{"id":54016,"date":"2026-04-07T15:41:12","date_gmt":"2026-04-07T13:41:12","guid":{"rendered":"https:\/\/www.elaine.io\/?p=54016"},"modified":"2026-04-13T10:33:27","modified_gmt":"2026-04-13T08:33:27","slug":"saas-is-dead-or-is-it","status":"publish","type":"post","link":"https:\/\/www.elaine.io\/en\/blog\/saas-is-dead-or-is-it\/","title":{"rendered":"SaaS is dead. Or is it? Why AI is rewriting the rules of enterprise software"},"content":{"rendered":"

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By Stefan von Lieven<\/em><\/p>\n

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As a founder and CEO in email marketing for more than 20 years \u2013 arguably the channel most often declared \u201cdead\u201d in the history of digital business \u2013 I\u2019ve learned to stay remarkably calm whenever someone announces that \u201cXYZ is dead.\u201d So now SaaS is dead?\u00a0Not quite.\u00a0 What is different this time is not that SaaS has suddenly become irrelevant. It is that artificial intelligence is fundamentally reshaping how software is built, deployed, and consumed. This shift is not about the end of SaaS. It is about the end of SaaS as we know it.<\/strong><\/p>\n

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Expectations vs. reality: the bottleneck is often organizational<\/strong><\/h2>\n

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We are currently deep in \u2013 or perhaps already slightly beyond \u2013 what Gartner would describe as the peak of inflated expectations in the AI hype cycle. The pace of innovation is staggering. Every weekend seems to bring a new wave of prototypes, demos, and \u201clook what I built with AI\u201d moments that point to a radically different future. Inside enterprises, however, the reality remains far more constrained.<\/p>\n

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Most organizations are still struggling to move even a single AI use case from pilot to production. The gap between experimentation and enterprise-grade deployment \u2013 secure, scalable, and compliant \u2013 remains significant. While a small number of companies are already operating at an advanced level, many others have barely progressed beyond AI-powered search.<\/p>\n

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The difference is not access to technology. It is organizational readiness. AI does not eliminate technical debt, repair weak data foundations, or break down siloed operating models. If anything, it amplifies them. Organizations with clean data, modular architectures, and cross-functional alignment move faster. Those without these fundamentals often find themselves stuck in perpetual pilot mode \u2013 and in weekly philosophical compliance meetings.<\/p>\n

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This helps explain why, despite AI\u2019s transformative potential, the primary enterprise focus today is not reinvention but efficiency. Gartner data shows that nearly half of organizations prioritize time savings (49%), followed closely by cost reduction (40%), while only a minority \u2013 just 19% \u2013focus on advancing personalization. In other words, AI is currently being used to optimize existing processes rather than create entirely new ones.<\/p>\n

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This gap is also reflected in the striking disconnect between expectations and concrete plans: 79% of executives believe AI will significantly contribute to their revenue by 2030, yet only 24% can clearly articulate where that revenue will actually come from, according to Gartner.<\/p>\n

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For CIOs and CMOs, this is both rational and limiting. In a macroeconomic environment defined by cost pressure and constrained resources, freeing up time and capacity is the immediate priority. But it also postpones the deeper transformation: rethinking business models, customer engagement, and value creation through AI.<\/p>\n

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At the same time, another shift is underway \u2013 one that directly challenges the traditional SaaS model. For years, enterprises have accumulated sprawling martech stacks, a phenomenon well documented in the Martech Landscape by Scott Brinker and Frans Riemersma, which has grown from a few hundred solutions to many thousands. Yet scale has not delivered simplicity. Instead, organizations are left with underutilized tools, fragile integrations, and workflows that rarely function as seamlessly as promised.<\/p>\n

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AI is changing that equation. It lowers the cost and complexity of building, integrating, and automating software. Tasks that once required dedicated vendors \u2013 custom workflows, data transformations, orchestration layers \u2013 can increasingly be handled in-house with AI-assisted development. What some call \u201cvibe coding\u201d is not just a passing trend; it signals that the boundaries between buying and building software are beginning to collapse.<\/p>\n

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As a result, enterprises are shifting from consuming software to composing capabilities. Gartner predicts that 80% of organizations will adopt composable architectures, while only 20% will continue to rely primarily on monolithic suite approaches. This transition reflects a broader change in mindset: away from predefined toolsets and toward flexible, business-tailored, outcome-driven systems.<\/p>\n<\/div>\n

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Pressure on SaaS: value shifts toward smartly connected systems<\/strong><\/h2>\n

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This is where the real pressure on SaaS companies begins. It is not simply a story of AI disruption. It is a story of enterprise maturity. Organizations are no longer willing to pay for bloated feature sets they do not use, nor to accept limited interoperability dressed up as seamless integration in marketing copy. AI gives them the tools to push back \u2013 to build what they need, integrate what they choose, and reduce vendor lock-in.<\/p>\n

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For vendors, this creates a new reality. Software that does not offer deep domain expertise will struggle to stay relevant. Commodity functionality can increasingly be replicated or orchestrated through AI. Even products repositioned as \u201cagentic\u201d solutions will fall short if they lack the contextual understanding needed to deliver meaningful outcomes.<\/p>\n

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What is emerging instead is a new model of enterprise application architecture \u2013 one that is inherently hybrid. At its core sits a deterministic layer: systems of record and control where rules are explicit, processes are auditable, and data integrity is guaranteed. Surrounding that core is an agentic layer, where AI operates across systems, orchestrating workflows, making decisions, and continuously optimizing outcomes.<\/p>\n

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In this model, individual tools fade into the background. They are no longer endpoints but capabilities \u2013 modular components that can be invoked, combined, and evaluated based on the results they deliver. The focus shifts from feature sets to outcomes, and from ownership to orchestration.<\/p>\n

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For CMOs, this means marketing execution becomes increasingly autonomous. Campaigns are no longer static constructs but dynamic systems that adapt in real time. Personalization evolves beyond segmentation into continuous decision-making at the individual level. For CIOs, the implications are equally profound. Architecture becomes a strategic lever, data governance a critical foundation, and vendor strategy a question of openness versus control.<\/p>\n

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This tension between open ecosystems and closed suites will define the next phase of enterprise software. Established suite vendors will try to reinvent themselves as AI platforms, often by layering \u201cagentic\u201d capabilities onto existing architectures. At the same time, many will seek to retain control \u2013 locking in data, monetizing access, and preserving their ecosystems. In contrast, a new generation of solutions is emerging that embraces composability, interoperability, and openness as core principles.<\/p>\n

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SaaS is not disappearing. But it is no longer the center of gravity. AI is shifting value away from standalone applications and toward integrated, intelligent systems.<\/p>\n

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The age of open ecosystems has begun \u2013 a principle that sits at the very heart of Entirely<\/strong><\/h2>\n

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For enterprise leaders, the strategic question is no longer which tools to buy. It is which capabilities to build, how to orchestrate them, and how to evolve them over time. The organizations that succeed will not be those that simply adopt AI to do the same things faster. They will be the ones that use it to do entirely new things.<\/p>\n

AI does not kill SaaS. It redefines it.<\/p>\n<\/div>\n

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