Salesforce's FY26 Q4 earnings highlight significant FY27 ARR growth expected from Agentforce and Data 360 segments, driven by rapid adoption and strategic investments. Agentforce shows particularly strong momentum, while Data 360 maintains steady growth potential.
Generated by Dafinchi AI. Source-grounded AI analysis, not investment advice.
Can you quantify FY27 ARR contribution expected from Agentforce versus Data 360 separately?
Based on the fiscal 2026 Q4 earnings transcript for Salesforce, Inc. (CRM), the key points and quantitative data relevant to the FY27 ARR (Annual Recurring Revenue) contribution expectations from Agentforce and Data 360 are as follows:
Source: Management commentary highlighting strong Agentforce and Data 360 ARR including Informatica contribution in FY26 123
Source: FY27 revenue guidance, FY30 framework update, bookings commentary 456738
| Segment | FY26 ARR ($bn) | Growth Expectation | FY27 ARR Estimate ($bn) | Notes |
|---|---|---|---|---|
| Agentforce | 0.8 | Very high growth (169% in FY26, >100% expected FY27) | ~1.3+ | Triple Q4 bookings QoQ; key driver |
| Data 360 (excluding Informatica) | ~1.0 | Moderate growth (~10-15%) expected | ~1.1-1.2 | Driven by Data harmonization and activation |
| Informatica Cloud (part of Data 360) | 1.1 | Stable to moderate growth | Likely stable/slight growth | Part of Data 360 ARR, acknowledged separately |
| Total Agentforce + Data 360 incl. Informatica | 2.9 | Continuing strong cumulative growth | >3.6 | Supported by guidance and pipeline |
Sources: 14273
This forecast aligns with management commentary emphasizing strong second-half revenue reacceleration and the incremental impact of continued investments in sales capacity and product innovation.
References:
1,4,5,6,2,7,3,8 (All inline citations refer to the provided Salesforce FY26 Q4 Earnings transcript excerpts.)
Disclaimer: The output generated by dafinchi.ai, a Large Language Model (LLM), may contain inaccuracies or "hallucinations." Users should independently verify the accuracy of any mathematical calculations, numerical data, and associated units, as well as the credibility of any sources cited. The developers and providers of dafinchi.ai cannot be held liable for any inaccuracies or decisions made based on the LLM's output.
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