AI Agents for Sales Enablement: Extract Email Data and Accelerate Quoting

In our previous post, Unlock Faster Quoting with Email2Case, we explored how Email-to-Case can automate case creation from incoming quote request emails. While this provides a solid foundation, there’s a critical next step: extracting structured data from those emails and automatically creating and enhancing Salesforce records.

This is where Salesforce Agentforce—Salesforce’s AI agent platform—transforms the quoting process. Instead of having sales representatives manually read emails, extract information, and create records, AI agents can do this work automatically, enabling your sales team to focus on what they do best: building relationships and closing deals.

The Problem: Manual Data Entry Slows Sales Teams

Traditional Email-to-Case workflows create cases from emails, but sales teams still face a significant bottleneck: manual data extraction and record creation. When a quote request email arrives, someone must:

1. Read the email carefully
2. Extract key information (company name, contact details, product requirements, census data)
3. Create or update Account records
4. Create or update Contact records
5. Create Opportunity records
6. Populate custom fields with extracted data
7. Attach relevant documents
8. Route to the appropriate sales representative

This manual process can take 15-30 minutes per quote request, and errors are common. For sales teams processing dozens of quote requests daily, this represents hours of lost productivity.

The Solution: AI Agents That Enable Sales Teams Natively

Salesforce Agentforce creates autonomous AI agents that can extract structured data from emails, create and enhance Salesforce records automatically, and route opportunities to sales teams—all without human intervention. These agents work 24/7, processing emails as they arrive and ensuring your sales team has complete, accurate records ready to work.

Why Sales Enablement, Not Customer Service?

There’s a common misconception that AI agents are primarily for customer service. While they can handle service inquiries, their real value is in enabling sales teams natively. Here’s why:

Customer Service Agents:

• Handle reactive inquiries
• Answer questions from existing customers
• Resolve issues and complaints
• Focus on satisfaction and retention

Sales Enablement Agents:

• Proactively extract data from incoming opportunities
• Create and enhance records automatically
• Route qualified leads to sales teams
• Accelerate the sales cycle from first contact to quote delivery

The key difference: Sales enablement agents work behind the scenes to prepare opportunities for sales teams, while customer service agents interact directly with customers. For quoting processes, you need agents that enable sales teams, not replace customer interactions.

How Agentforce Extracts Data from Emails

Agentforce uses large language models (LLMs) to understand the full context of email content, extract structured data, and create Salesforce records automatically. Here’s how it works:

1. Email Analysis and Data Extraction

When an email arrives via Email-to-Case, an Agentforce agent analyzes the content to extract:

Account Information:

• Company name
• Industry
• Company size (number of employees)
• Location/address
• Website

Contact Information:

• Contact name
• Email address
• Phone number
• Job title
• Role in decision-making process

Quote Request Details:

• Product interest (medical, dental, vision, etc.)
• Requested effective date
• Group size or census information
• Current carrier information
• Prior claims data
• Budget or premium requirements

Attachments:

• Census files (extract employee data using OCR)
• Current plan documents
• Prior claims data
• Other relevant documents

2. Automatic Record Creation and Enhancement

Once data is extracted, the agent automatically:

Creates or Updates Accounts:

• Searches for existing accounts by company name or domain
• Creates new accounts if not found
• Updates existing accounts with new information
• Links cases to accounts

Creates or Updates Contacts:

• Searches for existing contacts by email address
• Creates new contacts if not found
• Updates contact information
• Links contacts to accounts
• Identifies decision-makers

Creates Opportunities:

• Creates opportunity records from quote requests
• Links opportunities to accounts and contacts
• Populates opportunity fields (amount, close date, stage)
• Sets opportunity type and record type
• Links cases to opportunities

Populates Custom Fields:

• Extracts and populates industry-specific fields
• Census data (number of employees, demographics)
• Product interest fields
• Requested effective dates
• Budget or premium information

Attaches Documents:

• Links email attachments to records
• Extracts data from PDFs and spreadsheets using OCR
• Creates structured data from unstructured documents

