Software-as-a-Service (SaaS) in Healthcare and Life Sciences: not dead, but disabled
Since the launch of Claude Cowork, narratives suggesting that Artificial Intelligence (AI) will replace Software-as-a-Service (SaaS) platforms have gained significant momentum. We previously referred to this trend as the SaaSocalypse.
With the rise in vibe-coding and the rapidly advancing developer tools, many assume enterprises will increasingly build their own software, reducing dependence on SaaS providers. This sentiment has already been reflected in market behavior, including valuation corrections among leading SaaS companies such as Salesforce and Veeva.
However, although the term SaaSocalypse is compelling, it overlooks how SaaS platforms actually operate within Healthcare and Life Sciences (HLS) enterprises.
The real question is not whether SaaS is disappearing, but how significantly AI will disrupt the market. The answer lies in understanding the gap between how equity markets evaluate SaaS and how these platforms function inside enterprise environments.
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Why SaaS appears disrupted from an equity lens
Historically, SaaS valuations have been driven by expectations around continuous feature innovations, expanding user adoption, geographic growth, and increasing per-user pricing. This model assumes providers control innovation cycles and steadily deliver incremental enterprise value.
AI is disrupting that assumption. HLS enterprises can now build features faster, extend existing platforms, and reduce dependence on provider roadmaps. As a result, SaaS growth expectations are being recalibrated. The trajectory is not collapsing, but it is flattening as enterprises increasingly adopt build-oriented approaches.
Exhibit 1 illustrates how AI is reshaping SaaS growth expectations and flattening traditional value creation trajectories.
Exhibit 1: SaaS growth in the AI world
The HLS industry reality: SaaS is more than software
The HLS industry operates under a fundamentally different set of constraints than those reflected in public market sentiment. This is where the SaaS is dead argument begins to break down. Several structural factors continue to reinforce how SaaS platforms operate in HLS enterprises.
- Deeply embedded, regulation-aligned platforms: Leading SaaS providers deliver end-to-end enterprise systems, such as Customer Relationship Management (CRM), Electronic Health Record (EHR), and Electronic Data Capture (EDC) platforms that align closely with complex regulatory and compliance requirements, making them difficult to displace with AI-generated alternatives
- AI as an augmentation layer, not a replacement: The HLS industry is evolving from systems of record to systems of engagement with established vendors embedding AI capabilities through acquisitions, partnerships, and platform enhancements, strengthening rather than replacing existing SaaS ecosystems
- Unfavorable build versus buy economics: Developing and maintaining custom enterprise-grade custom solutions entails significant cost, ongoing maintenance burden, and specialized talent requirements, all while the industry faces growing cost pressures
- High risk aversion and outcome orientation: HLS enterprises prioritize validated, scalable, and compliant solutions, limiting appetite for internally developed systems that lack proven scalability, compliance, and reliability
SaaS in HLS enterprise environments is not just about features. These platforms serve as interconnected layers supporting workflows, data management, integrations, and compliance processes. Narrow, single-function tools with limited differentiation, such as rule-based automation, dashboarding and reporting solutions, are increasingly vulnerable to AI-native alternatives. In contrast, platforms deeply embedded in enterprise operations, including eClinical systems, Electronic Laboratory Notebook (ELN) / Laboratory Information Management System (LIMS), and Customer Relationship Management (CRM) systems, remain sticky.
Most HLS enterprises still operate in silos, with limited cross-functional integration and fragmented engagement. As a result, the industry focus is shifting toward orchestration layers that enable real-time coordination across functions and stakeholders. AI can replicate features, but replicating enterprise-scale orchestration, integration, and compliance remain significantly more difficult.
Exhibit 2 outlines the emerging divide between SaaS providers that are adapting to AI disruption and those struggling to differentiate.
Exhibit 2: AI is creating a clear separation between SaaS leaders, survivors, and vulnerable providers
This does not mean SaaS is immune to disruption. AI will continue to commoditize feature-level innovation and reduce dependence on provider release cycles. However, it is unlikely to replace core systems of record or the integration layers that underpin enterprise workflows.
AI is reshaping the SaaS ecosystem
This shift creates a new dynamic in the SaaS ecosystem. For incumbents such as Salesforce and Veeva, innovation remains constrained by roadmap prioritization and long-term planning cycles. These providers must compress multi-year development timelines into months without disrupting their own operating models.
Companies are already accelerating investments through AI partner ecosystems and acquisitions to address this challenge. For example, Veeva’s AI partner program and recent Ostro acquisition are enabling ecosystem-led innovation across its Vault applications. Similarly, Salesforce continues to expand its life sciences-focused alliance network to strengthen out-of-the-box integrations with data and AI partners, including Viz.ai and Infinitus.ai.
Meanwhile, this also creates a significant whitespace for the niche technology providers gaining traction across the HLS industry.
Exhibit 3 captures the areas across the life sciences value chain where niche technology players are emerging. Their value proposition is centered around building targeted innovative capabilities, addressing specific workflow gaps left unaddressed by SaaS incumbents, and deep domain expertise. They are not replacing core platforms but are increasingly workflow-specific value.
Exhibit 3: Emerging niche technology players across the value chain
SaaS in healthcare and life sciences is not disappearing. Core platforms will continue to remain deeply entrenched due to their role in managing complex workflows, data, and compliance. However, AI is redistributing value away from feature expansion and toward speed, flexibility, and ecosystem enablement.
The real question is not whether SaaS survives. It is how much disruption AI introduces, where value migrates, and which providers adapt fastest to the new enterprise reality.
If you found this blog interesting, check out, The era of the agents, Chapter 1: Commercial models for SaaS – Everest Group Research Portal, which delves deeper into another topic relating to SaaS.
If you have any questions or would like to discuss these topics in more detail, feel free to contact Durga Ambati ([email protected]).