In a few years we might look back and discover some investors were pretty significant winners; others losers as investors in enterprise software stocks, given the current volatility.
If software valuation multiples permanently contract lower, bears will have been proven correct. But if AI disruption proves less damaging, then enterprise software leaders are on sale.
According to Goldman Sachs analyst Matthew Martino, investors need to consider six different aspects of how artificial intelligence might affect an enterprise software supplier’s business:
Orchestration risk: The possibility that horizontal AI agent layers can bypass the platform and become the primary generator of value
Monetization model: Whether a business model is tied to users, which makes it more vulnerable, or assets and data, making it more durable
System-of-record ownership: If the platform governs approvals, compliance, and execution, it is harder to displace
Data and integration moat: Whether workflows depend on proprietary signals, structured data, and operational records that live inside the platform and must be accessed through it
AI execution: Whether the company is delivering real, embedded capabilities rather than conceptual roadmaps
Budget alignment: Determines whether AI adoption increases or decreases the strategic priority of the category.
For investors, the key takeaways might be:
Pure workflow or UI-heavy tools are more exposed; systems handling intricate enterprise logic less so
Subscription models linked to business scale rather than headcount tend to hold up better as AI automates tasks
Core ERP, financial systems, CRM with compliance hooks, or regulatory platforms are harder to displace
AI agents amplify the value of clean, governed, real-time data rather than replacing it
Execution separates fast followers who reinforce their moat from those getting disrupted. Incumbents with domain data have an advantage here
Look for categories where AI creates tailwinds, like data platforms, cybersecurity, vertical SaaS with complex compliance, or physical-to-digital workflows.
Company | Orchestration Risk | Monetization Model | System-of-Record Ownership | Data & Integration Moat | AI Execution | Budget Alignment |
Salesforce (CRM) | Moderate (−) Agentforce agents could be orchestrated externally, but platform workflows limit full bypass | Strong (+) Shifted to agentic ELAs + consumption (per conversation/action); Agentforce ARR ~$800M+ and growing fast | Strong (+) Core CRM is authoritative for customer data, approvals, compliance | Strong (+) Deep customer interaction history, proprietary signals, Data Cloud integrations | Strong (+) Shipping real agents (Agentforce) with measurable ROI; Einstein embedded | Strong (+) AI boosts CRM priority & spend (more data, governance, agent infra) |
ServiceNow (NOW) | Moderate-Strong (+) Complex, multi-system IT/service workflows hard for horizontal agents to fully orchestrate | Strong (+) Now Assist “Pro Plus” premium tiers + consumption upside; ACV >$600M–$1B run-rate | Strong (+) System-of-record for IT/service management, approvals, audit trails | Strong (+) Proprietary operational workflows + deep integrations across enterprise tools | Strong (+) Now Assist agents delivering real value; infrastructure fully cloud-native for AI scale | Strong (+) AI adoption increases strategic priority of service/ops platforms |
Oracle (ORCL) | Strong (+) Deep ERP/supply-chain/finance processes resist simple agent bypass | Moderate-Strong (+) Mix of license + cloud consumption; moving toward outcome-based | Strong (+) Authoritative ERP/finance/supply chain system-of-record with compliance hooks | Strong (+) Massive proprietary enterprise data across finance, supply chain, HR | Strong (+) Using AI to build full automated processes (not just copilots); ahead on domain execution per some peers | Strong (+) AI tailwinds for database/cloud infra spend |
Workday (WDAY) | Moderate (−) HR/finance copilots can partially bypass UI; more replicable workflows | Moderate (−) Still largely seat/user-tied; slower shift to consumption | Strong (+) Core HR/finance system-of-record for employee data & compliance | Moderate (−) Data is relatively standardized vs. highly proprietary; easier for AI to replicate externally | Moderate (+) Has copilots but viewed as lagging some peers on full agentic execution | Moderate (+) AI helpful but could shift some budget away from traditional HCM suites |