3. Intelligent Routing and Prioritization

After records are created, the agent:

Routes to Sales Teams:

• Assigns opportunities based on territory rules
• Routes to appropriate product specialists
• Assigns based on account size or complexity
• Routes to queues for team assignment

Prioritizes Opportunities:

• Scores opportunities based on extracted data
• Prioritizes high-value opportunities
• Identifies urgent requests
• Flags opportunities requiring immediate attention

Real-World Example: Insurance Quoting Workflow

Here’s how Agentforce transforms the insurance quoting process:

Before Agentforce (Manual Process)

1. Email arrives → Case created (2 minutes)
2. Sales rep reads email → Extracts information manually (10 minutes)
3. Sales rep creates Account → Searches, creates, populates fields (5 minutes)
4. Sales rep creates Contact → Searches, creates, links to account (3 minutes)
5. Sales rep creates Opportunity → Creates, links records, populates fields (5 minutes)
6. Sales rep reviews attachments → Downloads, reviews, extracts data (10 minutes)
7. Sales rep routes opportunity → Assigns to appropriate team member (2 minutes)

Total Time: 37 minutes per quote request

After Agentforce (Automated Process)

1. Email arrives → Case created automatically (instant)
2. Agentforce agent analyzes email → Extracts all data automatically (30 seconds)
3. Agent creates/updates Account → Automatic (instant)
4. Agent creates/updates Contact → Automatic (instant)
5. Agent creates Opportunity → Automatic (instant)
6. Agent processes attachments → OCR extraction, data population (1 minute)
7. Agent routes opportunity → Automatic assignment (instant)

Total Time: ~2 minutes per quote request

Time Saved: 35 minutes per quote request (95% reduction)

For a sales team processing 20 quote requests per day, this represents 11.7 hours of saved time daily—time that can be spent on relationship building, proposal development, and closing deals.

Implementation Guide: Setting Up Agentforce for Email Extraction

Step 1: Configure Email-to-Case

Ensure Email-to-Case is configured and working (see our previous post for details). This provides the foundation for Agentforce agents to process incoming emails.

Step 2: Create an Agentforce Agent

Use Salesforce’s Agent Builder to create a custom agent for quote request processing:

1. Navigate to Agent Builder in Salesforce Setup
2. Create a new agent for “Quote Request Processing”
3. Define the agent’s purpose: Extract data from quote request emails and create Salesforce records
4. Set guardrails: Define what the agent can and cannot do
5. Configure data sources: Connect to Salesforce objects and external data sources

Step 3: Configure Data Extraction

Define what data the agent should extract from emails:

Account Fields:

• Company name (required)
• Industry
• Number of employees
• Billing address
• Website

Contact Fields:

• First name
• Last name
• Email address (required)
• Phone number
• Job title
• Role

Opportunity Fields:

• Opportunity name
• Amount (if mentioned)
• Close date (requested effective date)
• Stage (set to “Qualification” or “Prospecting”)
• Product interest
• Group size

Custom Fields:

• Census data
• Current carrier
• Prior claims information
• Budget range
• Requested effective date

Step 4: Configure Record Creation Logic

Define how the agent should create and update records:

Account Matching:

• Search by company name (fuzzy matching)
• Search by email domain
• Create new if no match found
• Update existing if match found

Contact Matching:

• Search by email address (exact match)
• Create new if no match found
• Update existing if match found
• Link to account

Opportunity Creation:

• Always create new opportunity from quote request
• Link to account and contact
• Set default values based on extracted data
• Apply record type based on product interest

Step 5: Configure Attachment Processing

Set up OCR and data extraction for email attachments:

Census Files:

• Extract employee data (name, DOB, gender, etc.)
• Create custom object records or populate fields
• Validate data completeness

Documents:

• Extract text from PDFs
• Identify document type
• Extract relevant data points
• Link documents to records

Step 6: Configure Routing Rules

Define how opportunities should be routed:

Territory-Based Routing:

• Route based on account location
• Route based on account size
• Route based on product interest

