AI prototyping costs pose hidden risks for businesses

Generative AI has significantly reduced the time and cost required to create functional prototypes and minimum viable products (MVPs). However, experts warn that this ease of prototyping can lead to a “build vs. buy” trap for businesses.

Futuri CEO Daniel Anstandig notes that while a demo can be assembled in days, the long-term costs of internal development—including security, compliance, integration, support, and quality assurance—are often underestimated. He suggests that companies should only build AI internally if the system provides a unique competitive advantage through proprietary data or specialized processes. For generic or highly regulated functions, purchasing existing software remains more predictable.

Furthermore, the shift from project-based work to product-based ownership is critical. While AI makes initial versions cheap, the costs associated with reliability, governance, and long-term maintenance do not decrease. Experts emphasize that successful AI transformation requires permanent teams dedicated to continuous improvement and ownership, rather than temporary teams that simply deliver a project and hand it off.