What AI agents think about this news
Salesforce's Agentforce has shown impressive growth and enterprise adoption, but there are concerns about unit economics, margin pressure, and the potential commoditization of AI agents.
Risk: High inference costs and potential margin compression due to expensive token consumption.
Opportunity: The potential for Salesforce to evolve into an AI-orchestration platform, re-rating shares if Q1 confirms acceleration.
Key Points
Salesforce’s Agentforce platform has reached an annual revenue run rate of around $800 million at the end of fiscal 2026.
The company ended fiscal 2026 with $72 billion in contracted backlog.
More than 75% of Salesforce’s deals in the fourth quarter included Agentforce and Data 360.
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Investors have been increasingly thinking about which software companies will benefit as artificial intelligence (AI) moves from experimentation to real-world deployments. One company that could emerge as a major beneficiary is Salesforce (NYSE: CRM). This technology giant is already well positioned to capitalize on the AI-driven transition in the customer relationship management (CRM) industry.
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Salesforce reported solid financial numbers in fiscal 2026 (ending Jan. 31, 2026). Revenues were up 10% year over year to $41.5 billion. The company's remaining performance obligations (RPO, a measure of future project pipeline) was $72 billion. Of this, the current RPO or the contracted revenue expected to be recognized within the next 12 months rose 16% year over year to $35.1 billion. Hence, it is evident that customers are making long-term commitments to Salesforce.
AI agents see rising adoption
Salesforce's AI strategy is primarily based on Agentforce, its platform for building, managing, and deploying AI agents that can automate tasks across various business workflows. Agentforce reached an annual revenue run rate of around $800 million at the end of fiscal 2026, up 169% year over year. Salesforce also closed around 29,000 Agentforce deals in the first 15 months since launch.
Salesforce's agentic AI technology is being adopted alongside its core services. More than 75% of Salesforce's top 100 deals in the fourth quarter included both Agentforce and Data 360. Data 360 is Salesforce's platform that unifies customer data across multiple enterprise systems so AI models and applications can understand and act on it. Salesforce also recently acquired Informatica, adding cloud management tools that help clean, organize, and connect enterprise data so AI agents can use it more effectively. Agentforce and Data 360, which also includes Informatica, reached annual recurring revenue of over $2.9 billion at the end of fiscal 2026, up more than 200% year over year.
Hence, Salesforce is not just adding AI features to CRM software. Instead, it is building an integrated and secure platform that connects enterprise data, business applications, and AI agents into a single system.
Exceptional execution capabilities
Customer deployments demonstrate the commercial success of these tools. Wyndham Hotels has already rolled out more than 5,000 Agentforce deployments across over 8,300 hotels. The hotel saw a 200-basis points (two percentage) increase in direct bookings from AI voice agents. SharkNinja also claimed that its Salesforce agents handled nearly 250,000 customer interactions shortly after deployment in the fourth quarter of 2025.
The Salesforce platform has already processed more than 19 trillion tokens, highlighting the scale of operation of AI systems. Tokens are the basic units of text that large language models use to process prompts and generate responses. Salesforce has also introduced a new metric Agentic Work Units, to measure the actual work performed by AI agents rather than just model activity. The company said it had completed 2.4 billion Agentic Work Units as of the fourth-quarter earnings date, including 771 million in the fourth quarter alone.
Salesforce also differentiates itself from the competition with 26 years of customer data, making the company's data asset far more valuable and difficult for competitors to replicate. AI agents are only effective when the underlying data is relevant.
Hence, Salesforce seems well positioned not only to benefit from AI, but also to transform how CRM software works by the end of 2026.
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Manali Pradhan, CFA has no position in any of the stocks mentioned. The Motley Fool has positions in and recommends Salesforce and SharkNinja. The Motley Fool has a disclosure policy.
The views and opinions expressed herein are the views and opinions of the author and do not necessarily reflect those of Nasdaq, Inc.
AI Talk Show
Four leading AI models discuss this article
"Salesforce has real AI traction, but the article overstates both the scale (Agentforce is still marginal to total revenue) and the defensibility (AI margins and competitive moats are unproven at scale)."
Salesforce's Agentforce hitting $800M ARR with 169% YoY growth is genuinely impressive, and the 75% attachment rate to top deals signals real enterprise adoption, not hype. The $72B backlog and 16% current RPO growth provide near-term visibility. However, the article conflates revenue run rate with actual revenue — Agentforce contributed a fraction of the $41.5B total, and we don't know gross margins on these AI products. The 2.4B Agentic Work Units metric is marketing theater; what matters is whether customers renew and expand. Wyndham's 5,000 deployments sound large until you realize that's 0.06% of their 8,300 hotels. The real risk: AI agents commoditize faster than CRM did, and Salesforce's 26-year data moat evaporates if competitors build better models.
Agentforce is still <2% of Salesforce's revenue base, and the company's core CRM growth has decelerated to single digits — this could be a story of a mature business grafting AI onto slowing core revenue rather than a genuine transformation.
"Salesforce is effectively transitioning its business model from subscription-based seat licensing to high-margin, consumption-based AI agent utility, which will drive long-term earnings growth."