Team-Based Routing:

• Route to specialized teams (large group, small group, etc.)
• Route based on opportunity value
• Route based on complexity

Step 7: Test and Iterate

Test the agent with sample emails:

1. Send test emails with various formats and data
2. Verify data extraction accuracy
3. Verify record creation completeness
4. Verify routing assignments
5. Gather feedback from sales team
6. Refine agent configuration based on results

Best Practices for Sales Enablement Agents

1. Start with High-Volume, Low-Complexity Processes

Begin with processes that have:

• High volume: Many emails to process
• Low complexity: Clear, structured data
• High value: Significant time savings

Quote requests are perfect because they arrive frequently, contain structured information, and manual processing is time-consuming.

2. Define Clear Data Extraction Rules

Be specific about what data to extract:

• Required fields: What must be extracted for the record to be useful
• Optional fields: What’s nice to have but not critical
• Validation rules: What data must pass validation before record creation

3. Implement Human Oversight

Even with AI agents, implement oversight:

• Review queue: Flag records for human review when confidence is low
• Exception handling: Route complex cases to humans
• Quality checks: Periodically review agent-created records

4. Continuously Improve

Monitor and improve agent performance:

• Track accuracy: Measure data extraction accuracy
• Gather feedback: Get input from sales teams
• Refine rules: Update extraction rules based on real-world usage
• Expand capabilities: Add new data extraction fields over time

5. Measure Impact

Track key metrics:

• Time saved: Hours saved per day/week
• Records created: Number of records created automatically
• Accuracy rate: Percentage of correctly extracted data
• Sales team satisfaction: Feedback from sales representatives

Why Native Sales Enablement Matters

Traditional approaches often rely on customer service agents to support sales teams, but this creates inefficiencies:

Problems with Service-Focused Agents:

• Agents are optimized for reactive customer inquiries
• They don’t understand sales processes and workflows
• They create records in service objects, not sales objects
• They lack context about sales priorities and territories
• They can’t prioritize based on sales criteria

Benefits of Native Sales Enablement:

• Agents are built specifically for sales processes
• They understand sales workflows and terminology
• They create records in the right objects (Accounts, Contacts, Opportunities)
• They route based on sales criteria (territory, product, value)
• They prioritize based on sales priorities

The result: Sales teams get complete, accurate records ready to work, enabling them to respond faster and close more deals.

Real-World Impact

Organizations implementing Agentforce for sales enablement report:

• 95% reduction in manual data entry time
• 80% faster quote response times
• 60% increase in quote request processing capacity
• 40% improvement in data accuracy
• 25% increase in sales team productivity

Real-World Customer Success Stories

Organizations across industries are using Agentforce to transform their sales and service operations. Here are real-world examples of successful implementations:

TASC Outsourcing: Scaling Sales with AI SDRs

Challenge: TASC’s sales team was overwhelmed with manual research, personalization, and lead qualification tasks.

Solution: TASC implemented three Agentforce agents to handle research, personalization, and qualification automatically.

Results:

• 5.8x increase in email response rates
• 2,194 actionable leads generated automatically
• Sales team freed to focus on high-value activities

Learn More: TASC Case Study

Houzbay: Real-Time Lead Intelligence

Challenge: Houzbay needed to improve conversion rates and reduce cost per lead.

Solution: Deployed intelligent lead scoring with automated workflows through Agentforce.

Results:

• 30% conversion rate (3x improvement)
• 45% reduction in cost per lead
• Automated lead qualification and routing

Learn More: Houzbay Case Study

Simplyhealth: Expanding Service and Sales Capacity

Challenge: Simplyhealth needed to scale their service and sales operations without proportional staff increases.

Solution: Implemented Agentforce to answer frequently asked questions instantly and provide 24/7 lead management and nurturing.

Results:

• Multiplied service and sales capacity without adding staff
• 24/7 lead management and nurturing
• Continuous engagement with potential clients

Learn More: Simplyhealth Customer Story

Reddit: Driving Revenue with Advertiser Support

Challenge: Reddit needed to support small and medium-sized business advertisers more effectively.

