AI can do incredible things in Salesforce—from predicting customer behaviour to automating service—but only if it’s built on a foundation of clean, reliable data. If your Salesforce org is full of duplicates, incomplete records, or outdated information, AI tools like Einstein won’t deliver their full potential.
A 2024 Salesforce survey found that more than half of knowledge workers worldwide don’t trust the data used to train AI. That’s a big warning sign. Without trustworthy data, AI can make inaccurate predictions, deliver suboptimal customer experiences, and waste valuable resources.
Whether you’re running predictive analytics, building personalized campaigns, or streamlining customer support, clean data is where it all starts.
Where does AI fit into Salesforce?
When your data is clean and reliable, Salesforce AI becomes a valuable tool. Here are some common ways it helps:
- Predictive Analytics: Einstein reviews past trends to help sales teams target the most promising leads.
- Sales Forecasting: AI provides more accurate revenue estimates and points out deals that are likely to close.
- Customer Service Automation: Tools like Agentforce handle cases faster, improving customer satisfaction.
- Personalized Marketing: AI segments audiences to run campaigns that truly connect.
- Inventory Optimization: AI identifies demand patterns so you can prevent stock shortages or excess inventory.
- Fraud Detection: It flags unusual activity early, reducing the risk of bigger problems.
How Data Cleaning Improves AI Accuracy?
Example: Cleaning Up Leads
One Salesforce project focused on removing duplicate leads to improve lead scoring. The process was simple but effective:
- Found duplicates using Salesforce’s built-in tools.
- Merged records to keep the most accurate information.
- Set up validation rules to stop future duplicates.
The result? Better lead quality, improved sales performance, and a higher ROI on marketing campaigns.
How does XL-Connector help?
Salesforce can detect duplicates, but resolving them individually is time-consuming. XL-Connector makes it faster to clean data:
- Handling duplicates in bulk – Pull duplicate lead records into Excel, compare them side by side, choose what to keep, and update Salesforce directly — no exporting or re-importing required.
- Managing validation rules – Review, activate, or modify rules from a spreadsheet, which is faster when auditing or making multiple changes.
Combining Salesforce tools with XL-Connector reduces cleanup time and ensures AI models work with accurate data.
A Practical Approach to Starting Your Data Cleanup
The smartest way to kick off a Salesforce data cleanup is to connect it directly to the feature or process you want to build—especially if you’re working with Agentforce. Instead of trying to clean every record and every field in the org, zero in on the data that actually matters for your use case.
Let’s say you’re creating an Agentforce-powered process that flags and routes high-priority support cases to a specialist within two hours. In this setup:
- Agentforce can help identify or confirm the case priority based on the details.
- Salesforce automation tools, such as Flows, Assignment Rules, or Omni-Channel Routing, handle the actual assignment.
You don’t need to touch every field on the Case object or related Account record. You only need a clean set of core fields, like:
- Case ID
- Case Priority
- Case Creation Time
- Assigned Specialist Field
- Related Account Name
Focusing on these essentials ensures your AI and automation work smoothly without unnecessary cleanup. Think of it as boiling a cup of water instead of the whole ocean—you’re tackling what’s needed right now and leaving the rest for later.
This “start small” approach gives you quick wins, builds trust in AI outputs, and makes it easier to expand your cleanup efforts over time. Each small project improves your overall Salesforce data quality, setting the stage for more advanced and reliable AI features in the future.
