Salesforce Agentforce AI tool reaches 8000 customers with $100M average order value
Salesforce's Agentforce AI tool has attracted over 8000 customers and generates $100 million in average order value, CEO Marc Benioff announced.
Image: Bjorn Bakstad via Getty Images
Salesforce’s agentic artificial intelligence product, Agentforce, has already secured over 8,000 deals and is generating an impressive $100 million in average order value, CEO Marc Benioff revealed during the company’s Q1 2026 earnings call. The tool’s rapid adoption marks it as one of the fastest-growing products in Salesforce’s history, even before full deployment across all geographies, currencies, and languages.
Key Highlights:
- 8,000+ deals closed using Agentforce.
- $100 million average order value.
- 750,000+ requests handled on help.salesforce.com, reducing case volume by 7% YoY.
- 500 customer support employees redeployed to higher-impact AI roles, saving $50 million.
Broader AI Investment Context
Salesforce is among tech giants heavily investing in AI. Microsoft, for instance, plans to spend $80 billion on AI-enabled data centers this fiscal year (source). Salesforce Ventures has also committed $1 billion to AI over 18 months, including a $500 million fund announced last September (source).
Challenges and Market Reaction
Despite reporting $9.8 billion in Q1 revenue (up 8% YoY), Salesforce’s stock fell over 9% following the earnings call. Analysts at RBC Capital Markets downgraded the stock, citing concerns over the $8 billion Informatica acquisition and mixed feedback on Agentforce’s technological maturity (source).
“While we understand wanting to control the full stack at the data layer, we don’t know that it was mission-critical to own Informatica outright,” the analysts noted, highlighting integration risks and potential distractions from Salesforce’s core business.
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About the Author
Dr. Sarah Chen
AI Research Expert
A seasoned AI expert with 15 years of research experience, formerly worked at Stanford AI Lab for 8 years, specializing in machine learning and natural language processing. Currently serves as technical advisor for multiple AI companies and regularly contributes AI technology analysis articles to authoritative media like MIT Technology Review.