Solution: Deployed Agentforce-powered AI agents to assist advertisers with platform navigation, campaign launch, and ongoing engagement.

Results:

• Improved advertiser support for SMBs
• Faster campaign launches
• Increased revenue through better advertiser engagement

Learn More: Reddit Customer Story

Global Social Media Company: Boosting Self-Service

Challenge: A leading social media company needed to improve self-service capabilities for advertisers.

Solution: Implemented Agentforce service agents to handle advertiser inquiries automatically.

Results:

• 80% increase in case deflection
• Higher advertiser satisfaction
• Reduced support team workload

Learn More: Social Media Company Case Study

Healthcare Payer: Fraud Detection and Cost Reduction

Challenge: A leading healthcare payer needed to improve fraud detection while reducing costs.

Solution: Implemented an Agentforce AI-powered Predictive Analytics Agent for fraud detection.

Results:

• 40% increase in detected fraud cases
• 65% reduction in costs
• Improved compliance and revenue protection

Learn More: Healthcare Payer Case Study

GE Appliances: Instant, Personalized Service

Challenge: GE Appliances needed to provide instant, personalized service to customers.

Solution: Integrated Agentforce to unify customer data and enable autonomous AI agents to assist consumers.

Results:

• Instant, personalized service delivery
• Improved customer satisfaction
• Unified customer data across touchpoints

Learn More: GE Appliances Customer Story

Flok: Rapid Deployment of Knowledge Agent

Challenge: Flok needed a complex Agentforce knowledge agent deployed on a tight schedule.

Solution: Partnered with implementation experts to deploy a production-ready knowledge agent.

Results:

• Production-ready deployment in 8 business days
• 60% faster than industry standard
• Complex knowledge agent successfully deployed

Learn More: Flok Case Study

Heathrow Airport: Personalizing Passenger Experiences

Challenge: Heathrow Airport needed to personalize experiences for 83 million annual passengers.

Solution: Planning to use Agentforce to deploy AI agents that personalize passenger experiences.

Expected Results:

• Personalized experiences for millions of passengers
• Easier travel through AI-powered assistance
• Revenue growth through enhanced customer interactions

Learn More: Heathrow Airport Customer Story

IRS: Enhancing Operational Efficiency

Challenge: The Internal Revenue Service needed to improve efficiency in tax processing and taxpayer services.

Solution: Began deploying Salesforce’s Agentforce across key divisions including the Office of Chief Counsel, Taxpayer Advocate Services, and the Office of Appeals.

Expected Results:

• Augmented human review in tax processing
• Enhanced operational efficiency
• Improved taxpayer services

Learn More: IRS AI Agent Deployment

Integration with Existing Workflows

Agentforce integrates seamlessly with:

• Email-to-Case: Processes emails as they arrive
• Sales Cloud: Creates and updates sales records
• Service Cloud: Links cases to opportunities
• Einstein AI: Provides intelligent prioritization and scoring
• Flow: Automates follow-up actions
• OmniStudio: Powers guided quoting experiences

Resources

• Unlock Faster Quoting with Email2Case - Foundation post on Email-to-Case
• Salesforce Agentforce - Official Agentforce documentation
• Salesforce Help: Agent Builder - Building custom agents
• Structuring Medical Plans with the Order Object - Order management workflow
• Use Salesforce OmniStudio to Digitally Enable Business Processes - Process automation guide

Conclusion

AI agents shouldn’t be limited to customer service—they should enable sales teams natively. By using Agentforce to extract data from incoming quote request emails and automatically create and enhance Salesforce records, you can transform your quoting process from a manual, time-consuming task into an automated, efficient workflow.

Sales teams spend less time on data entry and more time on what matters: building relationships, developing proposals, and closing deals. The result is faster quote responses, higher data accuracy, and increased sales productivity.

Don’t let your sales team waste time on manual data entry. Implement Agentforce for sales enablement and watch your quoting process accelerate.