Any company can take on an effort to replace software. I am more inclined to be supportive if it's custom software. If so, one will need to rethink the workflow, train the model for user and enterprise context, agentify it. If done right it will have the property of increasing data integrity and also providing an ability to create enterprise context and intelligence over time which can significantly improve outcomes. The tools to do so are still nascent and evolving, mostly because models are expanding their capabilities and remit across memory, eval et al. Also, there is no backward compatibility on model embedding, a new model appears and the entire exercise on eval etc needs to be repeated. If enterprises are attempting to replace packaged software, I would caution against that, packaged software will get reimagined as an AI application by new startups as technology stabilizes. The challenge is as always the availability of competent resources who can architect an agile, AI first backend infrastructure. If you don't try though, you won't learn and be ready.