Impact of AI on the SaaS Industry

If I were running a legacy SaaS company today, I wouldn’t be sleeping much. For legacy SaaS startups, pivoting to an AI-native company is an existential challenge, testing the core of the Innovator's Dilemma. To their credit and courage, most SaaS CEOs are taking action, yet far too incremental, taking an "AI 1.0" approach by adding a copilot to their existing product. Real transformation lies in "AI 2.0"—reimagining the fundamental user interaction from the ground up. Why the alarm bells are ringing? * AI 1.0 ≠ transformation. Most SaaS incumbents bolt on a “copilot”. Nice demo, small impact. * AI 2.0 re-imagines the interface and workflow. Think GitHub Copilot vs Cursor: autocomplete add-on vs. full-stack code co-author that rewrites files, reasons across repos, and adapts to any model — developers feel the difference instantly. *The system-of-record moat is eroding. SaaS data model-based moat that created stickiness for the last two decades—is being replaced by conversational, intent and agentic based systems. Example:  CRM goes from a database to completing RFPs and follow-up emails. Why Legacy SaaS default to AI 1.0? - SaaS CEOs overestimate stickiness of the current UX and data model.  Customers will migrate. - Underestimate CIO/CTO AI mandates (new AI budgets are cannibalizing legacy line items). - Culture favors incremental roadmaps over zero-to-one bets. How Legacy SaaS can build for AI 2.0? 1. Redesign the interface. Start with the work-to-be-done, not the existing SaaS interface. 2. Build an orchestration layer for agentic workflows, tool calling, and human in the loop. Your current middleware gives a head start; extend it. 3. Staff for 0→1. Put founder-type product & engineering leaders, perhaps in an autonomous pod. Protect them from quarterly roadmap gravity. 4. Incentivize Customer Migration.  Ensure incentives of GTM teams are aligned to upgrading and moving existing customers over to the new platform.  Leadership test Ultimately, this is a test of leadership.  The SaaS CEOs and Founders who win will be those with the conviction to build for a new reality, even if it means disrupting their own successful products.

A new Fortune piece out with The Wharton School's Stefano Puntoni asks a simple question: is AI just improving #enterprise_software — or is it starting to \dismantle the business model that made SaaS so profitable in the first place? My view: the term “#SaaSpocalypse” may be exaggerated, but the underlying pressures are real. Based on two recent roundtables with senior business leaders in New York and San Francisco, we argue that three structural shifts are now converging. First, AI is exposing long-hidden market #vulnerability. For years, many enterprise software firms have enjoyed margins supported not only by product quality, but by #switching_costs, #customer_inertia, and the sheer #pain of migration. That is a powerful model — but also a fragile one. When customers remain because they feel trapped rather than delighted, #disruption becomes much easier to imagine. Second, #barriers_to_entry are falling fast. Building enterprise-grade software used to require major capital, large engineering teams, and long development cycles. AI coding tools are beginning to change that equation. This does not mean incumbents disappear overnight. But it does mean more entrants, more #experimentation, more credible #alternatives, and more pressure on the economics the sector has long taken for granted. Third, and perhaps most importantly, AI is changing what #customers actually #value. SaaS became powerful by #standardising_workflows across firms and sectors. But AI allows workflows to be #rethought from the ground up. That may shift advantage away from generic horizontal tools and toward context-rich, sector-specific #intelligence. In other words, deep #vertical_understanding may become more valuable than broad process standardisation alone. There is also a wider #ecosystem_story here. As copilots and agents begin to configure, run, and reshape workflows, the old division of labour starts to blur: software vendors, systems integrators, consultants, hyperscalers, and model providers are all moving into one another’s territory. The battle shifts to new control points — #orchestration, privileged #data_access, and distribution into day-to-day work. So no, I do not think enterprise software is disappearing. But I do think many of the assumptions that made it such an attractive and profitable sector are now under pressure. Link to a paywall-free version in comments. PS: More on ventures in this space am involved in to follow soon (shoutout, Evolver and Mario Schlener, Luis Vargas, PhD, Bas Kamphuis, Nicola Morini Bianzino); ditto for thoughts on how major SaaS incumbents should respond.

