{"id":4819,"date":"2025-09-12T12:58:44","date_gmt":"2025-09-12T12:58:44","guid":{"rendered":"https:\/\/cybersecurityinfocus.com\/?p=4819"},"modified":"2025-09-12T12:58:44","modified_gmt":"2025-09-12T12:58:44","slug":"how-will-agentic-ai-change-enterprise-data-management-in-2026-and-beyond","status":"publish","type":"post","link":"https:\/\/cybersecurityinfocus.com\/?p=4819","title":{"rendered":"How Will Agentic AI Change Enterprise Data Management in 2026 and Beyond?"},"content":{"rendered":"

SUB: Reltio CEO Manish Sood explains why \u201cagentic AI\u201d will first transform data matching and merging, as well as how enterprises can prepare for a new era of real-time, trusted data.<\/em><\/p>\n

By Corey Noles<\/em><\/p>\n

By 2026, the way enterprises manage their data may look radically different. Instead of humans painstakingly cleaning, merging, and validating records, autonomous AI agents will handle the heavy lifting\u2014deciding which sources to trust, resolving conflicts in real time, and surfacing insights before teams even know to ask.<\/p>\n

To understand how enterprises should prepare for this shift, TechnologyAdvice spoke with Manish Sood, CEO and Founder of Reltio<\/a>, a company at the forefront of data unification and governance. In this conversation, Sood shares where agentic AI will first make its mark, how it will reshape the day-to-day work of data teams, and what business leaders should be measuring as they move toward an AI-driven future.<\/p>\n

eWeek: <\/strong>When you say \u201cagentic AI\u201d for enterprise data, what\u2019s the first everyday workflow it will actually change in 2026\u2014matching\/merging, survivorship, enrichment, or stewardship?<\/p>\n

Manish Sood:<\/strong> Matching and merging will be the first everyday workflow transformed by agentic AI in 2026. It\u2019s where the complexity of today\u2019s data environments intersects most urgently with the speed and trust requirements of AI-driven operations.<\/p>\n

Why matching and merging? Because real-world data is messy\u2014full of duplicates, inconsistencies, and ambiguity. And in agentic AI workflows, there\u2019s no time for human intervention every time a record doesn\u2019t match perfectly. If an AI agent is going to recommend a product, process a claim, or engage a customer in real-time, it must operate on a clean, consolidated profile.<\/p>\n

With pretrained, LLM-powered matching models, we\u2019re already seeing the shift from rules-based \u201cis this the same entity?\u201d checks to agentic workflows that resolve matches in real time, complete with confidence scoring, auditability, and compliance guardrails. As this matures, it becomes the intelligent \u201cfront door\u201d to every downstream workflow\u2014survivorship, enrichment, stewardship, and beyond.<\/p>\n

Agentic AI doesn\u2019t just need data. It needs trusted, context-rich, consolidated data in milliseconds. That starts with entity resolution. That\u2019s why matching and merging comes first.<\/p>\n

eWeek: <\/strong>Paint a quick \u201cday in the life\u201d of a data team in late 2026. What\u2019s different for a data steward, an analytics lead, and a business owner when agents are in the loop?<\/p>\n

Sood:<\/strong><\/p>\n

Data Steward:<\/strong> Starts the day reviewing match suggestions already resolved by an agent overnight\u2014complete with audit trails. Instead of chasing duplicates, they fine-tune policies and coach the agents.<\/p>\n

Analytics Lead:<\/strong> Gets alerts from agents about quality anomalies before models drift. Focus shifts from cleansing data to collaborating with business teams on trusted, reusable data products.<\/p>\n

Business Owner:<\/strong> Opens a dashboard where every metric is explained by an agent: what changed, why it matters, and what to do next. No backlog. No bottlenecks. Just answers\u2014powered by governed, agent-ready data.<\/p>\n

What\u2019s different? Agents don\u2019t just assist\u2014they act<\/em>. Teams move from fixing data to unlocking value.<\/p>\n

eWeek: <\/strong>How do you let agents create the single source of truth without losing trust?\u00a0<\/p>\n

Sood: <\/strong>You don\u2019t lose trust by letting agents help create the source of truth\u2014you lose it if they do it invisibly. The key is governed autonomy.<\/p>\n

Agents can act autonomously within boundaries: resolving matches, flagging anomalies, and enriching from verified sources.\u00a0<\/p>\n

The result? Transparency builds trust. Stakeholders see not just the data, but why it was trusted\u2014who approved it, what logic was applied, and when.<\/p>\n

That\u2019s how agents earn their role\u2014not just in surfacing the truth, but sustaining it.<\/p>\n

eWeek: <\/strong>What early wins should a CDO expect in the first 90 days of embracing agentic AI in enterprise data management?\u00a0<\/p>\n

