Following Anthropic's latest product launch event, Wedbush stated that market concerns about generative artificial intelligence disrupting traditional enterprise software are significantly exaggerated. Recent volatility in the software sector stems more from sentiment than fundamental weaknesses. An analyst team led by Dan Ives at Wedbush noted that Anthropic showcased several enterprise-focused product updates during its "Enterprise Agents" launch, emphasizing agent-based workflows and enterprise-grade integration capabilities. Through live demonstrations, Anthropic illustrated Claude Cowork's application scenarios within several large enterprises. These included Spotify Technology using it to shorten engineering time for complex code migrations, Novo Nordisk utilizing it to improve the efficiency of clinical research document organization, and Salesforce.com employing it within Slack to compress project cycles. However, Ives emphasized that while these use cases are impressive, the new generation of AI tools will not "tear down and rebuild" the existing enterprise software ecosystem. "The value of AI tools is highly dependent on the data they can access and cannot operate independently of existing systems," he pointed out. He noted that the market often equates the capabilities of foundational models with complete enterprise software platforms, overlooking the practical complexities of corporate IT environments. Wedbush believes that foundational models are not equivalent to enterprise software platforms. Demonstrations by Anthropic and OpenAI showcase more the intelligence level of the models themselves, rather than the workflow orchestration, compliance and auditing systems, security controls, system integration, billing mechanisms, and enterprise-grade service level agreements that businesses truly require. In contrast, vendors like Microsoft, Salesforce.com, ServiceNow, and Pegasystems are already deeply embedded in core enterprise processes, serving as the "system of record." Replacing them would mean reconstructing critical infrastructure, not simply overlaying a large language model. Analysts also indicated that the proliferation of AI will actually increase system complexity, thereby driving higher cybersecurity spending. As AI agents and automated workflows are deployed, API interfaces, machine identities, lateral movement risks, and cloud-native workloads increase significantly, boosting demand for endpoint, identity, and cloud security, as well as security operations centers. Consequently, security vendors like CrowdStrike, Palo Alto Networks, and Zscaler are seen as beneficiaries, rather than losers, in the AI era. Regarding the competitive landscape, Wedbush argues that the key to success in enterprise software remains channels and customer relationships, not merely model performance. Anthropic and OpenAI lack the two-decade-old enterprise distribution networks or deep Chief Information Officer relationships that Microsoft, Salesforce.com, and ServiceNow possess. The latter control the application layer where business logic resides. Analysts stated that the accelerated adoption of AI actually enhances the strategic value of these platforms, prompting enterprises to undertake a new wave of system modernization rather than bypassing existing systems. From a valuation perspective, Wedbush believes the recent compression in software stock valuations does not align with future earnings risks. There is no current evidence of accelerated customer churn, budget freezes, or competitive displacement; the market is primarily reacting to "demo risk" rather than "data risk." Regarding specific stocks, Wedbush considers the market's concerns about Microsoft to be amplified, presenting a significant buying opportunity. Analysts also noted that the decline in IBM Corp's stock price lacks fundamental support. IBM remains deeply involved in running numerous critical mainframe systems based on COBOL. Even as AI accelerates code migration and modernization, enterprises still require systematic migration, compliance validation, and integration services—areas where IBM has long held advantages and successfully commercialized them. Wedbush concluded that AI is more likely to trigger a modernization cycle for enterprise IT rather than disrupt the existing software landscape. By reducing the friction costs associated with modernizing legacy systems, AI could ultimately strengthen the long-term value of mainstream enterprise software platforms.
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