Gartner's latest forecast positions agentic AI as a fundamental reshaping force for data and analytics infrastructure, with organisations expected to deploy AI agents across governance, data management and real-time intelligence by 2028. The analyst firm identifies four critical trends: decision governance frameworks to mitigate agentic AI risk, expanded adoption of dedicated AI governance platforms, real-time data streaming to feed autonomous agents, and AI-driven data management to accelerate operational responsiveness. The shift is quantifiable—Gartner predicts a 60% increase in organisations using data streaming specifically for agentic AI within the next two years, whilst 94% of tech leaders already anticipate data streaming amplifying their AI investments. This represents a wholesale move from batch-processed, retrospective analytics to continuous, real-time decision-making architectures.
For CX teams, this trajectory carries immediate operational implications. Support leaders currently managing Zendesk, Freshdesk or Salesforce Service Cloud deployments should recognise that the infrastructure underpinning these platforms is shifting toward agentic autonomy—meaning your data governance posture today directly determines your ability to scale AI-driven customer interactions tomorrow. The emphasis on decision governance and AI governance platforms signals that vendor lock-in around governance frameworks will intensify; teams without explicit governance policies embedded in their tech stack risk either over-constraining agent performance or exposing themselves to regulatory and reputational risk. However, Gartner's own analysts acknowledge a critical constraint: human oversight remains non-negotiable. As Martha Buyer notes in the report, AI agents lack common sense and operate within the boundaries of their training data, meaning CX teams cannot simply automate governance away. The real challenge becomes operationalising governance at scale—establishing monitoring frameworks that allow agents to act autonomously whilst maintaining human visibility over decisions that affect customer outcomes.
The practical question for support leaders is whether your current data infrastructure can support this transition. Organisations relying on legacy batch-processing workflows or siloed customer data will struggle to feed real-time agents with the contextual intelligence they require, whilst those already operating streaming architectures gain a compounding advantage. This creates a bifurcation risk: vendors and teams that embed governance-first agentic capabilities early will capture disproportionate market share, whilst those treating AI governance as a compliance checkbox rather than an operational necessity will find themselves managing increasingly unreliable autonomous systems. The window to establish governance foundations is narrowing.
Recent trends in data and analytics anticipate how organization will use AI agents to drive governance and data management by 2028.