With so much enthusiasm about the rapid advancement we’ve made in using LLMs this year, some of the remaining barriers and bottlenecks tend to get lost in the shuffle. As with all prior technologies, companies have to introduce an AI project the right way. The way I’ve heard it said is that new workflows and tools need to be a help, not a hindrance, to a company. We often talk about this as a productivity issue – if it’s instituted correctly, the new project will help workers to be more productive, confident, and on top of their jobs. If it’s done poorly, it can mire them in low productivity, and actually inhibit the work that needs to get done. Lack of buy-in and enthusiasm : This is another way that AI follows all other prior technologies. Yes, it’s a more powerful technology with a lot more versatility for implementation – but you still need stakeholder buy-in. Otherwise, you’re starting from a position of weakness, and it’s an uphill battle.This Substack piece talking about common challenges uses the phrase “low user adoption,” which basically means that people aren’t choosing to use a new AI tool or system. That on its own is a core problem for enterprise AI. Overly broad directives : Suppose someone in a company orders everyone to immediately “move everything to AI.” There are a couple potential problems with this. First, there’s lack of clarity about what these directives mean. There’s also likely to be a lot of overlap and redundant efforts, as well as chaos inside of departments.

Full opinion : 8 Major Problems That Companies Face While Adopting Enterprise AI.