Think Big Technology founder and CEO Omar Hafez was featured in Forbes Business Council, writing about a gap he's seen up close: AI systems that perform beautifully in a demo, then struggle the moment they leave controlled conditions and enter real, regulated work.
Drawing on our experience building an AI-driven fire sprinkler blueprint generator, Omar unpacks why "mostly correct" isn't good enough in high-stakes industries — where a small coverage error can mean regulatory non-compliance or catastrophic failure — and what teams have to do differently.
A few of the threads he pulls on:
- Demos hide the hard part. Systems that look accurate on stage rarely face the messy, incomplete, inconsistent data of real operations. The cost of being wrong changes everything.
- "Mostly correct" doesn't cut it. In regulated environments, small margins of error introduce financial, legal, and operational risk — so reliability has to be designed in, not assumed.
- Build for where it runs, not for ideal conditions. Closing the gap takes structured logic on top of machine learning, clear validation pathways, human oversight, and alignment with industry standards and compliance frameworks.
- Fit the workflow before replacing it. Adoption depends on working alongside legacy systems and supporting gradual change, not demanding immediate transformation.
- Augment, don't replace. The most effective systems reduce repetitive work and improve consistency so professionals can focus on higher-value decisions — AI as a force multiplier.
- Deployment is part of the build. You don't really know how a system performs until it meets real conditions; the teams that win keep refining how their product handles edge cases and imperfect inputs over time.