We’re convincedthe biggest beneficiaries of AI will be enterprise software companies. To understand exactly how AI is reshapingenterprise SaaS, we reviewed the earnings calls of 45 publicly traded SaaScompanies: Snowflake, Salesforce, MongoDB, ServiceTitan, ServiceNow, Microsoft,AppFolio, Palantir, Atlassian, Paylocity, Doximity, Qualys, Bill.com, ZoomInfo,Dynatrace, Monday.com, Blackbaud, Cloudflare, Freshworks, Klaviyo, Datadog, Q2Holdings, Shopify, HubSpot, Paycom, Unity, Twilio, Procore, JFrog, SPSCommerce, Waystar, SimilarWeb, Amplitude, Figma, Weave, RingCentral, Workiva,Five9, Alarm.com, Appian, HealthStream, Backblaze, Ziff Davis, Workday, andZeta.
What follows isa summary of what management teams are actually saying about AI's impact ontheir businesses, their customers, and their competitive positioning. We'llrelease additional installments as our work continues (We have 81 earningsreports to get through).
We believe thisis the most comprehensive analysis of enterprise software earnings callspublished this cycle and what we’re learning cuts against the grain: enterprisesoftware is going to be the biggest beneficiary of the move to agentic AI. Models are commoditized infrastructure, similarto cloud compute. The differentiation forenterprise AI comes from the proprietary data, workflows, and operating systemsthat software provides.
Big take-awaysare below:
AI is notprofitable yet. The goal for themoment is driving margin neutral revenue. Microsoft, Salesforce, ServiceNow and nearly every other company describedmargin pressures from deploying AI product. AI workloads are just very expensive at the moment. They’re also complex; Appian reported a significantjump in services revenue because these AI deployments aren’t just co-pilots; serious production grade deployments at theenterprise level require deep integration work, data governance, and processdesign.
Directrevenue from AI is nascent for most, especially relative to total revenue. And is non-existent for some players likeDoximity, or still in its very early stages but growing fast like at Freshworks. That said, enterprise customers are adoptingand benefiting from the new AI products built by their already critical softwareproviders like Datadog, Salesforce, and Monday.com. There is also a category of companies thatare building usage before monetizing, like Figma. No company we’ve observed is making AI producta significant part of any forecast, yet, even though they talk extensivelyabout positive AI impact. The softwarecompanies are being very conservative in forecasting, to their detriment in themarkets, but it’s because the sales cycle is different, pricing is new, deal sizesare bigger, and implementation timelines are unknown. Both customers and software vendors arefiguring out this new AI thing together.
The AIrevenue coming out of legacy SaaS co’s is impressive. Some of the AI revenue looks nascent onlybecause the existing SaaS businesses that are generating that AI revenue are huge. For instance, ARR at Workday is $8.8bln, ofwhich $400mm is from agentic product. That’s only 4% of revenue from AI, but if Workday’s AI product was astandalone business, it would look large and incredibly fast growing. Similarly ServiceNow claims $600mm of agenticrevenue, Salesforce claims $169mm (with 800% YOYG), and Ringcentral claims$100mm. Again these are small numbersrelative to total revenue at each of these companies, but it shows thatexisting SaaS companies may be the ones best positioned to scale AI productsgiven their customer base and sales and customer service infrastructure.
Monetizationis changing. The pricingconversation for AI is largely going from "per seat" to "peroutcome" or "per agent deployed.” Procore for instance wants to price it’s AI on construction dollar volumes,which would insulate it from employee count reductions. Some like Atlassian aresticking hard to the per seat model.
Infrastructurefor AI versus AI products. Some likeSnowflake, ServiceNow, and MongoDB, Twilio, Datadog, and Cloudlfare will win becauseAI is supported infrastructurally on their platforms. Some like Dynatrace believe AI makes their productmore compelling than ever (in their case it’s observability and monitoring of AI). Others like ServiceTitan and Salesforce arebuilding AI product which actually executes context-aware actions. AI asan assistant or Copilot has been de-emphasized.
Trust is anissue for AI. Doximity has stoppedreleasing AI product until they get it perfect, because it’s not ok for AI to mis-diagnosea patient. Qualys makes a similar pointin cybersecurity; being the agentic remediation layer requires a level of trustthat generic AI tools can't establish. AI errors in healthcare and cybersecurity are near unacceptable, andthus the bar for accuracy is higher.