Sood: <\/strong>In the first 90 days, a Chief Data Officer introducing agentic AI should be able to point to some very tangible wins. For example, one of the first things you\u2019ll notice is how quickly AI agents can clear out the backlog in match resolution. What used to be repetitive work for data stewards now gets handled in a fraction of the time, freeing up those teams to focus on higher-impact priorities.<\/p>\n

You\u2019ll also gain immediate visibility into data quality. Prebuilt agents can identify issues by domain, by source, even by business impact\u2014so you\u2019re not just improving data, you\u2019re prioritizing the fixes that matter most. That clarity helps leaders establish a fast baseline and build early momentum.<\/p>\n

And because analysts can finally stop spending their time cleaning data, you start seeing faster time-to-insight. Trusted data products come together more quickly, confidence in dashboards goes up, and KPIs start to feel reliable again.<\/p>\n

Most importantly, business teams see the results. Cleaner data. Faster answers. And proof that this isn\u2019t just another tech initiative\u2014it\u2019s delivering real business value from day one.<\/p>\n

eWeek: <\/strong>What KPIs prove it\u2019s working by the end of 2026?<\/p>\n

Sood: <\/strong>By the end of 2026, the impact of agentic AI on enterprise data management should be visible in a few clear KPIs\u2014and they tell a compelling story.<\/p>\n

First, you\u2019ll notice a significant reduction in manual effort. The percentage of match or merge decisions needing human review will fall. Data stewards will spend significantly fewer hours on repetitive tasks, and teams will spend less time tracking down and resolving data quality issues.<\/p>\n

Second, data activation improves across the board. Dashboards and data products get to value faster. More of the data feeding AI models and workflows will be trusted and up-to-date. And business users will notice\u2014they\u2019ll have an easier time finding the data they need, when they need it.<\/p>\n

Third, trust becomes something you can measure. You\u2019ll see better data quality scores around accuracy, completeness, and freshness. Lineage becomes more visible. Policy adherence improves. And the amount of rework caused by bad or outdated data drops significantly.<\/p>\n

Finally, you\u2019ll see business value accelerating. More teams\u2014from sales to operations\u2014will start using agents to inform decisions. More actions will be taken autonomously, without incident. And more new use cases will take off thanks to trusted, real-time data.<\/p>\n

Bottom line? Less friction, more trust, faster execution\u2014and all of it traceable.<\/p>\n

eWeek: <\/strong>How does agentic AI change data governance and compliance conversations with legal and security?\u00a0<\/p>\n

Sood: <\/strong>Agentic AI changes the conversation with legal and security teams in a pretty fundamental way. Where there used to be fear\u2014about losing control, or opening the door to risk\u2014now there\u2019s proof that control is actually stronger.<\/p>\n

Traditionally, governance has meant policies written down in documents, enforced manually after the fact. It\u2019s reactive, it\u2019s audit-heavy, and it\u2019s slow. With agentic AI, that flips. Now, AI agents enforce governance at runtime<\/em>. They inherit masking and access controls. They only operate within pre-approved boundaries. And every action they take is logged\u2014with full lineage and rationale.<\/p>\n

So when legal or security asks, \u201cCan we trust what the AI is doing?\u201d\u2014the answer isn\u2019t just \u201cyes.\u201d It\u2019s \u201chere\u2019s exactly what it did, why it did it, and how we know it was within policy.\u201d<\/p>\n

For those teams, this isn\u2019t a leap of faith. It\u2019s a measurable upgrade in control: real-time enforcement, full auditability, and built-in oversight.<\/p>\n

Agentic governance isn\u2019t a compromise. It\u2019s provably safer.<\/p>\n

eWeek: <\/strong>Rules, LLMs, and retrieval: how do they fit together? When should an agent rely on deterministic data management rules vs. use an LLM with retrieval (RAG) to make a decision?<\/p>\n

Sood: <\/strong>This isn\u2019t an either-or question\u2014it\u2019s about making sure every AI decision is grounded in the same trusted foundation. Agentic AI needs a real-time, context-rich enterprise data graph to act reliably.<\/p>\n

Most enterprises have customer, product, supplier, and location data scattered across dozens of systems, including CRM, ERP, data lakes, and third-party feeds. That data is often siloed, inconsistent, and not AI-ready.<\/p>\n

Reltio solves this by continuously ingesting data from all these sources\u2014structured and unstructured\u2014and then unifying it in real-time. It utilizes advanced matching, survivorship logic, and AI-powered enrichment to create accurate and trusted profiles across various domains.<\/p>\n

But here\u2019s what makes Reltio unique: it doesn\u2019t just create a clean record. It captures the relationships and interactions between entities\u2014so you don\u2019t just know who a customer is, but who they\u2019re connected to, what they bought, when they called support, and how they relate to a product, supplier, or location. That\u2019s what we mean by a data graph.<\/p>\n

This graph becomes a system of context for AI. It provides every agent\u2014whether resolving a match, personalizing an offer, or approving a claim\u2014with a real-time, trusted foundation to make informed decisions.<\/p>\n

