NOW vs PATH

ServiceNow presents a bullish AI-forward narrative: positioning itself as an "AI control tower" and highlighting product momentum (Now Assist, Workflow Data Fabric, Raptor, CPQ, Build Agent) and strategic moves (acquisitions of Moveworks, intent to acquire Armis and Veza). The company announced multiple partner integrations (Anthropic/Claude, OpenAI, Microsoft Agent 365, Figma) to bring foundation/frontier models and agent orchestration into its platform and announced programs (Build Program, Now Next AI, ServiceNow Store) to accelerate partner-built AI agents. Financially, Q4 2025 total revenue was $3,568M (20.5% YoY), non-GAAP gross margin 80.5%, non-GAAP operating income $1,101M, GAAP net income $401M, and non-GAAP free cash flow $2,032M. Guidance includes Q1 2026 subscription revenue of $3,650–3,655M and full-year 2026 subscription revenue $15,530–15,570M, and management links AI product adoption (Now Assist, Moveworks) to near-term guidance and long-term market opportunity.

UiPath positions itself as a leader in ‘agentic automation,’ delivering product and partner-driven AI progress this quarter. Key AI actions include platform capability enhancements (pre-built agentic solutions, agent-building and testing tools), integrations with Microsoft Azure AI Foundry and Microsoft Copilot products via the Model Context Protocol, a ChatGPT connector with OpenAI, a voice-enabled UiPath Conversational Agent built on Google Gemini, a collaboration with NVIDIA to deploy Nemotron models via NIM, and a Snowflake Cortex AI integration for agent-driven data insights. Management emphasizes execution and enterprise-scale adoption of agentic automation while reporting strong Q3 fiscal 2026 results: total revenue $411.11M (+16% YoY), ARR $1.782B, GAAP net income $198.84M, non-GAAP operating income ~$88M, non-GAAP gross margin 85%, and non-GAAP adjusted free cash flow $28M. Guidance for Q4 FY2026 revenue is $462M–$467M. The release is bullish on innovation and execution, with moderate risk language around stock-based compensation variability and macro/cloud dependencies.