For decades, the promise of software was clear: automate, scale, and eliminate human-driven service interactions. But, AI is making software feel like a service again—and it's reshaping how we think about software metrics altogether. The SaaS model thrived precisely because it removed the human touch. It promised predictable margins through recurring subscriptions, standard pricing tiers, and minimal customization. But something vital was lost in the process - the adaptability, understanding, and intelligence that comes with human service. Now AI is bridging this gap, but in ways that fundamentally challenge how we think about software: - From rigid to adaptive: Traditional software follows predetermined paths. AI-powered software creates new ones based on your needs. - From reactive to proactive: Old software waits for commands. New software anticipates your next move. - From categorical to contextual: Legacy tools force you into their mental model. AI tools adapt to yours. The shift is profound. We're moving from selling access to functionality toward selling outcomes and intelligence delivered through software. Put simply, AI-driven software looks more like a highly scalable service business than traditional SaaS. Here’s what that means for the future: 1️⃣ Valuations become more nuanced: Investors must unpack revenue more carefully, distinguishing truly recurring streams from outcome-dependent and experimental revenues. 2️⃣ New metrics take center stage: Traditional KPIs like ARR now coexist with new measures like "customer outcomes," "value realization rates," and "repeat success metrics." 3️⃣ Greater attention to volatility: Companies and investors alike must scrutinize revenue sources closely, understanding variability, concentration risk, and seasonal shifts. 4️⃣ Operational discipline reigns supreme: Success increasingly hinges on the consistent ability to manage complexity, variability, and customer expectations—no shortcuts, just execution. The era of straightforward, predictable SaaS is evolving into a richer, more complex AI-driven services landscape. Welcome back, truly, to software as a service 🤖

GenAI is the biggest swing factor in SaaS valuations today—doubling multiples for some, leaving others unchanged. Here’s where things stand: 1. SaaS baseline vs. GenAI uplift Most public SaaS names trade around 9× trailing revenue. But companies with a credible GenAI story are seeing multiples in the 17–28× range: • CrowdStrike trades at 28×, with AI powering threat detection and automation. • Snowflake and ServiceNow hover near 17–18×, positioning AI as central to their platform strategy. • Adobe, despite heavy investment in generative tools, has dropped closer to 7× following cautious signals on monetization. The median for AI-forward software companies is around 17×, nearly double the broader SaaS average. 2. Private AI startups are even more aggressively valued Recent deals in the GenAI space are pricing at 23–26× revenue, well above the private SaaS norm of 7–9×. This reflects investor belief in future expansion, even when current usage or monetization is early. 3. Why GenAI adds 8–10 turns The valuation premium isn’t just buzz—it’s grounded in investor conviction around: • Revenue acceleration through new SKUs, pricing power, and AI-led land-and-expand • TAM expansion, transforming point products into full platforms • Scarcity premium, with few scaled GenAI-native players in the market • Margin tailwinds, based on improving inference efficiency and pricing dynamics 4. But the premium is fragile Without clear, monetized AI traction, the multiple deflates quickly. Adobe’s recent dip is a case in point—investors want results, not just vision. In categories like cybersecurity, we’re already seeing a sharp divergence in multiples: those with visible GenAI differentiation are trading 4–5× higher than peers still early in their AI journey. 5. What to watch next • GenAI-specific revenue reporting: More companies will need to show AI’s direct business impact. • Inference cost curves: If infrastructure costs don’t drop fast enough, margin expansion assumptions will need to be revisited. • Platform consolidation: The long-term winners will become the embedded AI layer for enterprise workflows, agents, and copilots—not just feature vendors. Bottom line: GenAI is adding 8–10 full turns to SaaS valuations, but that uplift is fragile. Investors are no longer rewarding potential—they’re rewarding proof.

