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Salesforce forecasts weak Q3 revenue on slow AI monetization
Salesforce forecast Q3 revenue below analyst expectations, citing slower monetization of its AI agent platform as clients reduce spending amid economic uncertainty.
The company also expanded its share buyback program by US$20 billion, but shares fell over 5% in after-hours trading.
Salesforce has integrated AI across its cloud services, including a platform called Agentforce, aiming to streamline operations and boost margins in line with investor expectations.
Investors remain cautious about the timeline for returns from large AI investments as macroeconomic conditions remain volatile.
CEO Marc Benioff recently said the company cut 4,000 customer support jobs due to AI, with the technology now handling a significant portion of tasks.
Salesforce expects Q3 revenue between US$10.2 billion and US$10.3 billion, with the midpoint below analyst estimates.
Adjusted Q3 earnings are projected at US$2.8 to US$2.9 per share.
🔗 Source: Reuters
🧠 Food for thought
1️⃣ Massive AI investments haven’t yet translated to proportional revenue returns
Salesforce’s revenue forecast miss highlights a broader challenge across the tech industry where AI spending has dramatically outpaced immediate returns.
While the global AI market reached $391 billion in 2024 and is projected to hit $1.81 trillion by 2030, individual companies are struggling to monetize these investments quickly enough to satisfy investor expectations1.
The scale of this investment disparity is significant. Major tech companies are projected to invest $364 billion in data centers in 2025, yet converting these investments into immediate revenue growth remains challenging.
Salesforce’s experience demonstrates that even with significant AI integration efforts like their Agentforce platform, achieving immediate financial returns is difficult.
This reflects the broader reality that while 90% of organizations believe AI will provide competitive advantage, only 22% actually have visible AI strategies in place2.
2️⃣ Enterprise AI adoption lag creates a customer readiness bottleneck
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