In the year ahead, there will be signs of significant transformation at data sites. AI is the driver of change, of course, but it goes deeper, promising to reshape operations, security, customer interaction data, and a host of other functions. To explore the possibilities, we canvassed leaders across the industry on the changes they see ahead in the data world.
DEMOCRATIZATION OF AI
What’s hot: AI will continue to grow more accessible and become easier to use over the coming year. “With intuitive interfaces and natural language querying, asking questions and getting answers becomes accessible to everyone, not just data scientists and analysts,” said Manisha Khanna, global product marketing lead for AI and analytics at SAS. “This democratization of data insights ensures that all team members can engage with and benefit from the data.”
Current status: Leaders are increasingly recognizing that AI is but an augmentation tool—not a replacement for humans. “People are critical enablers who bring AI to life and are at the heart of AI adoption,” said Khanna.
Potential roadblocks: “Without active human involvement in data curation, model evaluation, feedback loops, and strategic oversight, AI systems cannot achieve reliable, beneficial, and trustworthy outcomes,” said Khanna. “To scale AI, organizations need to invest in roles, encourage collaboration, and use an infrastructure that facilitates sustained innovation.” This means more than training employees as well, he added. “Focus on redesigning work to uplift the unique human capabilities—creativity, critical thinking, and emotional intelligence—that AI cannot replicate. It should not be about solely automating tasks.”
AGENTS WITH PURPOSE
What’s hot: Autonomous data management powered by domain-specific AI agents is proliferating. These agents will be “deeply trained in the rules, context, and challenges unique to various different fields,” said Parth Patwari, principal with Deloitte Consulting. “They autonomously govern, cleanse, and optimize critical data processes, fundamentally transforming how organizations manage their information. For example, in healthcare, organizations are deploying multiple AI agents to streamline claims processing, interpret complex policy terms, and ensure consistent, accurate decisions.”
Current status: While still in early adoption phases, domain-specific agents are on the rise, said Patwari. “Elements of autonomous data management are visible today, especially in cloud-native environments and advanced analytics platforms.”
Potential roadblocks: Helping employees and managers to adapt to the changes domain-specific agents will bring may take time, said Patwari. “Success depends on developing skills, sustained attention, and thoughtfulness so individuals can embrace new technologies and foster an AI-first mindset.” Trust and transparency also need to be addressed and can be facilitated by “investing in explainable AI technologies and maintaining clear documentation,” Patwari added.
RISE OF THE DIGITAL WORKFORCE
What’s hot: The digital workforce will make its presence felt. “Fleets of AI agents trained on proprietary data, governed by corporate policy, and audited like employees will appear in org charts, collaborate on projects, and request access through policy engines,” said Sergio Gago, CTO for Cloudera. “They will be contributing insights alongside their human colleagues.”
A potential oversight framework may effectively be called an “HR department for AI.” AI agents are graduating from “copilots that suggest to accountable coworkers inside their digital environments,” agreed Arturo Buzzalino, chief innovation officer at Epicor. These agents are now capable of reading context, calling tools or APIs, and executing them under policy and security guardrails. “This closes the loop on exception recovery in supply/production, straight through procure-to-pay, contract-to-cash, self-healing master data, and continuous compliance.”
Current status: “The digital workforce is no longer an abstract concept; it’s rapidly becoming mainstream,” said Gago. A majority of organizations have started implementing AI agents. “There’s been a foundational shift around enterprise adoption of agentic AI systems.”
Potential roadblocks: Data privacy, system integration, and high implementation costs are concerns that may slow down adoption of AI agents, said Gago. “These hurdles reflect deep organizational anxieties around safeguarding sensitive data and adapting legacy systems.” There is also “a lack of in-house expertise and ethical or regulatory concerns compounding the challenge, demanding new frameworks for governance and accountability.”
Add to that the complexity of integrating AI agents into existing infrastructures—“meaning deployment is rarely plug-and-play,” said Gago.