AI has flipped the SaaS business model on its head. Because what costs nothing to build costs everything to integrate. Last weekend, I built a functional version of a Series B company's core product using AI tools. The build took 14 hours. The integration costs? Still ongoing after two weeks. This isn't unusual anymore. AI has dramatically compressed product development timelines while the complexity of fitting new tools into existing tech stacks remains unchanged. The implications for SaaS cannot be understated. When I examined where my weekend project hit roadblocks, it wasn't in creating features. It was in designing APIs, building connectors, ensuring compliance, and developing migration paths. The nature of value creation has shifted dramatically and few realize it. The majority of development hours weren't spent building core functionality, but rather on making it play nicely with everything else. This pattern repeats is a well-known problem already across the industry. Companies are discovering they can build sophisticated products and are fit for purpose, only to face the unchanged reality of enterprise integration challenges. Now this well known problem is taking a different face. The emerging reality: 1️⃣ Products that were once differentiators are becoming commodities 2️⃣ Integration capabilities now determine competitive advantage 3️⃣ Customer success teams matter more than development teams 4️⃣ Professional services revenue grows while license revenue shrinks As AI commoditizes building, integration becomes the new competitive moat. For founders, this means rethinking resource allocation. When product development costs approach zero, the relative value of integration expertise approaches infinity. The most successful SaaS companies of the coming era won't necessarily have the best products. They'll have the most seamless integrations. Technology value is fundamentally about what it connects, not what it contains. #startups #founders #growth #ai

We’re watching the rapid transformation - and possible end - of SaaS as we know it. Microsoft CEO Satya Nadella recently pointed out that traditional SaaS is disappearing, and I strongly agree. But I see the timeline accelerating even faster: Phase 1 (Right now): AI as Support AI enhancements like Copilot, Gamma, and Harvey are currently complementing existing SaaS platforms, making them seem more efficient and attractive. Providers feel secure, viewing AI as a feature rather than a threat. Phase 2 (Within 6-12 months): AI Takes Over Operations AI agents will quickly transition from assistants to autonomous operators. Instead of manually using tools like Tableau or Meta’s ad platform, we’ll simply instruct agents to perform analyses or optimize ads directly. The expertise traditionally embedded in SaaS interfaces becomes easily accessible through agents. Phase 3 (Within 1-2 years): Software Becomes Invisible AI agents begin interacting directly via APIs, eliminating the need for human-oriented interfaces like dashboards and menus entirely. This strips away the core value SaaS once provided—human usability. This isn’t standard disruption; it’s a fundamental shift away from human-operated software to agent-operated software. At the same time, the rise of AI-driven coding tools makes custom internal software development dramatically easier and cheaper. Companies no longer need to rely on costly SaaS subscriptions—they can quickly create tailored internal applications that perfectly fit their needs. The winners in this new era won’t simply be those who integrate AI the quickest. Instead, they’ll be companies providing open, agent-friendly APIs, becoming the trusted providers of actionable data and execution within their fields. The real question is whether giants of all industries will swiftly adapt or risk becoming obsolete, much like tech giants of the past. We’re entering an extraordinary period of opportunity for agile startups ready to embrace this change.

SaaS isn’t slowing down. It’s getting eaten alive ... cannibalized. Look at the Aventis Advisors growth chart. The trend isn’t subtle, it’s a cliff. We went from 36 percent growth to 12 percent. Almost a decade of down and to the right. With forecasts pointing to 11 percent … and falling, this feels like a pretty big structural shift. SaaS is starting to look like utilities and pipelines, durable and necessary, but no longer where the real upside lives. And the reason is pretty simple. AI is cannibalizing the very work SaaS used to monetize. Here’s what the chart doesn’t show, but every operator feels. 1) SaaS used to sell “workflows.” AI sells “outcomes” (OaaS). Agents do the work inside the tool, so the tool stops being the product. The labor becomes the product. 2) Budgets (and investors) are leaving SaaS and flowing to digital labor. CFOs aren’t buying more seats. They’re buying fewer humans. AI fits. SaaS doesn’t. 3) Feature parity killed differentiation. Entire categories are indistinguishable. CRM, CX, marketing automation … all the same. AI exposes how thin the moats always were. 4) Enterprises hit peak-SaaS years ago. Now they’re consolidating and cutting 20 to 40 percent of their stack. AI accelerates that purge. 5) AI startups are growing at speeds SaaS can’t touch. When companies hit nine figures in months, not years, investor expectations reset. SaaS looks slow, expensive, and operationally bloated. 6) Value is moving down the stack. The action is in compute, data, agents, and orchestration. SaaS is becoming a UI layer that AI sits on, not the engine driving the work. The growth-rate collapse isn’t a mystery, it’s more of a transfer of value. SaaS is maturing into a stable, cash-flow asset class. AI is becoming the new growth engine of the enterprise. That means founders have a choice, build SaaS and optimize it like infrastructure, or build AI agents that replace the workflows SaaS was built to capture. One path gives you stable multiples, the other gives you growth.

