The Math Has Changed

"Don't start over, just optimize" was good advice in 2018, when building software cost millions and took 18 months. That math is dead.

Today a small team with modern tools can rebuild a legacy workflow in weeks for less than the annual maintenance contract on the system it replaces. The cost curve flipped. Most leaders have not updated their mental model.

The Harder Truth About AI Deployments

Most "optimization" projects in equipment finance are automating chaos:

  • The process was never defined
  • The triggers were never named
  • The escalation paths live in someone's head

Bolting AI onto that foundation does not unlock potential. It scales the dysfunction.

The data on AI deployment failures:

  • 95% of organizations deploying generative AI saw zero measurable return (MIT Project NANDA, 2025)
  • 77% of those failures were organizational, not technical (McKinsey, 2026)

The model was not the problem. The undefined process underneath it was.

The Right Question

Sometimes the fastest path forward is a controlled burn. Not because legacy tools have no value, but because the cost of working around their limitations now exceeds the cost of replacing them.

The right question is not "rebuild or optimize." It is: what does this process need to do, and what is the cheapest, fastest path to that outcome?

  • If your existing stack answers that question, optimize it
  • If it does not, stop dragging it along