In a blog post on Schnitger Corporation, Monica Schnitger explores the evolving business models around AI in engineering and design. She uses Autodesk’s move toward outcome-based pricing as a case in point: rather than charging a flat annual fee for an AI assistant, the company is considering charging per design iteration or per “preliminary design created.”
The logic is clear: when AI becomes a partner in creative and iterative engineering workflows, pricing must reflect value delivered, not just hours of software use. In design work, users may generate multiple concepts, discard many, and refine a few. If AI accelerates this process, reduces scrap, and improves final outcomes, then a pricing model tied to iterations or results makes sense. Schnitger argues that if each iteration costs $N and the AI reduces iteration time or avoids bad ideas, then charging $N per iteration or less might still be profitable and acceptable.
Schnitger draws a parallel to the adoption of CAE solvers decades ago, initially seen as too expensive until pricing, cloud delivery, and user interfaces matured, making them accessible to more firms. She suggests we are at a similar inflection point for AI in engineering: the tool is morphing into a partner, and business models must follow suit. The article touches on the question of whether large firms will build their own AI solutions or rely on vendor offerings. While DIY remains possible, maintenance, updates, security, and scalability lean toward outsourcing to specialist vendors.
As AI moves from a “tool” to a “co-designer,” pricing must reflect this partnership: outcomes, iterations, quality gains. For engineering-software providers and buyers alike, aligning pricing with value might unlock broader adoption and innovation.