Bringing AI functionality into your Salesforce org—especially with a powerful tool like Agentforce— can feel like stepping into the future. But before you start letting AI handle customer interactions and internal processes, there’s a big question to answer: “Is your data ready for it?”
Without a solid data governance strategy, even the most advanced AI can misfire. You might have the best algorithms in place, but if your AI is pulling from outdated, incomplete, or overly permissive data structures, you’re setting yourself up for a mess. That’s why we keep emphasizing data governance—it should be the foundation of any Agentforce implementation. So let’s break it down: why it matters and how to get it right.
Who Sees What? The Critical Role of Data Access Controls
Salesforce has long been a leader in data security, offering layers of built-in permissions and controls to keep information where it belongs. The new Agentforce AI features are simply layered on top of this existing security framework, meaning if there are cracks in your data access settings, AI will inherit them.
At the beginning of most AI projects, we perform an audit of our client’s Salesforce org—essentially a health check of the system—to determine AI readiness and recommend changes, if necessary. This is a reality check—are users seeing [only] what they need to? The most common red flag we see is that user profiles have more access than they should. Simply implementing Salesforce best practices with respect to data access will help to ensure that AI applications are sharing appropriate information with each user. Among users who should technically have the same level of access, there may be several different audiences who need different information. Fine-tuning access controls is as much about security as it is about ensuring AI delivers accurate, relevant responses.
For Example: Let’s say your customer service reps handle different regions, but there’s no restriction on which cases they can access. If AI pulls insights from one region and serves them up to another, you could end up with compliance issues or just plain confusion.
When it comes to data hygiene, we love to use the phrase, “Garbage In, Garbage Out”. In the current context, we’re saying, “AI is only as good as the information it has to work with”. If it’s pulling from outdated or miscategorized knowledge articles, it’s going to give outdated or misleading answers. This is where structured knowledge management becomes essential.
As a company that jumped in headfirst with Agentforce implementations, we’ve learned a thing or two about the role data hygiene and governance play in AI success. One key insight? Many organizations eager to adopt AI weren’t as prepared when it came to structuring and organizing the data that powers it. So, what did we do? We developed a method to help AI applications recognize the most common wording associated with a topic. To make this work, our clients update each knowledge article with a summary that includes alternate search terms—especially for product names, features, technical terms, acronyms, and industry jargon—ensuring AI can retrieve and interpret information accurately.
Key Reminders when Organizing Knowledge Articles to Increase AI Readiness
- Ensure knowledge articles are accurate and current: AI can’t fact-check in real-time. If an article hasn’t been updated in months or years, it could be spreading incorrect information.
- Categorize content properly: If knowledge articles aren’t labelled correctly, AI may struggle to find the best answer, leading to irrelevant or incomplete responses.
- Use alternate search terms: This is to prepare for people describing things in different ways. For example, an article titled “How to make a deposit” should include alternate search terms like “adding funds”, and an article about Salesforce’s Field Level Security feature should have “FLS” as an alternate search term. Including common synonyms, abbreviations, and acronyms in knowledge article summaries makes AI more effective.
Implementing AI isn’t as simple as flipping a switch—it’s about making sure the right data reaches the right people at the right time. A well-structured governance strategy doesn’t just keep things running smoothly; it prevents security risks and misinformation from becoming major roadblocks.
If you’re assessing your company’s AI readiness or already rolling out Agentforce, start by evaluating your data governance strategy. Who has access to what? How is your knowledge base structured? Is your AI learning from clean, well-organized data?
AI is here to stay, but whether it works for you or against you depends on how well your data is managed. Let’s get it right from the start. Need some help with planning or execution? Let’s chat.
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