Analyst(s): Keith Kirkpatrick
Publication Date: February 10, 2026
SaaS is being tested—not displaced. Although the presence of AI-first companies is intensifying competitive pressure, enterprises will face significant hurdles if they plan to rip and replace deeply integrated SaaS applications with AI-native tools. SaaS companies can remain relevant by continuing to invest in AI while leveraging their strengths in scale, data governance, and enterprise operational expertise.
What is Covered in this Article:
- An overview of the sharp decline in major software stocks, including SAP, ServiceNow, Salesforce, and Microsoft
- How AI advancements and investment are intensifying competitive pressures on legacy SaaS providers
- Identifies the three major considerations for enterprises seeking to swap SaaS software for AI-led offerings
- The strategies SaaS vendors can apply to quell investor and buyer concerns about the impact of AI-native providers
The News: On January 29, 2026, US software stocks suffered a steep selloff after SAP’s cloud revenue forecast and ServiceNow’s earnings disappointed investors, intensifying fears that AI-driven automation is eroding the value proposition of traditional SaaS offerings. SAP shares plunged more than 16% after its cloud backlog and 2026 revenue forecast missed expectations, while ServiceNow fell 11% despite projecting strong subscription growth.
The S&P 500 Software and Services Index dropped 8.7% to a nine-month low, with other major players like Salesforce, Adobe, Datadog, Microsoft, Atlassian, Zscaler, Intuit, and HubSpot also posting significant declines. Meanwhile, chip and memory firms surged, highlighting a stark divergence in market sentiment.
A key recent development has been the launch of OpenAI’s Frontier, an enterprise platform designed to help organizations build, deploy, and manage AI agents that function as digital coworkers rather than standalone bots. Frontier is designed to support durable, production-ready agents by unifying several key areas: shared business context, agent identity and permissions, workflows for onboarding and learning, and tools for evaluation and optimization.
Meanwhile, Anthropic is attempting to become the control plane for enterprise cognition. Instead of relying on a traditional application to enable workers or agents to take action, an agentic control plane is designed to ingest data, decide what action is required, execute actions across multiple systems autonomously, and then only escalate when human judgment or actions are truly required.
These new offerings challenge SaaS vendors whose value is anchored in workflow ownership, and portend a world in which agents, not applications, are the primary enterprise interface.
Is SaaS Facing a Threat from AI Automation?
Analyst Take: The emergence of AI-first tools that are seen as disrupting SaaS offerings is forcing enterprises to address four considerations:
- The Build vs. Buy Conundrum
- Managing data integrity, security, and data gravity issues
- Leveraging AI to drive performance improvements around processes, workflows, and decision-making
- The requirement for SaaS companies to level up pricing, packaging, and performance to compete against AI-native competitors and deliver value to investors
The Build vs. Buy Conundrum
AI-native vendors are promising to provide AI that will enable organizations to supercharge productivity by generating code, automating workflows, and ultimately helping organizations improve productivity, efficiency, and surface insights that can help drive their business forward. The productivity gains generated through co-pilots in the coding world, combined with a plethora of new “vibe-coders,” have added more enthusiasm for the build mantra.
However, AI tools, no matter how efficient or easy to use, will still require organizations to build out the core functionalities that were provided by SaaS companies, including CRMs, ERPs, and other business applications, that AI-native tools are supposedly replacing.
For most companies, these development tasks do not align with the organization’s core business, and are likely to introduce both short- and long-term resource and cost issues. We’ve seen the cycle before: new, more efficient tools to allow the development of software appear promising, but the reality is that software development, not to mention integration work, data management issues, and system maintenance and upgrade work, are usually not core functions or areas of competency for many businesses.
It is these companies that have typically seen the value in SaaS software vendors, which handle the heavy lifting by providing relatively out-of-the-box solutions that benefit from the scale of serving hundreds or thousands of customers, and are thus able to provide continuous innovation with far fewer headaches and hassles.
Notably, full rip-and-replace migrations to new software are seen as massive undertakings, and attempting a migration to a full DIY build approach is unfeasible and unlikely to gain traction among most organizations.
Managing Data Integrity, Security, and Gravity
Indeed, the challenges of migration from an installed SaaS platform or application often arise because of data issues. Data is the “oil” and competitive differentiator for most organizations, and as such, managing data integrity and hygiene, as well as data privacy and security, requires significant time, effort, and expertise. SaaS providers incorporate these elements as core features of their software because they realize the precious nature of an organization’s data.
Left to manage these issues on their own, an organization would need to build a data management platform, security software, and data storage solutions, which requires significant resources and expertise. For multinational organizations that need to pay attention to data usage and storage regulations across myriad jurisdictions, costs and resource requirements may grow exponentially. Ultimately, the build approach may leave non-tech firms scrambling to ensure their solutions incorporate the latest innovations and security and maintenance updates, and likely would require adding additional resources to manage them.
However, the most challenging aspect for organizations seeking to sever their SaaS contracts is around data gravity. SaaS companies provide the storage for enterprise customer, process, and product data, and are unlikely to make it simple or inexpensive to migrate that data from their platform.
