AI Agents: The Future of Enterprise Work and Data Management

Snowflake's Baris Gultekin believes AI agents are critical for future of work

"We believe agents are the future for enterprises," says Baris Gultekin, Head of AI at Snowflake. For the uninitiated, Gultekin's talking about AI systems that do far more than spit out chat-like responses. For these "AI agents" can plan tasks, fetch the data they need - structured or unstructured - and then actually execute multi-step operations in a governed environment. If that sounds a bit futuristic, well, that's because it is. But Gultekin insists it's happening now, thanks to Cortex Agents, Snowflake's latest offering in what it calls an "AI data cloud" approach. I caught up with Baris over a quick video call about Snowflake's new push into the agentic age of AI. He spoke at length about bridging enterprise data with large language models, the complexities of structured vs. unstructured data, and how Snowflake is tackling head-on the problem of "hallucinations" and security lapses that plague many AI projects today. Also read: From AI agents to humanoid robots: Top AI trends for 2025 "At Snowflake, we see ourselves as an AI data cloud," Baris begins. "What that means is our customers bring their data - the crown jewels - into Snowflake to secure, govern, and analyze." If you've followed the buzz around AI in the past year, you'll know the big question is: how do organizations safely run large language models near the data they want to glean insights from? If they have to push that data across random pipelines or third-party services, it undermines security and governance -- something that Snowflake's trying to fix and simplify. "We focus on easy, efficient, and trusted," Baris continues, describing Snowflake's philosophy for enterprise AI. For him, this means a single platform that can handle both structured data (think rows and columns) and unstructured data (like PDFs, marketing material, or policy docs), all while giving users levers to govern who sees what. "In a governed system, trust is really important," he notes. The gist is that an AI agent, rather than being a glorified chatbot, can break down a complex query (maybe a five-step request about sales data and appended policy text) into smaller parts, route them to the right data source - Cortex Analyst for structured queries, Cortex Search for unstructured (both of which are Snowflake's offerings in the AI data cloud) - and then combine results to produce a final answer. Gultekin underscores how the next wave of AI is bigger than just a fancy chatbot that can write short paragraphs. "We're seeing two kinds of agents," he says. One type is a more advanced version of ChatGPT-like assistants. "They can reflect on how to solve a problem, use relevant tools, and verify results before giving you an answer." The second is "worker agents," which quietly automate back-office tasks, from analyzing thousands of emails for patterns to triaging complex workflows. "An example is a data provider that receives thousands of emails. They used to have staff reading these to flag urgent updates. Now they built an agent system and saw a 90 percent reduction in that workload," he notes. That's the "quiet brilliance" of an AI agent: it can step into tasks that are often menial but also require a bit of reasoning, saving entire teams thousands of hours. Part of Snowflake's pitch is that bridging structured and unstructured data is no small feat. "Actually, answering structured data queries is often harder than unstructured," Baris points out. He highlights that large language models do fine with open-ended text, but the moment you need them to talk to a database, join multiple tables, and produce an accurate numeric answer, things can fall apart. "The margin for error is low, especially in business-critical functions like finance or engineering," he says, acknowledging the fear of "hallucinations" that plague most LLM-based solutions. Snowflake claims it solves that with specialized retrieval systems. If you're a typical user, of course it's easy to write all this off as enterprise jargon - until you realize how these agents would trickle down into the consumer world. Consider an insurance claim scenario, where a user wants to query both structured data (policy coverage, claim amounts) and unstructured (doctor's notes, attachments). An agent can glean the relevant facts, ensuring you get a single, coherent answer rather than dealing with disjointed phone calls or mountains of PDF scans. Also read: AI agents explained: Why OpenAI, Google and Microsoft are building smarter AI agents Baris paints the scenario: "Think of a claims adjuster who typically has to pull data from various systems, then read through unstructured notes. If you have an agent, it can do that in minutes, not hours." Over time, he predicts, such productivity gains will show up in everyday experiences - like quicker reimbursements or a more transparent breakdown of your coverage. Asked about how India's shaping up in AI adoption, Gultekin is bullish. "India is at the forefront of adopting advanced AI," he says, citing his past roles at Google Assistant and seeing how voice-based and local-language tech took off. "I see India as a key market. The appetite for new technology is strong, and the user base is massive." He envisions Indian enterprises leveraging AI agents for everything from BFSI (banking, financial services, insurance) to e-commerce. The complexity of data in these sectors - some structured, some not - makes them prime candidates for a platform that unifies it all under one roof. Looking ahead, Gultekin sees a near future where agent-based