True Effectiveness with Salesforce Agentforce Requires Elevating Data Quality First

The first step in Agentforce implementation is improving data and records across the platform—before AI begins its work.

By nearly any measure, the launch of Agentforce by Salesforce in September 2024 has been successful. CEO Mark Benioff credits this to its unified architecture that combines apps, data, and agents into what he calls a "trinity." He also believes three important elements must work together for Agentforce to achieve its potential in the modern enterprise.

• Humans remain essential, because they provide oversight and handle complex cases that require judgment.
• All Salesforce apps are built on a common core and framework, ensuring consistent data handling.
• Salesforce Data Cloud aggregates data from myriad available sources, making it available across the company for agents to access complete, contextual information.

Certainly, high-quality data is the bedrock upon which the effectiveness of Salesforce's Agentforce platform is built. Agentforce's AI-driven agents—designed to autonomously handle tasks such as customer engagement, sales coaching, and service support—are heavily reliant on data.

To ensure these agents deliver accurate, contextually relevant, and actionable outcomes, the underlying data must be complete, valid, non-duplicative, and current. Without these qualities, the platform's ability to effectively interpret customer intent, provide meaningful insights, and automate processes is severely compromised. Poor data quality undermines the trust and intelligence that Agentforce aims to bring to business operations.

Beyond Data Quality: Other Factors in Successful Agentforce Implementation

While data quality is paramount, it's not the sole factor in successful Agentforce implementation. User training, system integration, workflow design, and organizational alignment also play significant roles. However, data quality often takes precedence because it's the foundation upon which these other elements depend.

Incomplete records (e.g., missing customer contact details), duplicates (e.g., multiple entries for the same lead), or stale data (e.g., outdated purchase histories) can derail even the best-designed processes or most well-trained teams. The emphasis on data quality is heightened with Agentforce because its AI capabilities amplify both the potential benefits of good data and the pitfalls of bad data.

Impact on Adoption, Frustration, and Business Outcomes

Data quality issues have been a recurring factor in slow adoption, user frustration, and negative business outcomes across all Salesforce implementations—not just Agentforce specifically.

• When data is unreliable, users lose confidence in the system, leading to reduced engagement and adoption. For instance, sales reps might hesitate to rely on Agentforce’s Sales Development Rep agent for lead engagement if the data feeding it is riddled with duplicates or outdated information. This mistrust creates a vicious cycle: low adoption leads to less data input, which further degrades quality.
• Internally, frustration mounts when users spend time correcting errors or working around bad data instead of leveraging the platform’s intended efficiencies. This can lead to decreased productivity, increased employee dissatisfaction, and potential resistance to change.
• Externally, poor data quality can harm customer experiences and business results. For example, a service agent powered by Agentforce might provide irrelevant solutions if it’s drawing from incomplete case histories, leading to customer dissatisfaction and potential churn. Or a sales agent might target the wrong prospects due to inaccurate segmentation, missing opportunities and damaging revenue potential.

Real-world examples abound in broader Salesforce contexts: Companies have reported lost deals due to inaccurate pipeline reports or reputational hits from contacting customers with outdated information. While specific Agentforce examples are currently less documented given its relatively recent launch, the same principles surely apply, amplified by the platform’s reliance on AI and automation.

Back to the Basics for Data Quality

The free Salesforce app, Data Quality Helper, from Passage Technology is a valuable tool in addressing these challenges. Designed to tackle data quality issues, it helps Salesforce Admins and users maintain clean, reliable data—exactly what Agentforce needs to thrive.

The app automates tasks like identifying and merging duplicates, flagging incomplete records, and cleaning up outdated or unneeded entries with sound data storage deletion strategies. For instance, it could ensure that customer records feeding into Agentforce are free of redundancies and fully populated with key fields like email or purchase history, enhancing the AI agents’ decision-making accuracy and effectiveness.

(In addition, Passage Technology’s free apps Lookup Helper and Rollup Helper support ongoing data hygiene and AI functionality. The former supports  displays of related records and aggregated information  across the dataset; the latter creates customizable automated rollups across any objects, including those with a lookup relationship or hierarchy rollups. This tighter data aggregation, in turn, ultimately “teaches” the AI model to better understand the available data.)

Proactive maintenance reduces the risk of data degradation over time, a common pain point in growing organizations. For Agentforce users, this will result in less manual intervention and more trust in the system’s outputs—potentially boosting adoption rates and reducing frustration. From a business outcome perspective, cleaner data translates to better customer interactions (e.g., personalized service responses), more reliable analytics (e.g., accurate sales forecasts), and, ultimately, increased revenue and profitability.

Conclusion

While training and other governance activities cannot be overlooked, tools like Data Quality Helper address the core data quality needs that Data Cloud and Agentforce depend on. It’s a practical, scalable solution that complements Salesforce’s native tools and aligns with the platform’s data-centric ethos, making it a strong consideration for organizations aiming to maximize Agentforce’s potential while minimizing the enterprise-wide risks of poor data quality.

Ensure that data is accurate, complete, and up-to-date—unlock the full power of Agentforce in particular and Salesforce in general. Drive significant improvements in customer engagement, sales productivity, and overall business success.