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AI & Automation

Nothing looks wrongLook again

In AI & automation, the model that impresses in the demo is the one that breaks in production.Real data, real load and real consequences don't care how good the demo looked.

  • LLM in production
  • Evaluation
  • Data readiness
  • Human in the loop
Production reality

The demo was never the hard part

The gap between an impressive prototype and something you can trust in production is where most AI work quietly fails

The signs it won't survive contact with production

  1. 01No evaluation harnessQuality is judged by vibes and cherry-picked examples, never measured against real tasks
  2. 02Data that doesn't exist yetThe workflow assumes clean, labelled, permitted data that nobody has actually produced
  3. 03Happy-path onlyImpressive on the demo prompt, untested on the messy, adversarial inputs real users bring
  4. 04No fallback planWhen the model is wrong there is no human, no override and no safe default
  5. 05Dashboards, not decisionsOutput lands on a screen instead of changing what a system or a person actually does