Performanceis strong. Quite a few of these companieslike MongoDB, Cloudflare, and ServiceNow, and Datadog closed some of largestdeals ever in Q4. Others like Atlassianhad record quarters. Zeta beat guidance for an 18th consecutivequarter. Companies that are selling AIproducts to their customers report better growth and retention among thosecustomer cohorts. On the other hand, there are companies likeZoominfo and Ziff Davis experiencing serious disruption, with growth falling tonear zero.
The moat isvery high. Moats for enterprise softwareexist around proprietary non-public and sensitive historical customer data,workflows, governance, security, integrations, compliance, vendor trustespecially in highly regulated industries like healthcare (Waystar and Healthstreamcited this), and operational knowledge of the existing customer. AI cannot standalone, but rather needs to siton top of software which manages all the above in an enterprise-friendlymanner. Additionally, any of thesesoftware companies building agents have serios edge, because those agents aretrained on enormous repositories of historical customer data that an AI startupwill not have. Additionally, hyperscalerscharge significant egress fees to move data. All that said, SaaS companies focused on SMB customers, which have muchless internal complexity, could face a serious threat from AI that allowscustomers to do internal builds or AI startups; Monday.com and Zoominfo citedissues in their SMB customer bases. SMBs have simpler workflows, lessinstitutional complexity, and less switching cost.
Sitting nearthe data is especially advantageous. Anumber of companies like Five9 and Appian report their customers have to solvedata hygiene and silo issues before AI can be deployed effectively. This is a pre-requisite, so if you’re asoftware company sitting near or on top of the data already, you’ve got edgeover an upstart. Additionally SaaScompanies with proprietary data sets like Zeta, Waystar, and SPS Commerce willsee the value of those data sets increase as AI agents and tools scale intothat data.
Internal AIimproves overall margins. Many ofthe companies themselves such as Blackbaud and Klaviyo are using AI in theirown operations to improve margins and productivity. For instance, Workday’s engineering outputgrew 22% in the last six months measured by code delivered. Figma built and shipped Sana (Workday's AIlayer) from project start to GA in three months. HubSpot had 97% of codecommits using AI assistance. JFrog accelerated key API development 30x. The value of AI to SaaS is not just a revenuestory but also a margin expansion and product velocity story.
More AI willincrease the need for existing software. AI needs software to operate more efficiently. As the Appian CEO put it, “AI without workflowsis chaos.” For companies that sit on the measurement, observability, andanalytics like Amplitude, Datadog, Dynatrace, and Snowflake, AI-drivendevelopment increases demand for incumbent software. AI agents are also becoming a new customer oruser. The consumer of enterprise dataplatforms is no longer just a human analyst or developer, it's an AI agentquerying autonomously, continuously, at scale. That consumption will become monetization for software companies. Even the foundational AI companies themselvesare customers: Datadog has 14 of the top20 AI-native companies as customers. Amplitude has 25 AI-native customers above$100K ARR with one frontier lab at seven figures.
AI is allowingsoftware companies to really show their ROI. AI is allowing software companies new ways to show ROI. Examples include Waystar ($15B in preventeddenials), ServiceTitan (18-point EBITDA margin improvement for Max customers),Klaviyo (50% higher open rates, 40% higher revenue per campaign), HubSpot (2xmeetings booked for Customer Agent users). They are using those outcomes to justify both higher prices and fasterexpansion within accounts. Missioncritical and financially consequential workflows to the customer are seeingreal agentic adoption from software companies.
Build vsBuy. Customers are concludingquickly they don’t want to build AI internally, especially those in regulated industries. RingCentral notes the engineering talent and customercompliance issues make building AI in-house unattractive; why not trust anexisting software vendor? Waystar noteda similar dynamic. Customers are notbuilding AI point solutions, rather they’re turning to their existing software vendors.
The legacybusiness is funding the excitement. Quitea few of the publics have a dynamic whereby their legacy business which is slowgrowth but generating free cash flow is funding an exciting but nascent AIplatform or product. Dropbox,RingCentral, SPS Commerce all fit into this. These companies are re-inventing themselves with AI, not being destroyedby it. That transition may take time, butif their theses are right, it has tremendous upside for the patient investor.
Revenuemultiples in real time can be seen for all these companies at https://www.softwaremultiples.com/. Also visit https://www.blossomstreetventures.com/for detailed financials and metrics data for all these companies. If you would like summaries of the actual earningscalls so you can run your own analysis, please email me.
Thank youfor your readership. Email the author directlyat sammy@blossomstreetventures.com