So instead of every agent operating in isolation, they\u2019re all drawing from the same continuously updated, governed, and explainable data environment. That\u2019s what makes agentic AI scalable, compliant, and aligned with your business logic.<\/p>\n

So the question isn\u2019t which technique to use\u2014it\u2019s where those techniques are anchored. Without a system of context, even the best AI becomes fragmented. With Reltio, it becomes trustworthy at scale.<\/p>\n

eWeek: <\/strong>What\u2019s the right build\u2013buy\u2013partner mix? Where should enterprises lean on platforms (like Reltio + hyperscalers), and where does custom make sense?<\/p>\n

Sood: <\/strong>When it comes to AI and data, the most innovative enterprises don\u2019t try to build everything\u2014they focus on building what differentiates them.<\/p>\n

So what\u2019s the right mix?<\/p>\n

Buy the foundation.<\/strong> Data unification, governance, and real-time infrastructure are not DIY territory. You don\u2019t want to spend years trying to cobble together your own data backbone. That\u2019s where platforms like Reltio Data Cloud\u2122, combined with your preferred hyperscaler, come in. They give you enterprise-grade trust, scale, and security\u2014out of the box.<\/p>\n

Partner where speed matters.<\/strong> Prebuilt agents, vertical templates, and implementation services can accelerate your time to value\u2014especially for high-volume workflows like governance, match resolution, or data enrichment. There\u2019s no need to reinvent what others have already optimized.<\/p>\n

Build at the edge.<\/strong> Custom agents make sense when you\u2019re solving for something uniquely yours\u2014your scoring logic, your customer journey, your IP. That\u2019s where your AI should feel like you<\/em>, not a generic copilot.<\/p>\n

Rule of thumb:<\/strong><\/p>\n

Platform for trust and interoperability<\/p>\n

Partner for speed<\/p>\n

Custom for edge use cases<\/p>\n

eWeek: <\/strong>Looking past 2026, what\u2019s the responsible pace? One bold bet you\u2019d make for 2027\u20132028\u2014and one pitfall leaders should avoid this year.<\/p>\n

Sood: <\/strong>By 2027\u20132028, enterprises won\u2019t just be deploying a handful of AI agents\u2014they\u2019ll be orchestrating hundreds,<\/em> each performing high-stakes, real-time work across sales, service, compliance, and operations. The bold bet? Most of these agents won\u2019t be built from scratch. They\u2019ll be hired<\/em>\u2014just like employees.<\/p>\n

That future only works if there\u2019s a trusted system of context they can plug into instantly. That\u2019s where Reltio comes in.<\/p>\n

We believe every enterprise will need an intelligent data graph, not just as a source of truth, but as a source of action. One that continuously unifies, governs, and serves trusted data across domains, and proactively guides agents with policy, history, and context in milliseconds.<\/p>\n

The bold bet: by 2028, the enterprise data graph will be as essential to agentic AI as the network was to the internet. And Reltio will be the foundation that makes agentic AI enterprise-grade\u2014safe, scalable, and aligned with how our customers\u2019 businesses actually run.<\/p>\n

In other words, AI doesn\u2019t replace data strategy. It makes it existential. And Reltio is how you make it real.<\/p>\n

But in 2025\u20132026? Go slower to go faster. Start with embedded governance, not just flashy copilots. Trust is the compounding asset.<\/p>\n

The biggest pitfall to avoid now is not letting LLM hype bypass your data foundation. AI and agents are tools that need trusted information to work properly. If the data isn\u2019t unified, governed, and explainable\u2014agents will move fast\u2026 in the wrong direction.<\/p>\n

The post How Will Agentic AI Change Enterprise Data Management in 2026 and Beyond?<\/a> appeared first on eWEEK<\/a>.<\/p>","protected":false},"excerpt":{"rendered":"

SUB: Reltio CEO Manish Sood explains why \u201cagentic AI\u201d will first transform data matching and merging, as well as how enterprises can prepare for a new era of real-time, trusted data. By Corey Noles By 2026, the way enterprises manage their data may look radically different. Instead of humans painstakingly cleaning, merging, and validating records, […]<\/p>\n","protected":false},"author":0,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2],"tags":[],"class_list":["post-4819","post","type-post","status-publish","format-standard","hentry","category-news"],"_links":{"self":[{"href":"https:\/\/cybersecurityinfocus.com\/index.php?rest_route=\/wp\/v2\/posts\/4819"}],"collection":[{"href":"https:\/\/cybersecurityinfocus.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/cybersecurityinfocus.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"replies":[{"embeddable":true,"href":"https:\/\/cybersecurityinfocus.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=4819"}],"version-history":[{"count":0,"href":"https:\/\/cybersecurityinfocus.com\/index.php?rest_route=\/wp\/v2\/posts\/4819\/revisions"}],"wp:attachment":[{"href":"https:\/\/cybersecurityinfocus.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=4819"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/cybersecurityinfocus.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=4819"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/cybersecurityinfocus.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=4819"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}