Imagine every application in your business had its own AI assistant—smart enough to make real-time decisions and act dynamically. That’s the vision behind Agentforce, Salesforce’s agentic AI powerhouse. According to Honey Bhatnagar, Business Transformation Services and Data & AI Practice Lead at Simplus, it’s not just a vision—it’s happening now.

But here’s something most RevOps won’t discover until it’s too late: AI only works if your data does. One study found that 77 percent of business leaders admit that their companies struggle with data quality. Research estimates that this lousy data costs companies an average of $15 million yearly and chews up around 25 percent of potential revenue. 

When teams feel let down by their platform’s performance—sluggish automation, inaccurate forecasts, clunky user experiences—it’s easy to point fingers at the technology. But more often than not, the real culprit isn’t the platform at all. It’s the quality of the data flowing through it. 

Even the most powerful Salesforce tools can’t deliver game-changing results if fed incomplete, outdated, or inconsistent information. 

In a recent conversation, Honey shared how Simplus is helping enterprises unlock the full potential of AI through one essential ingredient: a rock-solid data strategy.  

 

AI-Powered Tools Demand Data Discipline

AI-enabled Salesforce tools promise more intelligent customer engagement, predictive insights, and faster decision-making. But without a robust data strategy, those promises fall flat. 

In this clip, Bhatnagar shares how Infosys helped a global tech enterprise unlock the power of unstructured data to enhance AI-generated customer service responses. What was the success of this transformation? It hinged on three pillars: data quality, governance, and integration.

“Imagine the possibilities,” Honey said. “But none of it is possible without the right data structures in place.” It’s a leap forward from traditional automation. Instead, this is AI technology that understands context, adapts, and executes.

 

Building AI-Readiness with a Strong Data Strategy

Before you plug AI into your business, ask yourself: Is your data AI-ready?

Honey says many companies hit roadblocks not because of weak AI but because of weak data infrastructure. For instance, Agentic AI, like Agentforce, demands real-time, contextual, clean data to make the right decisions.

Whether deploying AI to personalize customer journeys or automate service responses, your systems must be fed clean, consistent, and accessible data. Simplus Business Transformation Services helps customers achieve this with solutions to streamline the transformation of messy data into structured and strategic insights across platforms. 

 

Key Action Items for Organizations

To make the most of Salesforce’s AI-powered features like Agentforce, businesses must take proactive steps to assess and elevate their data readiness:

• Evaluate your current handling of unstructured data. Is it siloed, inconsistent, or inaccessible? If so, AI will struggle.
• Review your data ecosystem. That means assessing data quality, governance, architecture, and master data management maturity.
• Explore Infosys’ data accelerators. These tools help transform your raw data into a scalable, AI-ready asset for long-term value.

 

A Real-World Example: Turning Unstructured Data into Action

Let’s take a real-world case. The Infosys and Simplus teams recently joined forces to work with a large tech company to enhance service agent responses using AI. The challenge? Massive amounts of unstructured data—think FAQs, support documentation, and billing statements.

Here’s how we solved it:

• Sourced the unstructured data from AWS using a standard Data Cloud Connector and APIs.
• Applied Retrieval-Augmented Generation (RAG) to index and retrieve relevant data.
• Used a prompt template and RAG retriever to generate grounded, context-aware answers.

“The result was more accurate, AI-powered customer support—driven by intelligent data flows,” Honey says.

What Makes a Strong AI Data Strategy?

Whether you plan to scale in manufacturing, telecom, financial services, or healthcare, the foundations are the same. Honey and the BTS team follow a clear set of principles when preparing enterprise data for AI:

Data Quality: Ensure the 3 C’s—Completeness, Correctness, and Clarity

Master Data Management vs. Unified Customer Identity: Pick what fits your model

Architecture Maturity Curve: Know your current state and future goals

Data Governance: Build responsible, secure, and compliant systems

Storage & Archival: Keep data accessible and protected

The AI revolution is already reshaping how we sell, service, and market—and Simplus is at the heart of that transformation. But without high-quality, governed, and integrated data, organizations like yours risk falling behind. 

 

The BTS Edge: Accelerating AI Readiness

Simplus and Infosys use Salesforce AI-supported tools to help companies:

• Extract, validate, and transform data.

• Harmonize it into Salesforce Data Cloud.
• Enable Agentforce to perform at peak intelligence.

This isn’t just about plugging in AI. It’s about building a data backbone that scales with it. “With the right data strategy,” Honey says, “Agentforce becomes more than an assistant. It becomes a driver of growth.”

 

Is Your Data Strategy Holding You Back?

If you’re investing in AI, but your results are falling short, the problem might not be the AI itself. It might be the data feeding it. Take a step back. Look at your unstructured data, governance practices, and system architecture. Then ask: Are we AI-ready?

We can help. With a modern foundation—like the one Infosys and Simplus can help build—you’ll go from an AI pilot to a go-to-market powerhouse.