Acceleration is the new normal. Across industries, businesses are no longer just exploring AI — they're operationalizing it. The rapid rise of generative and agentic AI is transforming how organizations think about their data strategies, with a clear shift toward execution, experimentation, and intelligent governance.
In this era of instant impact, AI innovation is no longer reserved for highly technical teams, as was displayed at the recent Gartner Data & Analytics event in March 2025.
Salesforce, for example, is making it possible to build and deploy AI Agents in under 15 minutes — no heavy coding required. Microsoft’s latest leap, Microsoft Fabric built on OneLake, acts as a “OneDrive for all your data.” With Copilot integrated throughout the platform, users can generate real-time insights with tools like Power BI in just seconds. These demonstrations aren’t just technical feats — they’re signals that AI is becoming radically accessible.
Becoming ‘AI-ready’
This wave of innovation is reframing what it means to be “AI ready.” Organizations are turning their focus to data quality, governance, and integration as core enablers of responsible AI. Every solution provider in the space is now positioning to answer the same question: how can we help businesses get their data AI-ready, fast?
One key shift is happening within the realm of data governance. While not every company has a mature governance model, AI is becoming the catalyst that forces the conversation. Many teams are rethinking their operating structures, with a noticeable trend toward centralizing governance functions. “We’ve historically had a federated model,” one Data Governance Manager shared, “but the importance of AI is pushing us to centralize.”
Of course, strategy doesn’t change overnight. Shifting course is more like turning a cruise ship. Still, experimentation is thriving — and rightly so. To support it, organizations are bringing diverse stakeholders to the table: legal, security, IT, marketing, and privacy teams are all aligning to manage AI risks and unlock new possibilities. As one Data Management lead put it, “We just started working with our security and legal teams to govern how we use data for AI.”
Implementing governance
The rise of the AI Council is another trend gaining momentum. These cross-functional groups, often backed by executive leadership, are taking on the responsibility of steering responsible AI innovation. Ren Nunes, Data & AI Governance Manager at Blackbaud, described how their journey evolved from privacy compliance to a unified AI governance framework: “What started with CCPA in 2018 quickly became a broader digital trust strategy. That foundation helped us create a structured way to assess AI risks and ensure the data powering these projects is trusted.”
Looking ahead, real-time risk management and deliberate, documented learning will be essential to sustainable data strategies. AI governance is becoming a board-level topic—and it’s reshaping the data management landscape. As AI capabilities continue to scale, expect to see AI data preparation converge under a broader, centralized data management function, supported by tools built specifically to enable responsible AI deployment.
The path to AI readiness is no longer theoretical — it’s unfolding now. And the pace is only picking up.
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