While AI threatens many software companies, those built on strong network effects (like Slack) could become even more vital. AI agents will need to use these platforms as tools to perform tasks, solidifying their position as the central hub of work.

Beneath the surface, sales 'opportunities,' support 'tickets,' and dev 'issues' are all just forms of work management. The core insight is that a single, canonical knowledge graph representing 'work,' 'identity,' and 'parts' can unify these departmental silos, which first-generation SaaS never did.

A key sign of product-market fit in enterprise SaaS is when a product, initially adopted by one team, gets pulled into other departments organically. This internal virality, driven by demonstrated value, is a powerful growth engine and a clear PMF indicator.

According to Box CEO Aaron Levie, the stickiest SaaS products are those with strong network effects, deep integrations, and mission-critical workflows. A simple heuristic for vulnerability: if you can get the same value from a fresh install as a decade-old one, your product can be easily replaced by AI-generated software.

According to AWS's VP of Agentic AI, the primary struggle for enterprises is that critical context is siloed in 'walled gardens' like Outlook, Slack, and other SaaS tools. The most valuable function of AI agents is not just task automation, but their ability to work across these applications to gather and synthesize context, bridging the gaps.

A key competitive advantage wasn't just the user network, but the sophisticated internal tools built for the operations team. Investing early in a flexible, 'drag-and-drop' system for creating complex AI training tasks allowed them to pivot quickly and meet diverse client needs, a capability competitors lacked.

When product, CX, and engineering teams use the same tool to see user friction and deploy solutions, they move beyond departmental beliefs ("stated truths"). This forces collaboration based on shared, verifiable user behavior data ("observed truths"), breaking down organizational silos.

As AI makes it possible to replicate any SaaS application's features within days, the defensibility of a product no longer lies in its engineering complexity. The real, enduring moat is the network effect, which AI cannot trivially reproduce.

Platforms like ServiceNow dominate not because they are beloved, but because their initial flexibility allowed customers to build deep, custom workflows. This creates immense stickiness and high switching costs, making it difficult for users to leave even if they are unhappy with the product.