Data Quality: the invisible foundation of your Agentforce projects
Artificial intelligence is transforming business line tools, and Agentforce comes with a bold promise: to automate processes and boost CRM efficiency. But without reliable and well-structured data, even the most advanced AI agents cannot reach their full potential.
Ambitious projects held back by data challenges
Agentforce projects are designed to give sales teams a decisive edge. These agents can assist teams, handle routine requests, and even interact directly with customers. But their intelligence depends entirely on the data they are given: customer history, past tickets, preferences, contact details, and more.
Unfortunately, in many organisations, this data is incomplete, inaccurate, or siloed. This gives recommendations that are unreliable, and automated actions that miss their target, meaning end users lose confidence in the system.
Data quality, a critical foundation for Agentforce
Succeeding in a project like Agentforce depends on more than just the technical dimension of its implementation. Success is measured by the business line value it creates, which hinges on the company’s ability to feed its automation engines with high-quality data.
Ensuring data quality means taking a comprehensive approach to make sure data is:
- Complete: every relevant field in the Salesforce CRM system is filled in;
- Reliable: data elements are consistent, exact, and up to date;
- Accessible: data elements are available, without duplication or silos, and easy to use;
- Consistent: data is structured according to business line standards defined in Salesforce.
This rigour is crucial so that Agentforce agents can understand requests, offer relevant responses, and contribute to a seamless customer experience.
Plan for roadblocks from the start
Data quality must be addressed from the project’s early design phase. It’s vital to implement validation, structuring, and standardisation mechanisms aligned with Salesforce objects and metadata. This reduces input errors, detects duplicates as soon as they happen, and ensures perfect consistency with business line processes. By guaranteeing useful, trustworthy data from the outset, you prevent delays following deployment and provide your AI agents with the right conditions to perform.
This means:
- Assessing the current state of your data
- Clearly defining data governance
- Automating checks throughout the entire data life cycle
This proactive approach helps avoid post-launch disappointment, when it becomes clear that AI agents don’t have the data they need to work effectively.
An Agentforce project can’t succeed without a solid foundation: high-quality data. This is much more than just a technical issue, it is a strategic means to guaranteeing AI agent performance and automation reliability. To take full advantage of Agentforce within Salesforce, data governance has to be considered a main pillar of the project from the start.
To find out more, download the e-book, “Agentforce: Ensuring Project Success with Reliable Data and the Right Partners”.
About DQE
Because data quality is essential to customer knowledge and the construction of a lasting relationship, since 2008, DQE has provided its clients with innovative and comprehensive solutions that facilitate the collection of reliable data.
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