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Why Salesforce’s AI Gains Don’t Fix Its Sluggish Growth

Paul Allen

04 Dec 2025 • 6 min read

Salesforce’s latest quarter showed surprising strength, delivering $3.25 per share in earnings before stock compensation—well above analyst expectations. Still, its overall growth remains sluggish, despite heavy investment in agentic artificial intelligence initiatives. The tension lies not in technological capability, but in the systemic constraints limiting Salesforce’s expansion. “AI can accelerate value only when structural bottlenecks are identified and reconfigured,” a principle quietly exposed by this report.

Growth Should Follow Innovation—But It Doesn’t

Conventional wisdom expects that heavy AI investment directly translates into high growth. Analysts see Salesforce’s AI bets as growth catalysts poised to unlock new revenue streams and product efficiencies. They overlook that innovation alone isn’t growth; it’s how you embed that innovation into scalable systems that matters. This disconnect echoes the leverage failures that led to the 2024 tech layoffs, where automation investments failed to reshape business constraints (Think In Leverage).

Salesforce’s AI projects show progress, but their core platforms still wrestle with legacy complexity and customer execution gaps. Unlike OpenAI, which scaled ChatGPT to 1 billion users through a tightly integrated cloud and data infrastructure (Think In Leverage), Salesforce’s AI lacks that systemic embedment in its go-to-market and service mechanics.

Agentic AI Isn’t a Growth Switch Without Platform Leverage

Salesforce’s AI tools automate agent workflows and customer insights, reducing human task burden. But these improvements mostly optimize existing sales and support models rather than redefine them. Competitors like Microsoft and Adobe pursue AI features that integrate directly into developer ecosystems, fostering partner-driven scale at lower acquisition costs.

Salesforce’s CEO implicitly targets this gap by calling out AI as part of a broader platform transformation strategy. But the constraint isn’t AI capability—it’s that Salesforce must redesign its ecosystem to let AI create leverage automatically. This requires enabling independent third-party developers and tighter hooks into enterprise workflows, not just internal tooling upgrades.

What Salesforce Must Do Next to Unlock Systemic Leverage

The core constraint revealed is Salesforce’s legacy platform architecture, which slows integration speed and constrains partner enablement. Solving this is a systems problem: modularizing the platform and embedding AI-driven automation that works without constant human oversight.

Investors and operators should watch Salesforce’s moves to open platform APIs and ecosystem incentives. If Salesforce can reposition itself from a product vendor to an embedded intelligence layer, its AI investments will compound growth. This shift echoes how WhatsApp’s chat integration unlocked new distribution levers (Think In Leverage), but on CRM and sales automation scale.

Without breaking platform constraints, AI remains a faster engine stuck in slow lanes. Salesforce’s results highlight the difference between capability and systemic advantage.

Forward Signals From Salesforce’s AI Experimentation

As Salesforce navigates these systemic limits, other SaaS giants are learning to build AI-driven ecosystems that automate partner and consumer workflows with minimal human intervention. This structural repositioning creates compounding advantage and lowers go-to-market friction.

Companies that recognize AI is a lever, not a standalone growth driver, will outpace peers battling fundamental platform constraints. Salesforce’s AI progress signals a necessary pivot — the future belongs to those redesigning system architecture around AI, not just layering AI on top.

For businesses striving to align their AI advancements with effective sales strategies, tools like Apollo can provide essential insights into B2B leads and sales intelligence. By streamlining prospecting and contact management, Apollo helps facilitate the kind of systemic integration and agile workflows necessary for leveraging AI-driven growth. Learn more about Apollo →

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Frequently Asked Questions

Why is Salesforce's growth sluggish despite its AI advancements?

Salesforce’s growth is hindered by systemic constraints in its legacy platform architecture that slow integration and partner enablement, limiting the scalability of AI-driven innovation.

Salesforce delivered $3.25 per share in earnings before stock compensation, exceeding analyst expectations in the latest quarter.

What is agentic AI and how does Salesforce use it?

Agentic AI refers to AI tools automating agent workflows and customer insights. Salesforce’s AI tools optimize existing sales and support models but do not fundamentally redefine them.

How does Salesforce's AI strategy compare to OpenAI and Microsoft?

Unlike OpenAI’s tightly integrated system scaling ChatGPT to 1 billion users and Microsoft’s AI integration into developer ecosystems, Salesforce’s AI lacks systemic embedment in go-to-market and service mechanics, limiting its leverage.

What platform challenges does Salesforce face affecting AI growth?

Salesforce's legacy platform architecture slows integration speed and constrains partner enablement, making it difficult to embed AI-driven automation that operates without constant human oversight.

What must Salesforce do to unlock systemic leverage with AI?

Salesforce must modularize its platform, open APIs, and incentivize ecosystem participation to embed AI as an intelligence layer within enterprise workflows beyond internal tooling upgrades.

How are other SaaS companies approaching AI differently?

Other SaaS giants are building AI-driven ecosystems that automate partner and consumer workflows with minimal human intervention, creating compounding advantages and lowering go-to-market friction.

What is a key takeaway about AI as a growth driver?

AI is a lever for growth only when integrated systemically; standalone AI investment cannot overcome fundamental platform constraints, as demonstrated by Salesforce’s current experience.