𝐒𝐚𝐚𝐒 𝐚𝐬 𝐰𝐞 𝐤𝐧𝐨𝐰 𝐢𝐭 𝐢𝐬 𝐝𝐢𝐬𝐚𝐩𝐩𝐞𝐚𝐫𝐢𝐧𝐠 In his conversation with Sam Altman and Brad Gerstner, Satya Nadella explained that traditional SaaS apps — built around static logic layers and human users — are being replaced by AI agents that perform the same workflows autonomously. Instead of humans clicking through a CRM, ERP, or project management tool, agents will sit on top of the data, understand the context, and take action. Nadella described it as a structural shift: The old SaaS stack (data + logic + UI) was tightly coupled. The new stack separates the AI logic layer from the interface, turning agents into the new users. Usage patterns flip — from “per seat” pricing to “per agent” consumption. He added that in Microsoft’s products — from GitHub Copilot to Microsoft 365 Copilot — usage and data creation have surged. The more AI is integrated, the more data is generated, which in turn powers better grounding for future models. In this new model, agents become the interface, and data becomes the product. SaaS isn’t ending — it’s evolving into something more autonomous, contextual, and continuous.

“Will AI kill SaaS?” The recent SaaS stock selloff seems to suggest that AI will replace SaaS/vertical SaaS altogether. I think that’s far from reality.. AI is a utility, infrastructure service. This is how we need to view it. AI replacing vertical SaaS is like saying electricity will directly run hotels, airlines, or factories. Infrastructure enables value — it doesn’t deliver it. About 10–15 years ago, there was a similar belief: cloud infrastructure would take over end applications. The thinking was simple (and wrong): If AWS, Azure, and GCP provide compute, storage, and data, why wouldn’t they just move up the stack and replace enterprise software? What actually happened: Cloud became foundational infrastructure,SaaS exploded on top of it Vertical software companies became more valuable, not less because infrastructure doesn’t equal outcomes. AI is following the same path. • AI models don’t understand industries on their own. Vertical SaaS exists because deep domain context matters — travel, hospitality, pricing, distribution, regulation, seasonality. Models don’t magically infer that. • Switching costs are real. SaaS lives inside complex, integrated system landscapes. Replacing systems of record and workflow is far harder than generating insights or content. • In travel & hospitality, I’d argue less than 10% of existing technology capability is actually adopted today. The bottleneck isn’t intelligence — it’s adoption, change management, and expertise. Where AI will have massive impact: AI will supercharge SaaS companies, not replace them: • Faster product innovation • Higher sales and support productivity • Lower cost-to-serve • Easier adoption of complex software At RateGain, we’re embedding AI directly into pricing, distribution, marketing, and decision workflows — not as a feature, but as a way to help customers extract real value from systems they already rely on. AI is not the product. AI is the accelerator. The real divide won’t be AI vs SaaS. It will be vertical SaaS companies that deeply embed AI into real industry problems.

I recently spoke with the The Australian Financial Review's Paul Smith about what some are calling a “SaaSpocalypse”. I see something different: The market is moving away from software that simply digitises workflows and charges per seat, toward platforms that deliver the actual outcome a business wants and embed intelligence at scale. AI is accelerating that shift. When features can be replicated quickly and marginal costs fall toward zero, the defensibility of traditional SaaS weakens. The question every software company now faces is simple: what’s your moat if AI can reproduce your product quickly and cheaply?? For us at Employment Hero, it's simple: We have spent 15 years building the underlying infrastructure that powers employment across payroll, compliance, awards, workforce data, and multi jurisdiction regulation. More than 350,000 businesses run their people through our platform. That creates network effects, proprietary data, and insight that AI alone cannot replicate. The next decade will reward companies that combine AI with real employment infrastructure and measurable economic outcomes. https://lnkd.in/gcwFFBhC