.”
“By 2029, AI agents are projected to generate 10 times more data from physical environments than from all digital AI applications combined.”
“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, a new wave of unicorns will emerge, with $2 million annual recurring revenue per employee boasting billion-dollar-plus valuations driven not by investor capital, but by extreme capital efficiency that produces valuation multiples based on performance, not promise.”
How is Agentic AI Impacting and Disrupting Your Data Management Discipline and Technology NOW?
Presented by Ramke Ramakrishnan, VP Analyst, Gartner
Agentic AI is transforming traditional data management architectures and enabling new use cases in data management and engineering. In this session, Ramke Ramakrishnan, VP Analyst at Gartner, discussed the impact of agentic AI on existing data management architectures and technologies, the new use cases it enables, the evolving skills landscape, and how organizations can prepare for these changes.
Key Takeaways
“The rise of agentic AI is rapidly accelerating the adoption of goal-driven agents for data management processes, thereby freeing practitioners to focus on strategic initiatives.”
“Gartner predicts by 2029, agentic data management using adaptive, context-aware AI agents will have automated 75% of data engineering workflows freeing capacity for higher-value reinvestment.”
Types of AI agents applied to data management include:
o Task-based agents are specialized AI agents designed to perform specific, well- defined data management functions.
o Orchestration agents manage and coordinate tasks across multiple task-based agents.
Multi-agent systems (MAS) involve the deployment of multiple AI agents, both task-based and orchestration agents, working collaboratively to manage data at scale.
“The success of agentic AI depends less on the model and more on data context. To realize its full potential, organizations should assess their context readiness, invest in semantics (meaning) over syntax (storage), and establish strong governance frameworks.
