Why companies fail at AI

Companies often fail at adopting AI because they fail to recognize the issues already present in their operations. Remote, a company that implemented AI for performance reviews, found that the technology exposed existing problems rather than introducing new ones. They compressed a process that once took eight weeks down to two days, demonstrating that AI can quickly reveal hidden flaws.

Many companies experience failures when they automate revenue projections or payroll compliance with unvalidated data and misconfigured rules. In these cases, the existing issues were amplified by the AI, making them impossible to ignore.

Air Canada's experience with a chatbot providing incorrect information about bereavement fares highlights another common pitfall. The error was present before the AI was introduced, but the technology brought it to light in a matter of weeks instead of letting it go unnoticed for years.

The standard advice to slow down, build a solid foundation, and deploy AI later is considered backwards by some. If a company's processes are broken, waiting won't fix the issue. Instead, deploying AI can make the problems visible, measurable, and impossible to overlook. Companies that move fastest in adopting AI are discovering existing risks that their competitors fail to address.

A June 2026 IBM study found that 70% of organizations are deploying AI tools faster than their leadership can keep up. This rapid adoption is often framed as a crisis, but it is simply what adoption looks like in the modern business landscape. The key is for leadership to speed up the process, as the stakes are high in areas such as performance reviews and payroll. When using AI, it is crucial to have a human involved in the output until confidence in the system is built.

While "keeping a human in the loop" and "not deploying yet" may seem contradictory, they serve different purposes. The former is engineering, while the latter is rooted in fear. Before deploying AI to a process, companies should ask themselves if they trust the technology. If the answer is yes, they should proceed. If not, they should still move forward to uncover what's broken, years ahead of their competitors. The only real failure mode is standing still while the rest of the industry advances at an accelerated pace.