Your Agentforce agent just gave a customer the wrong answer.

• It recommended a product that was discontinued six months ago
• It surfaced an account with three duplicate contacts
• It escalated a case unnecessarily because critical history was missing

The agent is working exactly as designed. The problem is the data it was fed.

This scenario is playing out across organizations deploying agentic AI — and it's far more common than most leaders admit. 86% of analytics and IT leaders agree that AI outputs are only as good as their data inputs.¹ When an agent underperforms, it's rarely an AI capability issue. It's a data quality and data authority problem.

The demand for AI-ready data has reached a tipping point, with 92% of analytics and IT leaders saying the need for trustworthy data is higher than ever. ¹ Yet many organizations are struggling to keep up:

• 57% cite data reliability as the top barrier to moving AI pilots into production — a challenge that has remained unchanged for over a year¹
• 50% of data leaders say data quality and retrieval issues are the #1 reason agents fail to go live²
• 92% are concerned that new AI initiatives are moving forward without addressing the core data issues uncovered in earlier deployments²

These concerns highlight a growing reality: while data security and reliability have always been critical, the rapid advancement of AI has made them non-negotiable. Wendy Batchelder, Chief Data Officer at Salesforce, emphasizes that “the AI revolution is actually a data revolution,” noting that any AI strategy is only as robust as the data strategy — and the trust — supporting it.³

Agentic AI depends on more than access to data. It depends on clean, complete, current, and authoritative data.

When agents lack that foundation, they deliver inconsistent, outdated or incorrect recommendations. This isn't a failure of intelligence, it's a failure of data authority. The agent cannot confidently act on what it "knows" because the source itself is unverified.

Bad data enters Salesforce every day, from web forms, third-party feeds, manual entry, and legacy migrations. In most organizations, no one catches it before it reaches production.

At 100 agent interactions a day, humans can compensate.

At 100,000, the failure becomes systemic.

The fix isn't a better AI model. It's a better data foundation, and it requires three things.

Informatica Data Quality (DQ) is the cleaning, validation and monitoring engine for the Salesforce ecosystem. It provides the foundation that ensures Agentforce, Data Cloud and every Salesforce Cloud deliver accurate, trusted outcomes instead of generic or wrong outcomes.

1. Standardize Data Across Every Cloud (Sales, Service, Operations)
Agentforce recommendations are only as good as the data beneath them. Duplicates, missing fields and inconsistent formats degrade accuracy before a response is ever generated. Informatica DQ cleanses, standardizes and enriches CRM and ERP data so every agent operates from a trusted, complete record regardless of where the data originates.

2. Apply Data Quality at the Source (IT, Data, Operations)
Bad data doesn't announce itself. It enters quietly through web forms, third-party feeds, manual entry, and legacy migrations. Informatica DQ applies quality rules at the point of ingestion, ensuring dirty data never reaches your agents, dashboards or customers in the first place.

3. Monitor Continuously — Not Just at Launch (Data, Finance, Compliance)
Data quality isn't a one-time fix. Trust erodes when data degrades as systems grow and agent interactions scale. Informatica DQ continuously monitors and enforces quality rules so agents stay accurate long after go-live. Notably, 61% of leaders credit better data quality and completeness as the primary driver of AI success.¹

The gap between AI potential and AI performance comes down to one thing: data quality.

High-maturity organizations are 2x more likely to have the quality data required to use AI effectively. ² The difference isn't the AI model, it's the data foundation beneath it.

By addressing data quality with Informatica Data Quality, organizations can achieve:

• More accurate Agentforce responses, grounded in verified, complete and current data
• Faster paths from pilot to production, with fewer data blockers
• Less manual cleanup, replacing spreadsheets and scripts with automated rules
• Fewer customer-facing failures, catching bad data at ingestion — not after trust is lost

Your agents are ready. Ensure your data is too.

Ready to get your data AI-ready? Connect with an expert today.

_{¹https://www.informatica.com/lp/scale-ai-with-data-you-can-trust_5282.html
²https://www.informatica.com/lp/cdo-insights-2026_5264.html
³https://www.salesforce.com/news/stories/data-analytics-trends/}