There are bunch of tedious / routine tasks that AI can automate.
I think the big hurdle is mostly education / shift in mindset. We are so used to doing the task manually that most of us (including me) don't pause to think if I should be doing this or can I give to an agent.
I had browseros do a bunch of data validation for me in my Dolibarr ERP system. It cross checked my new master data against our old ERP, flagging bad links and filling in missing data. I could have done it much quicker overall with the api and some scripting, but it was easy to just write a two line prompt telling it where the data is and how to manage disagreements. Then I just watched it run on a second monitor for a few hours while I worked other projects.
I used a local Ollama model and though it was kind of amazing that it worked. I couldn't turn a typical user lose with something like this yet, but I think I see the vision. I image a lot of automation could happen this way in the future. I put less effort into the prompt than I would have needed to spend teaching someone from the office pool to accomplish the same goal, and got a good enough result.
In practice I have found that I can accomplish the same results in a stricter, more accurate, and faster way just using codex on the command line with some scripting and API access, but that's not going to work for a lot of people and putting it in the browser is pretty convenient... The MCP server that's built in can also become a bit of an API for the entire web if you're careful in how you use it, which opens up possibilities for things that don't have real APIs.
There are bunch of tedious / routine tasks that AI can automate.
I think the big hurdle is mostly education / shift in mindset. We are so used to doing the task manually that most of us (including me) don't pause to think if I should be doing this or can I give to an agent.
I had browseros do a bunch of data validation for me in my Dolibarr ERP system. It cross checked my new master data against our old ERP, flagging bad links and filling in missing data. I could have done it much quicker overall with the api and some scripting, but it was easy to just write a two line prompt telling it where the data is and how to manage disagreements. Then I just watched it run on a second monitor for a few hours while I worked other projects.
I used a local Ollama model and though it was kind of amazing that it worked. I couldn't turn a typical user lose with something like this yet, but I think I see the vision. I image a lot of automation could happen this way in the future. I put less effort into the prompt than I would have needed to spend teaching someone from the office pool to accomplish the same goal, and got a good enough result.
In practice I have found that I can accomplish the same results in a stricter, more accurate, and faster way just using codex on the command line with some scripting and API access, but that's not going to work for a lot of people and putting it in the browser is pretty convenient... The MCP server that's built in can also become a bit of an API for the entire web if you're careful in how you use it, which opens up possibilities for things that don't have real APIs.