SAP (SAP) | Strong (+) ERP complexity & regulatory workflows make full orchestration bypass difficult | Moderate-Strong (+) Cloud shift + some consumption elements; historical pricing power | Strong (+) Dominant global ERP system-of-record for finance, supply chain, compliance | Strong (+) Deep structured operational data + decades of integrations | Moderate-Strong (+) Embedding AI into core ERP; fast-follower re-architecture | Strong (+) AI increases need for governed ERP data & execution layers |
Adobe (ADBE) | Moderate (−) Generative tools could let agents bypass some creative workflows/UI | Moderate-Strong (+) “Generative Credits” + consumption model for Firefly; shifts value capture to usage | Moderate (+) Creative asset system-of-record but less regulatory/compliance weight | Moderate-Strong (+) Proprietary creative workflows + Firefly-trained data moat | Strong (+) Firefly & generative AI delivering real embedded capabilities; strong execution in content | Moderate-Strong (+) AI boosts content creation budget but compresses some per-seat value |
Snowflake (SNOW) | Moderate (−) Data platform can be bypassed as pure backend; agents query elsewhere | Strong (+) Consumption-based (compute/data usage); Cortex AI adds monetization vectors | Moderate (+) Strong data platform but not always the business process system-of-record | Strong (+) Data cloud moat + governance; AI amplifies need for clean, governed data | Strong (+) Cortex AI services shipping real capabilities on customer data | Strong (+) AI dramatically increases data volume/query spend (force multiplier) |
MongoDB (MDB) | Moderate (−) Flexible document DB can be commoditized by agents using open formats | Strong (+) Consumption-based usage; vector search/AI integrations drive upside | Moderate (+) Application data store but rarely the final system-of-record | Moderate-Strong (+) Developer-friendly data + vector capabilities; less proprietary than ERP data | Moderate-Strong (+) Vector search & AI app tools executing well; benefits from AI dev boom | Strong (+) AI apps drive more database consumption and developer spend |
And even if all that is correct, the palpable investor fears about how to value enterprise software firms do not seem to be abating. But some analysts think a “new normal” valuation level is close to stabilizing.
Software Segment | New "Floor" P/E | Historical Context |
Mega-Cap SaaS (Microsoft, SAP) | 28x – 32x | Historic: 35x+ |
High-Growth / "Rule of 40" (ServiceNow, CrowdStrike) | 45x – 55x | Historic: 80x – 100x+ |
Mature / Cyclical Enterprise (Salesforce, Oracle) | 18x – 24x | Historic: 25x – 30x |
Infrastructure / Dev Ops (Datadog, Snowflake) | 50x – 60x | Historic: 100x+ |
Mid-Market / "Broken" SaaS | 12x – 16x | Historic: 25x |
In 2021, software was valued on Enterprise Value/Revenue (often 15x–20x).
Analysts now believe the stable floor is EV/Free Cash Flow or Forward P/E.
For a standard healthy software company, a 5x–6x revenue multiple is now considered "stable," whereas 10x or more is reserved only for elite AI-winners.
Analysts at firms including Goldman Sachs and Morgan Stanley believe "legacy" enterprise software could fall further (P/E of 12x–15x) if they cannot prove AI utility.
Based on those estimates, there is considerable danger of further downside.
Company | Est. Forward P/E (FY 2026) | Trend vs. 5-Year Average | Valuation Driver |
Microsoft | 22.4x – 23.2x | ~30% Compression | Balanced by Azure AI growth and office productivity dominance. |
Adobe | 23.5x – 25.0x | ~20% Compression | Premium maintained due to Firefly AI integration in Creative Cloud. |
Salesforce | 20.8x – 22.0x | Significant Reset | Shift toward margin expansion and buybacks over aggressive M&A. |
Oracle | 18.8x – 19.5x | Relatively Stable | Lifted by OCI (Oracle Cloud Infrastructure) demand for AI training. |
SAP | 21.0x – 22.5x | ~15% Compression | Resilient due to "sticky" ERP migration to S/4HANA Cloud. |
Workday | 43.1x – 43.5x | >50% Compression | Transitioning from high-growth SaaS to a mature HCM platform model. |
Analysts at J.P. Morgan and Bessemer note that legacy firms are now strictly valued on their ability to maintain a combined growth and profit margin of 40 percent (“the rule of 40”). Firms falling below this are seeing P/E multiples dip into the mid-teens.
Also, private consensus is that software multiples will not return to 2021 levels unless the 10-Year Treasury falls below three percent. As long as rates remain "higher for longer," analysts are modeling a permanent 20-percent haircut on terminal valuation multiples compared to the last decade.
source: MacroMicro
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