Leveraging AI to Drive Performance Improvements
While it’s true that AI-native companies are moving quickly to develop agentic and other AI technology to improve processes, they do not yet possess the embedded business processes, logic, and domain expertise. Enterprises are increasingly focused on the direct impact that software spend has on the business, in the form of topline growth or bottom-line profitability.
It is this domain expertise and experience on how to best manipulate and leverage the company’s data that can reduce the time to insights and value, particularly for more complex industry actions, workflows, and processes. This expertise and experience, which can include domain-specific regulatory insights, human-worker interaction learnings, and best-practice knowledge around data flows, work processes, and system integrations, is unlikely to be available from these new AI-forward companies.
Although AI-first companies are likely to provide forward-deployment resources to help ensure their software meets enterprise needs, this can add cost, complexity, and time to deployment schedules. It would also require organizations to open access to their proprietary customer data, currently held in the CRM or ERP system and owned by the customer, not the vendor, which requires significant guardrails and trust that the data won’t be used to train models or be exposed to other parties.
SaaS AI Investments Required to Keep Up With AI-Native Companies
The current decline in the value of US software stocks can be seen as an indicator of a market reckoning with the disruptive potential of AI in the SaaS sector. The rapid evolution of generative AI and automation tools is challenging the very foundation of subscription-based software businesses. This dynamic is reflected in the market’s response, where investors have been heavily focused on the promises of AI-native companies to leverage their tools to provide business-application functionality without what they believe is the unnecessary cost and complexity of SaaS applications.
As such, even when stalwarts such as ServiceNow, SAP, Salesforce, Adobe, and others post solid quarterly results, investors focus on the massive investment these companies are making in their AI capabilities and expect to see revenue and growth that can be directly tied to these expenditures.
While there is certainly progress being made, particularly in driving adoption among customers using copilots, AI agents, and other AI technologies, customers are still reluctant to either pay a premium for AI technology or embrace it at the scale required to drive significant vendor revenue.
This problem may be solved by a solution rooted in a traditional, non-AI sales and service strategy: meet customers where they are and acknowledge a potential shift to a world where applications may no longer be considered essential to getting work done.
If SaaS vendors do not evolve their packaging and pricing, they may risk becoming data providers and execution engines, giving up strategic value, pricing power, and customer mindshare to these new platforms. This may result in buying centers shifting from CIOs evaluating features to executives focusing more heavily on outcomes.
This means offering flexible AI packaging and pricing models tailored to the customer’s use case, risk tolerance, and usage volume. Options should include seat-based pricing, consumption pricing, all-you-can-eat models, and outcome-based approaches, depending upon the level of complexity of the use case and underlying metric, the organization’s budget and forecasting needs, and risk tolerance.
By providing flexible pricing models that meet customers’ specific needs around AI, they can reduce the friction that often occurs when organizations have concerns about costs, particularly when scaling the technology across the entire enterprise. When this friction is reduced, AI can be deployed at scale, which will help SaaS vendors drive additional revenue that can be directly tied to their investment into AI models, compute, and ancillary services, bolstering confidence among both executives and investors.
What to Watch:
- Emergence of new AI-native competitors challenging established SaaS categories: Investors and enterprise buyers will continue to see whether these competitors can efficiently deliver value as full solutions or simply as replacements for aging point solutions.
- The use of data gravity as a competitive moat for SaaS players, particularly as a strategy to prevent full-scale migration off their platforms.
- Whether enterprises take on additional complexity and cost to leverage AI-native tools to build out business applications, or cancel or let their SaaS contracts expire.
- The evolution of SaaS packaging and pricing strategies in response to new market entrants, and the continued demand to demonstrate how AI is being monetized.
- Enterprise monetization vs. spend: Investors will continue to evaluate how SaaS vendors translate large AI spending into material growth and margin expansion.
See the news story that covers the drop in software stocks, published at Reuters.com.
Disclosure: Futurum is a research and advisory firm that engages or has engaged in research, analysis, and advisory services with many technology companies, including those mentioned in this article. The author does not hold any equity positions with any company mentioned in this article.
Analysis and opinions expressed herein are specific to the analyst individually and data and other information that might have been provided for validation, not those of Futurum as a whole.
Other insights from Futurum:
Systems of Agency: Agentic AI to Drive $762B Enterprise Software Super-Cycle by 2031
AI Reaches 97% of Software Development Organizations
AI Inference: Enterprise Infrastructure and Strategic Imperatives
Author Information
Keith Kirkpatrick is VP & Research Director, Enterprise Software & Digital Workflows for The Futurum Group. Keith has over 25 years of experience in research, marketing, and consulting-based fields.
He has authored in-depth reports and market forecast studies covering artificial intelligence, biometrics, data analytics, robotics, high performance computing, and quantum computing, with a specific focus on the use of these technologies within large enterprise organizations and SMBs. He has also established strong working relationships with the international technology vendor community and is a frequent speaker at industry conferences and events.
In his career as a financial and technology journalist he has written for national and trade publications, including BusinessWeek, CNBC.com, Investment Dealers’ Digest, The Red Herring, The Communications of the ACM, and Mobile Computing & Communications, among others.
He is a member of the Association of Independent Information Professionals (AIIP).
Keith holds dual Bachelor of Arts degrees in Magazine Journalism and Sociology from Syracuse University.
