Dassault Systèmes, PTC, and Siemens used their latest earnings reports to make a similar case for industrial AI: useful engineering AI depends on governed, trustworthy product data. While their strategies differ, all three are positioning existing software platforms and domain knowledge as essential foundations for AI adoption, says Monica Schnitger in her blog.
Dassault Systèmes reported second-quarter 2026 revenue of €1.556 billion, up 4%. Subscription revenue increased 8%, while 3DEXPERIENCE Cloud grew 60%. Overall performance was restrained by weaker Life Sciences revenue and challenging European automotive conditions. The company is also acquiring ArisGlobal for $1.8 billion as it expands its Life Sciences AI and compliance capabilities.
PTC reported $600 million in third-quarter revenue. Although revenue fell below expectations because of the shortened duration of one large contract expansion, annual recurring revenue performed better. Constant-currency ARR reached $2.448 billion, up 9.1%, prompting PTC to raise its full-year ARR and revenue guidance.
Siemens Digital Industries Software recorded 15% revenue growth, supported by more than 30% growth in electronic design automation. Orders reached approximately €1.7 billion, while organic ARR increased 11%.
AI provides the clearest distinction among the companies. Siemens is building Intelligence Center X as a horizontal industrial AI platform incorporating knowledge graphs, industrial ontologies, AI Studio, and Mendix. Dassault Systèmes favors AI embedded directly into 3DEXPERIENCE, arguing that engineering applications require physics-based, domain-specific understanding rather than general-purpose models connected to existing applications. PTC emphasizes its CAD, PLM, ALM, and service lifecycle management systems as trusted systems of record that provide structured product data to AI models.
Despite these differences, the three vendors share a fundamental strategy. Each sees governed engineering data as the competitive advantage that can make AI reliable for industrial applications, reinforcing the importance of established PLM platforms as AI moves deeper into engineering workflows.
