Mochi: which SaaS can't be killed by AI

The safest software businesses in an artificial intelligence boom are not the ones with the flashiest chatbot; they are the ones that own a company’s daily workflow. (youtube.com) The video at the center of this argument says software survives when it sits inside the work itself: approvals, records, handoffs, audits, and the systems that move data between teams. Bain made a similar point in a September 23, 2025 report, saying artificial intelligence is more likely to displace software in routine, rules-based tasks than in workflows with proprietary data, switching friction, and regulatory barriers. (youtube.com) (bain.com) That distinction is showing up in the market’s own language. Bain’s framework lists “proprietary data depth,” “switching and network friction,” and “regulatory or certification barriers” among the indicators that make a software workflow harder for an artificial intelligence agent to copy. (bain.com) In plain terms, a large language model can draft text or answer questions, but it does not automatically inherit the permissions, audit trails, exception handling, and cross-system links that companies use to run payroll, approve drug samples, or route a hospital referral. Microsoft’s guidance for enterprise agents says every agent must be observable, governed, and limited in what it can access, which turns “just add artificial intelligence” into an operations problem. (learn.microsoft.com) (bain.com) That is why the strongest “artificial intelligence-proof” examples tend to be vertical software companies, which sell into one industry and encode its rules. Veeva says its Vault CRM for life sciences includes hundreds of compliance features and supports region-specific business requirements, while Epic says its software is built as a single comprehensive health record that works across care settings. (veeva.com 1) (veeva.com 2) (epic.com) The same pattern appears in horizontal enterprise software when the product controls process, not just interface. ServiceNow says its Workflow Data Fabric connects data across systems, adds business context, and applies policy-based governance controls so artificial intelligence can take “trusted action,” and Workday says its orchestration tools trigger predefined actions across Workday and connected systems with real-time monitoring. (servicenow.com) (workday.com) This is also the part of software that is expensive to rip out. Veeva lists integrations with Microsoft Outlook, Microsoft Teams, SAP Concur, Salesforce Marketing Cloud, and other products, while ServiceNow markets native integration tools for end-to-end digital workflows across external apps and data sources. (crmhelp.veeva.com) (servicenow.com) The counterargument is that agents will flatten software into back-end application programming interfaces, with users talking to one assistant instead of logging into ten dashboards. Bain says that outcome is plausible for some categories, especially where tasks are repetitive, data is structured, and workflows are externally observable. (bain.com) Even in that scenario, the surviving vendors may still be the ones that own the system of record. If an agent needs approved content, customer permissions, medical compliance checks, or a patient’s current chart, it still has to call into the software that stores those facts and enforces the rules around them. (veeva.com) (epic.com) (servicenow.com) The practical lesson is narrower than “software is safe” and broader than “artificial intelligence kills software.” The products hardest to replace are the ones customers cannot easily unplug because they hold the workflow, the data, and the accountability all at once. (youtube.com) (bain.com)