automation quietly handles a wide range of tasks behind the scenes, drastically cutting overhead for entire teams. "We believe that many of our customers are already building production systems that rely on agent-based orchestration," he notes, pointing to large clients like Siemens Energy or S&P Global who've integrated Snowflake's advanced AI features to process millions of records. "At the same time," he cautions, "trust is incredibly important. We never want to be reckless. The data needs to be governed. The system needs to produce the right answer, or at least have a confidence measure about it." So while the illusions of "ChatGPT mania" might suggest overnight transformations, Snowflake's approach is more methodical, layering AI on top of robust data governance. "There is no AI strategy without a data strategy," Gultekin concludes. Indeed, that might be the real headline: AI can't fix messy data, and an agent is only as good as the pipeline feeding it. For all the hype around generative text or code, it's still about bridging the gap between what you have and what you want to do with it. Snowflake's newly announced Cortex Agents push that logic further, letting dev teams build a single orchestrator that can talk to either structured or unstructured data. "It's a more advanced version of chatbots - these agents can plan tasks, and reflect on results," Gultekin says with no small amount of pride. So is this the final iteration of enterprise AI? Hardly. "Agents are very new," Baris says. "We're only at the start of seeing how they'll streamline existing processes." But the ambition is unmistakable. An AI that's more than a toy, that truly "knows" how to unify everything from a bank's invoice data to a retailer's marketing library, then churn out verifiable, real-time answers. As to whether these advanced agents will remain behind corporate firewalls or eventually become standard in consumer-facing apps, that's an open question. But if Snowflake's bet is right, your next interaction with a big company's "AI help desk" might not feel like a token chatbot - it might be a full-blown agent, seamlessly pulling from sales spreadsheets, PDF manuals, and other data sources, all while making sure your personal info stays in the right data silo - always safe and secure. "We're excited about it," Baris says with a laugh. "It's a big leap from just chat. If we get it right, it could change how entire industries handle knowledge work." And for all of us tired of rummaging through endless documents, that sounds like a future that couldn't come sooner.

The end of data silos? How SAP is redefining enterprise AI with Joule and Databricks

SAP today announced a substantial upgrade to its enterprise-wide data management offerings and strategy. Called SAP Business Data Cloud, the new offering is a managed software-as-a-service (SaaS) that, the company says, "unifies and governs" all SAP data, "seamlessly" connects it with third-party data, and provides AI capabilities throughout. The company also announced a strategic partnership with Databricks, which creates unified data lakes and data warehouses (they call them Lakehouses). Databricks' data unification makes it possible for more comprehensive AI workloads and analytics. Also: Crawl, then walk, before you run with AI agents, experts recommend In a separate but concurrent announcement, SAP introduced ready-to-use Joule agents for service, sales, and finance. Joule is SAP's conversational AI assistant. Make no mistake: These are big announcements, especially for existing SAP customers. Let's break down what this all means in the context of the SAP ecosystem. To be clear, SAP has long offered a data unification and management solution in its SAP Datasphere offering. SAP Datasphere is marketed as a business data fabric that allows real-time connectivity between systems and applications. According to SAP's Datasphere product page, "SAP Datasphere capabilities are natively available in SAP Business Data Cloud." SAP Datasphere provides data management and integration across both SAP and external systems, while the new SAP Business Data Cloud is a much broader, fully-managed SaaS platform that not only manages SAP data but incorporates AI and deeper analytics integration using the data lake and data warehousing capabilities of Databricks. In terms of strategic direction, SAP Datasphere focuses on connectivity, while the new SAP Business Data Cloud platform provides advanced capabilities for governance, wide interoperability, and AI-driven insights and oversight. Key to the SAP Business Data Cloud is the Databricks partnership, which enables SAP clients to operate a much more seamless, AI-driven data environment. Pop quiz: When it comes to enterprise insight and governance, what is AI's kryptonite? Short answer: data silos. The thing that has made ChatGPT so amazing is its ability to sift rapidly through what seems like the entire web and synthesize answers based on a broad and ever-growing knowledge base. When you're managing a sprawling enterprise, you want to be able to get answers and insights that are as broad and comprehensive. But if much of your data is stored in individual silos -- operated by different business units, departments, and even third parties using unrelated solutions -- all that data becomes invisible to comprehensive AI analysis. Also: AI data centers are becoming 'mind-blowingly large' That's where the Databricks partnership comes in. Databricks' core competence is toppling silos to provide a more universal view of both SAP and non-SAP data. The proprietary Lakehouse architecture is built on top of Apache Spark's distributed data processing engine, Delta