Enabling agentic AI: Why data strategy is now business strategy.
In this episode of eSpeaks, host Corey Noles explores “Enabling Agentic AI: Why Data Strategy Is Now Business Strategy” with Abhi Visuvasam, Field CTO of Enterprise Architecture and Solutions at Reltio. The conversation unpacks what agentic AI means, why strong data foundations are essential, and how leaders can mitigate business risks while unlocking new opportunities. From governance and architecture pitfalls to near-term investment priorities and a pragmatic 90-day roadmap, the discussion offers executives a clear view of how data readiness directly shapes AI success.
Transcript:
Host (Corey Noles):
Hello and welcome to the eSpeaks podcast, where leaders unpack technology shaping business strategy. I’m Corey Noles, and today we’re tackling a big one: enabling agentic AI — and why data strategy is now business strategy.
Agentic AI, or systems that act autonomously to pursue goals, promises huge opportunities. But only organizations with strong data fundamentals will be able to take advantage. Our guest today is Abhi Visuvasam, Field CTO of Enterprise Architecture and Solutions at Reltio.
Abhi helps enterprises bridge architecture, data strategy, and product vision to safely unlock advanced AI. Today, we’ll demystify agentic AI, discuss the risks of weak data foundations, and explore the moves leaders should make today to win tomorrow.
Defining Agentic AI
Corey: Abhi, how are you?
Abhi: Very good, thank you. I’m really excited about this. Thank you for having me on. I think it’s going to be a great conversation. Everyone’s talking about this topic, and it’s helpful to bring another point of view.
Corey: Definitely. Let’s start by defining it. What do you mean by agentic AI, and how is it different from the AI most businesses are using today?
Abhi: That’s a big question. Everyone wants to define it in a way that feels comfortable to them. Is agentic AI truly autonomous? If there’s a human in the loop, is it still agentic?
The way I see it, agentic AI is about building systems that operate like humans — using intelligence to make decisions that align with the decisions a person would make. Where do we draw the line? Honestly, I think right now it makes sense to include the full spectrum. It’s a large, still-developing category.
At its core, it’s about making complex decisions in real or near real time. It’s about continuous learning — either through engagement with humans or autonomously — and refining insights along the way.
And, like a human, it can work across multiple functions. It doesn’t only solve problems in one domain, but also draws on other factors to make well-rounded decisions. That’s what I see as agentic AI and the challenge businesses are trying to solve.
Why It Matters for Leaders
Corey: So why should CDOs and CIOs care about this right now? What’s the upside if it’s done right?
Abhi: Many leaders are focused on productivity and efficiency. They want to reduce grunt work. For example, data stewards often spend hours just reviewing data quality in one system or across one category. Meanwhile, so much more is happening across the business.
Scaling people isn’t the answer. Hiring more isn’t sustainable. Instead, we’ve long turned to automation and workflows. Now with AI, we can amplify those efforts.
Instead of a person manually comparing healthcare provider records to ensure accuracy, AI can automatically generate that comparison. The steward simply reviews and confirms. That shifts the workload dramatically.
AI can process dozens or hundreds of records in the time it takes a human to check a handful. That’s huge efficiency. And even though humans remain in the loop, mistakes are possible. If we can train AI agents to a high level of quality, we can rely on them for accurate, valuable insights.
Risks of Weak Data Foundations
Corey: Many leaders also worry about the hype. What are the real risks when organizations try to deploy advanced AI on shaky data foundations?
Abhi: Trust is the biggest issue. MIT recently released a report showing that 95% of AI projects fail. At a small scale, humans can oversee results and say, “Yes, I trust this.” But scaling beyond that requires data trust.
Without a trusted data foundation, projects stall. That’s why platforms like Reltio are so important. They unify data from multiple sources, cleanse it, and create a trusted golden record that the whole organization can agree on.
When you build AI on top of trusted data, you inherit that trust. And from there, you can safely layer on unique, differentiating AI capabilities.
