The warning from AlixPartners highlights a major potential shift in the enterprise software landscape.
According to analysis detailed in their 2026 Enterprise Software Technology Predictions report and related discussions, AI agents—autonomous, agentic AI systems capable of handling complex tasks, workflows, and decision-making—threaten to disrupt traditional software models significantly.
They estimate that up to $500 billion in enterprise software revenue could be at risk or “collapse” as these agents replace or subsume entire categories of tools that knowledge workers currently rely on. This isn’t just about adding AI features to existing SaaS products; it’s a fundamental restructuring.
AI agents could eliminate the need for many standalone applications by directly orchestrating data, processes, and outcomes. Traditional per-seat/subscription pricing (the SaaS backbone) faces pressure as AI boosts productivity, potentially reducing required “seats” or shifting to usage/outcome-based models.
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This contributes to what’s been dubbed the “SaaSpocalypse,” with recent sharp declines in software stock values; hundreds of billions in market cap evaporation in early 2026 sessions tied to fears of AI disruption.
The firm predicts accelerated M&A potentially $600 billion in deal value for 2026, consolidation in the mid-market, and a move toward hybrid valuations that factor in AI leverage and outcomes rather than pure ARR multiples.
Experts like Michelle Miller at AlixPartners emphasize that no segment escapes unscathed, though adaptation like transitioning to AI-powered services or outcome pricing could help some thrive. This ties directly into Palantir’s recent performance and messaging.
In their Q4 2025 earnings reported early February 2026, Palantir delivered blowout results: revenue hit ~$1.41 billion up 70% YoY, beating estimates, with U.S. commercial revenue surging 137%. Adjusted profits and margins were strong, and they guided aggressively higher for 2026.
CEO Alex Karp has been vocal about this shift, arguing that AI especially large language models isn’t enough on its own—true value comes from platforms that integrate them deeply into enterprise complexity (data, operations, ontology).
He has positioned Palantir as replacing or outperforming legacy software stacks, with their AI Forward Deployed Engineers (AI FDEs) and ontology enabling rapid migrations and automations that sideline traditional tools.
In essence, Karp’s thesis aligns with the disruption narrative: AI isn’t merely augmenting enterprise software—it’s actively replacing chunks of it, favoring platforms like Palantir’s that orchestrate AI agents effectively rather than point solutions.
This contrast is stark—many legacy SaaS players face revenue compression risks, while Palantir and similar AI-native or ontology-heavy players appear to benefit from the transition, capturing outsized growth through deeper, outcome-driven deployments.
These views reflect a broader 2026 debate: AI could destroy massive value in incumbent software while redirecting spend toward more integrated, agentic systems. The $500 billion figure represents potential “at-risk” revenue rather than guaranteed disappearance—much depends on how vendors adapt, but the pressure is real and already showing in market reactions and earnings narratives.
Snowflake is betting big on AI agents as “workflow engines” that operate across functions, such as in marketing, finance, and media. For instance, in advertising, agents could automate personalization and optimization, while in financial services, they unify data for real-time insights.
The strategy includes building ecosystems where agents interact seamlessly, backed by consistent data semantics and human oversight rules. These features are designed to operationalize AI, moving beyond pilots to enterprise-scale deployments.
Snowflake emphasizes “friction-free” adoption, where AI runs natively on the platform, helping firms in regulated industries break down silos and achieve competitive advantages. A cornerstone of Snowflake’s strategy is its model-agnostic stance, avoiding lock-in to any single AI provider.
This is evident in high-profile partnerships: OpenAI: A $200 million multi-year deal announced in February 2026 integrates OpenAI’s models like GPT series into Snowflake, accessible across major clouds (AWS, Azure, GCP). This allows customers to build AI on their data without migration, enhancing enterprise-ready AI.