Points and Platforms

Feature-Oriented Platforms

In ServiceNow’s Q4 FY2025 Earnings Call, CEO Bill McDermott remarked,

Many people ask why our valuation has not kept pace with our results. The short answer is that we have been viewed as a feature-oriented SaaS company. We are not living in a SaaS neighborhood. We are a platform company, executing a long-term platform strategy where AI agents and workflows are harmonious and synonymous, creating sustained advantage, not short-term wins.

The term “feature-oriented SaaS company” can probably describe most software companies. Historically, even companies that ultimately became platforms often won customers through a flagship product that offered a best-in-breed solution for a single high-value workflow. Salesforce is perhaps the most classic example of this. Salesforce landed customers through its CRM product, Sales Cloud, and then expanded across adjacent clouds and capabilities. Even ServiceNow has traditionally followed a similar sales motion. Many customers first adopted ServiceNow through its ITSM software and then expanded usage to other workflows across IT, employee, and customer operations. You’ll notice that in both cases, Salesforce and ServiceNow initially won customers because they offered a single best-in-class point solution that delivered clear, immediate value. Importantly, customers did not buy Salesforce or ServiceNow just because they were a platform. In that way, both Salesforce and ServiceNow were fundamentally feature- and product-led rather than platform-oriented.

That’s not to say platforms, despite being product-led, don’t have real structural advantages over point solutions. Take Zoom, a point solution. Most people think of Zoom today as a low-growth company; after revenue surged more than 300% during COVID, growth has cooled to around ~3% YoY, largely reflecting the normalization of work patterns. But Zoom has another indicator of slowing momentum that isn’t fully explained by the shift back in person: net dollar retention.

According to Zoom’s Q3 FY2026 Earnings Deck, Zoom has a TTM NDR of 98% for its enterprise customers, meaning the average enterprise cohort is shrinking year over year. While it’s understandable that the drop in demand has led to slower revenue growth, it’s less convincing that the shift back to in-person is solely responsible for the lackluster retention rate. Even in a post-COVID environment, organizations still rely on video conferencing for core functions such as cross-office collaboration, customer calls, recruiting, and meetings with external partners. Therefore, since video conferencing remains a durable, must-have utility, you’d expect existing customers to at least hold steady even if growth has stalled.

The lackluster net retention rate is probably best explained by competitive pressure from Microsoft Teams and Google Meet. Even if, in my experience, Teams and Meet are meaningfully worse products on pure video quality and usability, large enterprises don’t buy video conferencing in a vacuum. Because Teams is bundled into Microsoft 365 and Meet is bundled into Google Workspace, Microsoft and Google can sell them at a discount. Therefore, both products only have to be “good enough” to convince procurement teams to standardize and cut a standalone Zoom contract, both saving money and reducing vendor sprawl. In this environment, Zoom can only keep a foothold for premium use cases, but it’s harder to expand within existing accounts. And some customers will rationalize seats or switch entirely simply because the all-in economics favor the bundle, ultimately leading to Zoom’s low NDR.

Software Platforms and AI

Bundling and the pricing leverage it creates are still relevant for software platforms in the AI era, but that isn’t the “sustained advantage” Bill McDermott emphasized on ServiceNow’s earnings call. Instead, he argues the durable edge comes from being a platform where “AI agents and workflows are harmonious and synonymous.” Put simply, McDermott’s claim is that the strongest position in enterprise software is to be the system that hosts AI tools and embeds them directly into core workflows. Of course, that conclusion itself seems quite obvious. If AI is truly transformative for enterprise tech, naturally, you would want to be the platform where AI runs and work gets executed. But why is McDermott so confident that ServiceNow is best positioned to win that role? Or, more generally, what allows the enterprise software platforms of today to defend their positions in a post-AI world?

The overarching advantage software platforms have is that they’re already deeply embedded in some of their customers’ most critical workflows. Take ServiceNow for example. Because its product suite spans incidents, requests, assets, and other core IT processes, a large share of an organization’s day-to-day IT work flows through the platform. This gives ServiceNow a unified and coherent view of how a customer’s IT operations actually run, something an AI-native startup or a legacy point solution typically can’t replicate without years of deployment depth and integration.

This visibility gives legacy platforms a major advantage: data. Because platforms like ServiceNow and Salesforce have a wholistic view on core workflows, over time, they have accumulated a depth of workflow data that AI-native startups and standalone point solutions can’t match. With that data, platforms are often better positioned than AI-native startups and point solutions to train and refine agents that behave reliably inside real enterprise processes.

Ironically, one of the most explicit examples of this platform advantage comes not from an incumbent, but from a late-stage startup: Rippling. In Rippling’s 2024 Investor Memo, CEO Parker Conrad claimed that,

The corollary opportunity for Rippling, then, is to rebuild business software across each software vertical, but to embed an understanding of your company’s employees in the foundations and tissue of each of these products.