Lake's reliable and performant storage layer, and MLflow, the machine learning lifecycle management tool, among other technologies. By combining Databrick's flexible data sharing with SAP's strong governance capabilities, data governance -- especially compliance and security -- is simplified and reinforced. Databricks also offers far deeper business insights, both because AI is no longer stymied by data silos, and because Databricks has its own AI and large-scale analytics capabilities that are built for deeper insights and predictive decision-making across enormous oceans of data. Essentially, the Databricks partnership gives SAP's AI room to run. That's critically important to enable. SAP is introducing pre-built AI agents that actively perform tasks rather than simply providing recommendations. They are designed to automate and execute complex business processes. Before we go on, I'll share with you my concern about AI agents. We know AIs make mistakes and confabulate. When working with a chatbot, you have to make sure you double-check every so-called fact the AI provides. Now, what happens when you scale that up and let an AI loose across your enterprise? There's always the possibility the AI will make a serious mistake and then propagate that mistake at light speed all across your network. Also: AI agents might be the new workforce, but they still need a manager To be fair, humans are equally prone to mistakes. Here at ZDNET we have written regularly about how human error caused services and networks to go down until repaired. Just don't start deploying agents thinking they'll be perfect. Be sure to QA everything, human and AI. OK, now that we've passed my "trust, but verify" warning for AIs, let's discuss Joule agents. SAP is introducing pre-built agents for the finance, sales, and service industries. The finance agent can help handle repetitive financial tasks and help in decision-making. SAP specifically called out a cash collection agent, which can analyze disputes and "work across finance, customer service, and operations to validate details and recommend resolutions." In the future, finance agents might be able to perform automatic invoice processing, matching invoices to purchase orders, identifying anomalies, and pointing out discrepancies. They could also provide more predictive cash flow management, help with fraud detection, automate expense management, and reconcile accounts to help with the monthly, quarterly, and yearly closing processes. Also: AI agents will match 'good mid-level' engineers this year, says Mark Zuckerberg The Joule service agent works with customer service organizations to resolve customer service issues. Using contextual awareness from the SAP Knowledge Graph, the AI can analyze previous service interactions to suggest relevant solutions. Going forward, agentic AI could help with automated case resolution, proactively prevent equipment failures by suggesting maintenance procedures predictively, help customers provide self-service solutions, optimize schedules for service technicians and replacement parts, and detect rising customer dissatisfaction by identifying the root problems causing that dissatisfaction and suggesting mediations. SAP is introducing a cross-functional Q&A agent that can monitor sales opportunities and customer cases, proactively identifying questions and providing answers from appropriate knowledge sources. This agent can support both the sales and service teams. Another agent is a case creation agent that actively watches customer service cases; when a new way of resolving a problem is identified, it creates a knowledge article that customer service staff can later reference. Also: 7 apps that helped me escape the cloud - and protect my data privacy A case classification agent can understand the context of a case (SAP uses the example of identifying a tax-related query even if the word "tax" isn't used) and then route the cases to the most qualified teams or individuals. Assuming appropriate guardrails are in place, AI agents, particularly those vertically integrated with deep and validated solutions like SAP offers, have tremendous potential to reduce cost and time while increasing output quality. There seem to be four major strategic legs to SAP's modernized data strategy. They are: SAP describes Business Data Cloud as "built to prioritize openness and customer choice" as an open data ecosystem. At announcement time, the environment integrates natively with solutions from Collibra, Confluent, and DataRobot, as well as solutions provided by McKinsey, PwC, EY, Deloitte, and Capgemini. SAP Business Cloud and Joule AI agents are offerings that will help you make a more strategic shift toward an intelligent, interconnected enterprise powered by AI-driven data analytics, driving a holistic decision-making process enterprise-wide. SAP is making a big push to unify structured and unstructured data with advanced AI agents and analytic operations across systems and even providers. Also: How ChatGPT's data analysis tool yields actionable business insights with no programming SAP is not simply enhancing business efficiency, but redefining how their customers extract value from data and scale AI-driven operations in our increasingly complex digital landscape. What do you think about SAP's latest moves? Do you see SAP Business Data Cloud and the Databricks partnership as a game-changer for enterprise AI and data management? Are you excited or skeptical about Joule AI agents handling critical business tasks? How do you think AI-driven automation will impact finance, sales, and customer service in the years ahead? Let us know in the comments below.