Data Readiness as Advantage
Corey: You’ve said data readiness can be a competitive advantage. What does data readiness actually look like in an enterprise preparing for agentic AI?
Abhi: Data readiness is something our customers are asking for. The good news is that Reltio’s platform inherently prepares organizations for AI.
Trusted, unified, connected data is critical. Reltio’s data graph naturally supports AI because AI needs context. It requires stitched-together relationships to uncover insights.
It’s not just about good data, though. It’s about connected data and real-time accessibility. For example, in applications like chatbots, humans don’t want to wait. They expect millisecond response times. Reltio ensures that kind of responsiveness.
So if you’re on Reltio, you’re already data-ready. From there, the sky’s the limit.
Governance and Trust
Corey: Governance often gets a bad reputation. How do you design governance that enables agentic behaviors without stifling innovation?
Abhi: Governance is often seen as a necessary evil — a “buzzkill.” But with AI, it’s essential.
If autonomous agents are running hundreds of thousands of iterations, you must be confident they’re producing correct insights. Especially if those outputs are going to customers, partners, or suppliers.
That’s why governance is back in vogue. With Reltio, when data from many sources is pulled together, quality becomes visible. That transparency elevates governance practices and builds trust.
Transparency and trust are the foundation for agentic AI. Without them, adoption at scale won’t happen.
Common Pitfalls
Corey: From an architecture perspective, what are some common anti-patterns that trip up teams building agentic systems?
Abhi: The same challenges have tripped organizations for years: fragmented data, silos, inconsistent definitions.
For example, we’ve seen customers debate what “customer” even means. With Reltio, you unify that definition by mastering all customer data in one place.
Connected data is also critical. Relationships — like linking a customer to a product they purchased — create the real business value. Data in isolation is meaningless. Connected data is where agentic AI delivers its power.
Real-World Examples
Corey: Can you share a real-world example where getting data strategy right unlocked agentic AI?
Abhi: Agentic AI is still new, but we’re helping customers move from foundation to adoption. One big step forward is AgentFlow — Reltio’s conversational AI interface.
With AgentFlow, users engage with the intelligent data graph in natural language. They can query, build agents, and even connect to other agentic platforms for agent-to-agent operations.
We’ve launched a bold initiative to build 100 agents in 100 days. We’re embedding engineers with customers to identify pain points and rapidly create agents. Over time, this will grow into a marketplace of agents that customers can adopt and deploy directly.
The goal is instant productivity. But adoption is the key. Many companies can prove experiments work. The real challenge is operationalizing them at scale — and that’s where we focus.
Measuring Success
Corey: How should executive teams measure progress and success? What KPIs matter most?
Abhi: Standard KPIs like speed to deploy and efficiency gains are important. For example, how long does it take a steward to enrich a profile manually versus using an agent?
But the most critical KPI is adoption. How quickly can you move from experiment to production, delivering tangible business outcomes like cost savings, efficiency, and productivity gains?
That’s what leaders should focus on.
A Pragmatic 90-Day Plan
Corey: For leaders ready to act, what’s a pragmatic 90-day plan to turn data strategy into business strategy for agentic AI?
Abhi: Ninety days is a long time in AI, but here’s the approach:
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Assess your foundation. Do you have the right platform? DIY doesn’t work here. If you don’t have scalable technology like Reltio, you’re already behind.
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Focus on the right use case. Choose one that clearly benefits from AI. Don’t just use AI for the sake of it.
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Start small, scale fast. Prove value quickly, then expand adoption.
Organizations that delay risk building solutions that can’t scale as the business evolves.
Closing
Corey: This was an excellent look at how data strategy has become the strategic foundation for the next wave of AI-driven business. Thank you for sharing frameworks, examples, and practical steps, Abhi.
Abhi: Thank you, Corey. For more, visit reltio.com or connect with us on LinkedIn. We’re forging new territory — building an agentic future for data.
Corey: Thanks again, and thank you to our listeners for joining us on eSpeaks. Subscribe and share your biggest data challenges in the comments. I’m Corey Noles, and we’ll see you next time.