In recent months, the ground has been moving underneath the feet of the overseers of enterprise data and IT environments. AI is bringing new challenges, along with seemingly insatiable business demands for real-time insights.
To explore the most compelling technologies promising to shape the data world in the months and years to come, we canvassed market leaders to get a sense of the top technological changes and potential issues that may arise.
AGENTIC AI
AI has become a panacea shaping data operations, and now agents—targeted, specific functions invoking AI— are making that a reality. For example, “AI assistance has reduced the amount of time and effort involved in moving data between legacy and modern platforms,” said Ashwin Patil, principal and data engineering and analytics practice leader at Deloitte Consulting.
“Agentic AI significantly augments and automates a once very-manual process of profiling data, performing quality checks, building business rules, and integrating data across applications,” said Corey Keyser, head of AI at Ataccama. He noted that with AI agents, “We’re finally seeing the promise of real and generalized automation. It’s completely changing the way we think about our strategy and even user interfaces. These aren’t just chat interfaces; they’re systems that can interpret intent, construct and execute on complex plans, and autonomously adapt to new data as they work.”
AI agents also bring changes to the roles of data professionals—helping them “reimagine their roles as strategic enablers rather than tactical operators,” said Srujan Akula, CEO and cofounder of The Modern Data Company. “They are evolving from information gatekeepers to business accelerators.”
Finally, with the emergence of AI agents, “Data becomes what every CEO imagined it would be: a genuine strategic asset,” Akula added. “When powered by data products— reusable data assets—AI agents become exponentially more powerful. And they don’t just make data teams more efficient—they make every business user a data practitioner.”
Potential Issues
Trust is key to successful agentic AI implementations. “Without it, all of this falls apart,” said Keyser. “Agents need more than just access; they need context into what the data means, how reliable it is, whether it complies with policy. That requires a trust–validation layer, situated between agents and the data they interact with.”
Orchestration of AI agents remains a messy problem as well, said Keyser. “Most enterprises aren’t set up to manage swarms of agents making decisions.”
This takes agentic AI implementations beyond the technical realm and into cultural or organizational adoption. “Humans struggle with feeling like they are giving power away, especially to a system,” said Patil. “We need to understand that in order to drive greater efficiency and innovation, we can partner with AI, keeping a human in the loop.”
Tangible Business Benefits
“Done right, agentic AI unlocks a new level of speed and intelligence,” said Keyser. “Agents will deliver dashboards along with clear explanations, recommended actions, and the trust signals to support them. The days of ‘ask the data team and wait a month’ are certainly numbered.”
Productivity is a key benefit seen with agentic AI. “Digital agents will begin to act with minimal human intervention, prompting us on how to ask it for help, leading to progressively increasing its efficiency,” said Patil. “It can shorten the length of time for what you are building by assisting or augmenting or automating various manual activities humans perform.”
Agentic AI “can also help shrink the gap between an experienced worker and a new worker, filling in the gaps and bringing collective knowledge,” Patil added.
“The business value is going to manifest in ways traditional data investments never delivered,” said Akula. “AI agents powered by data products can unlock the compound value of organizational knowledge. Instead of insights sitting in isolated reports, they become living assets that can inform strategy, optimize operations, and identify new revenue opportunities. Data finally fulfills its promise as the ultimate force multiplier for business intelligence.”
AI-DRIVEN DATA GOVERNANCE
Data governance has long been sought to oversee and untangle data-intensive functions, and this is where AI also can step in. “This is AI embedded within the governance framework itself,” said Nic Adams, cofounder and CEO at 0rcus. The introduction of AI into the data governance process “moves it beyond reactive, rule-based processes to proactive, intelligent automation of data policy enforcement, quality monitoring, and access control across distributed data environments.” In addition, AI can play a role in “continuous assessment of data lineage, bias detection in datasets, and dynamic classification of sensitive information.”
Potential Issues
Of course, fragmented legacy data systems and governance tools—which often lack standardized metadata or API interfaces—may be difficult to bring under a comprehensive governance umbrella, Adams cautioned. He added that such a transformation requires “substantial change management and reskilling of data professionals.” AI development skills are essential. Accurately training AI models for nuanced policy enforcement and bias detection is a complex undertaking. As with all applications of AI, explainability and auditability of AI-driven governance decisions will be needed. Adams urged developing “policy-as-code” frameworks to address this.
Tangible Business Benefits
Businesses will realize several tangible benefits from AI-driven data governance, said Adams. AI-driven data governance will help boost “a measurable increase in data quality and reliability,” he explained. In addition, AI will help ensure regulatory compliance. Ultimately, AI-driven data governance will “foster greater trust in data assets across the enterprise, enabling faster, more confident data-driven decision making.”