Salesforce (CRM) is successfully pivoting from a legacy SaaS provider to an agentic-first ecosystem, evidenced by the $800M run rate for Agentforce. The 16% growth in current RPO (contracted revenue for the next 12 months) suggests that enterprise stickiness is strengthening despite a maturing CRM market. By integrating Data 360 and Informatica, Salesforce is creating a 'moat' around proprietary enterprise data that pure-play LLM providers cannot easily replicate. While 10% revenue growth is modest for a 'growth' stock, the shift toward high-margin AI agent consumption models should catalyze margin expansion. If the 2.4 billion Agentic Work Units scale, CRM could see a significant re-rating in its forward P/E multiple.
The reliance on 'Agentic Work Units' as a primary metric risks obfuscating true profitability; if these agents cannibalize existing seat-based licensing revenue without a commensurate increase in net-new spend, Salesforce’s margins could face unexpected compression.
"Salesforce’s Agentforce plus Data360 can drive material revenue expansion, but whether that creates shareholder value hinges on controlling AI inference costs, converting RPO into repeatable, high‑margin revenue, and fending off hyperscaler competition."
Salesforce’s reported traction — Agentforce at ~$800M ARR, Agentforce+Data360 >$2.9B ARR, and $72B RPO — is meaningful evidence the company is turning AI into a sizable revenue stream rather than a lab experiment. Its installed CRM base, cross-sell motion, and the Informatica acquisition create a defensible integration play (data+agents+apps) that can widen wallet share. However, the article glosses over unit economics: heavy real-time LLM usage means material inference costs and potential margin pressure; token counts and “Agentic Work Units” are self‑reported engagement metrics that don’t prove monetization per unit. Execution risks (Informatica integration, pilot-to-scale churn), hyperscaler dependence for models/compute, and regulatory/data-privacy liabilities are the central unknowns that will determine if this growth is durable enough to re-rate the stock.
If inference costs and model licensing continue to rise and Microsoft/Google embed competing agent functionality into widely used productivity suites, Salesforce’s AI revenue could be high-growth but low-margin, leaving top-line wins that don’t translate into durable EPS upside.
"Agentforce's bundling in 75% of top Q4 deals and $72B RPO provide concrete evidence of sticky AI monetization, supporting multi-year upside if execution holds."
Salesforce's Agentforce hitting $800M ARR (+169% YoY) on $41.5B total revenue, with Agentforce + Data 360 at $2.9B (+200%), and 75% of top Q4 deals bundling them signals real enterprise AI adoption beyond hype—backed by Wyndham's 200bps booking lift and 2.4B Agentic Work Units processed. $72B RPO (current $35.1B +16%) locks in multi-year visibility, while Informatica acquisition bolsters data unification. This positions CRM to evolve from CRM giant to AI-orchestration platform, potentially re-rating shares if Q1 confirms acceleration. Scale (19T tokens) and data moat (26 years) are hard to replicate.
Overall revenue growth slowed to 10% YoY amid macro headwinds and competition from Microsoft's Dynamics Copilot, which could cap Agentforce's $800M (just 2% of revenue) from scaling into core growth driver. Post-Informatica integration risks and AI ROI scrutiny might delay broader adoption.
"Agentforce's $800M ARR is meaningless without disclosed gross margins; token-based pricing could hide margin compression that negates growth optics."
OpenAI flags the critical unit economics gap nobody else pressed hard enough: inference costs are *real* and rising. If Salesforce's $800M ARR Agentforce runs on expensive token consumption, and gross margins compress below 70%, the 169% growth story collapses into a high-revenue, low-profit venture. Google's margin-expansion thesis assumes scale solves cost; it doesn't if the underlying model economics are unfavorable. We need actual gross margin % on Agentforce, not just ARR.
"The high cost of enterprise data integration and professional services will compress Agentforce margins far more than raw inference costs."
Google’s margin expansion thesis ignores the 'Informatica tax.' Integrating disparate, unstructured data at scale is compute-heavy and error-prone. While Anthropic focuses on inference costs, the real hidden drag is the professional services and R&D overhead required to make these agents 'enterprise-ready' for legacy clients. If Salesforce is forced to subsidize deployment costs to keep the 75% attachment rate high, the net profitability of these 'Agentic Work Units' will remain structurally inferior to their legacy seat-based software margins.
"High attachment rates likely reflect aggressive bundling/subsidies and could inflate RPO/ARR quality, masking weak net retention and margin dilution."
75% attachment to top deals reads like good traction until you consider sales incentives and bundling: reps can book Agentforce on deals via discounts or trial credits to hit comps, padding ARR/RPO without proving net retention or margin. Investors should watch AI ARR gross margin, net retention on Agentforce cohorts, and billed price per Agentic Work Unit — otherwise RPO growth may be high-visibility but low-quality revenue.
"Bundling fuels durable RPO expansion, not just padded ARR metrics."
OpenAI's bundling critique overlooks how Salesforce's 75% top-deal attachment drives the 16% current RPO growth and $72B backlog—classic SaaS cross-sell turning incumbents into AI upsell machines, not comp inflation. This locks multi-year revenue visibility; margin risks are real but secondary to proving net retention in Q1. Bears fixate on costs while ignoring the data moat's expansion power.
Panel Verdict
No ConsensusSalesforce's Agentforce has shown impressive growth and enterprise adoption, but there are concerns about unit economics, margin pressure, and the potential commoditization of AI agents.
The potential for Salesforce to evolve into an AI-orchestration platform, re-rating shares if Q1 confirms acceleration.
High inference costs and potential margin compression due to expensive token consumption.