Rippling’s core thesis is that its products are built around a shared layer of employee data, namely Rippling’s Employee Graph. Therefore, every application in the suite benefits from the same unified system of record. That gives Rippling an advantage over point solutions that only see a narrow slice of the customer and thus cannot build the same cross-functional context into their products. Just as importantly, Rippling’s point-solution competitors struggle to justify comparable investments in their data infrastructure. For Rippling, that infrastructure is amortized across the entire suite, whereas for a single-product vendor, that same infrastructure can only improve one workflow, making the ROI on building a similarly deep data layer far less attractive.

This advantage arguably applies even more strongly against AI-native point-solution startups. Rippling can leverage the proprietary data from its Employee Graph to train and improve its agentic AI features, because those agents operate inside a system that already knows who employees are, what roles they have, what they’re allowed to do, and how workflows connect across functions. By contrast, AI-native point solutions typically have thinner access to employee and workflow data and even less incentive to build a deep, unified data layer if it only improves a single product. The result is that their agents often have less context, weaker permissions awareness, and fewer reliable integration points, which can translate into a less capable enterprise agent.

You might notice there is another glaring advantage to being a platform that I have yet to mention: the switching costs platforms impose on their customers. And in an AI-driven market, that advantage is hard to overstate. A standalone point solution is relatively easy to replace. You can easily pivot to an AI-native alternative in one team, migrate a narrow dataset, retrain users, and swap one workflow with limited disruption. But ripping out a platform is much more difficult. Let us return to the example of ServiceNow. ServiceNow’s platform is woven into the fabric of their customers’ IT operations. Replacing that entire system means rebuilding integrations across dozens of systems, migrating years of historical data, and retraining multiple teams that depend on it. That depth of entrenchment dramatically raises the bar for any AI-native challenger hoping to displace the platform end-to-end. As a result, if platforms like ServiceNow, Salesforce, and Rippling can embed AI into workflows quickly and credibly, it becomes much harder for new point solutions to gain a foothold, and far less likely that enterprises will rip and replace a system that already sits at the center of their core business workflows.

Notice that all of the advantages above stem from the platform itself rather than any single product. The ability to act as a system of record and leverage workflow data across modules is what a software company actually needs to survive and thrive in a post-AI world. And those advantages can only accrue to companies that operate as true platforms, not standalone point solutions. Fundamentally, this is what Bill McDermott means when he says that ServiceNow’s advantage comes from being a “platform company.”

AI-Native Startups

Of course, it’s important not to ignore the flip side of this dynamic. Large software platforms are often slow to pivot to new business models. They are constrained by massive installed bases, legacy architectures, and the need to maintain reliability, security, and governance across thousands of enterprise deployments. That friction creates an opening for more agile, AI-native startups that are able to iterate faster and ship new workflows quickly to capture value before incumbents fully adapt.

We saw a similar dynamic during the early 2010s as software companies rushed towards the cloud. As Parker Conrad pointed out:

Because [business software vendors like SAP, Oracle, and Microsoft] were poorly positioned to rebuild their technology as cloud software, the shift to the cloud created a moment-in-time opportunity for focused competitors to peel off specific features and products from these mega-vendors and turn them into standalone, point-SaaS companies.

With AI, there is once again a real “moment-in-time” opening for AI-native startups to ship standalone products that can compete with established platforms by simply being better at the AI layer. And it’s hard to deny that some of these companies are breaking out. Serval, for example, is positioning itself as an AI-native IT service management platform and has landed credible early customers such as Perplexity AI, Clay, Mercor, and Notion. It raised a $75M Series B led by Sequoia Capital at a reported $1B valuation, representing over 110x Serval’s ARR on 500% revenue growth since August. This signals of both rapid adoption and investor conviction even while it competes in territory long dominated by ServiceNow.

While AI-native players like Serval are outpacing incumbents in building the agentic layer, incumbent platforms like ServiceNow and Salesforce still own two assets that are nearly impossible to replicate: the data layer, namely the system-of-record visibility into real workflows, and the distribution channel. In that world, the most important thing for platforms to emphasize isn’t any single feature. Instead, it’s having the strongest data stack and workflow visibility, because that’s what lets software companies seamlessly integrate any agentic solution they build or acquire. Then the incumbent’s existing sales engine can immediately drive cross-sell and upsell into its installed base of enterprise customers. Thus, in the AI era, the defensible advantage shifts from the product to the platform itself.

Back in 2011, Marc Andreessen said, “Software is eating the world.” And now, almost 15 years later, enterprise software is still not full; it’s just moving to a new course: AI.