A lot of companies have made casual AI use easy and serious use very difficult. They provide access without teaching or understanding proper practice, build internal products without understanding the workflow they’re meant to augment, and set allowances that stop the people attempting to achieve more more.
Learning to delegate the whole job
Being able to prompt tells you very little about your ability to hand off a substantial piece of work and know whether it was done properly. Real delegation means learning how much context to supply, what can run independently, what deserves rigorous verification, and when and how to intervene.
In the workflow I use, my agents receive clear assignments, build the change, and pass it to other agents that run the software and try to break it. Failures go through a repair loop, and all of the records from every action stay within the project so later runs can leverage them as further context. The agents can construct this workflow too, though I still have to decide what success means and what evidence I’ll accept. That structure is how I can leave work running 24/7 while I do something else. Getting to this point requires significant practice with tasks that matter: including the unpleasant part where you discover that the thing you thought was finished doesn’t work.
“The left hand is useless at almost everything, for lack of practice. But it guides the reins better than the right. From practice.” — XII. 6
Anthropic’s analysis of roughly 400,000 Claude Code sessions found that users who demonstrated a working grasp of the task were more likely to achieve success supported by evidence in the analyzed transcript. Most of the gap was between novice and intermediate users, with smaller gains beyond that. A library of clever prompts leaves most of that skill untaught. Companies have to teach (or environments that encourage) it by working through real tasks with people and reviewing what went wrong.
Do the product work
Instead of teaching the practice, many organizations try to package everything into an internal AI platform. An internal platform might have users but often these users can simply keep doing their work elsewhere. Before calling that resistance to change, watch the job you’re asking them to change.
Imagine a salesperson preparing for a customer call. A new portal requires them to find the account notes, upload documents, explain the situation, and carry the answer back into the system they were already using. The model answers quickly, but the whole task may still take longer or may be completely antithetical to how they want to work. In RAND’s interviews with 65 experienced AI practitioners, recurring failure modes included misunderstanding the problem and pursuing technology ahead of users’ needs. Giving your AI platform an internal audience doesn’t make those mistakes less expensive.
Compare this with a study of 5,172 customer support workers that found a 15 percent increase in issues resolved per hour after an AI assistant was introduced. The tool worked because it monitored the customer conversation already happening and supplied suggestions the worker could use, edit, or ignore. A different way of working has to earn the effort it demands. You have to understand the existing work well enough to decide which steps to remove, rebuild, or keep, and then measure the entire task: including gathering context, checking the answer, and putting the result to use.
This is basic product thinking that still applies when your customer is on your payroll.
Let the budget follow the result
Even when the tool fits the work some will learn to do much more with it than others. A cap generous enough for rewriting emails is too small for someone delegating hours of work to agents.
In OpenAI’s 2025 enterprise research, workers reporting more than ten hours saved per week consumed eight times as many credits as workers reporting no time saved. Those savings were self-reported, and the relationship doesn’t establish that spending caused them, but it gives a clear reason to examine heavy use before deciding on universal across the board usage limits.
For example, the last 30 days through October 22 (today), my public activity log showed ~5,400 agent sessions, 1,600 commits and 110B tokens. Its roughly $53,000 in API spend. I know what I achieved with this usage and it was substantial. A cap that allows only moderate chatting would prevent this way of working entirely. It might also prevent someone from learning whether it suits their job.
Give everyone room to learn, then increase budgets for people who can show useful results. Judge the cost of completed, accepted work, including the human time spent checking and correcting it, and ask the people finding better ways to work to teach them. The person using ten times as much AI deserves a closer look before they deserve a smaller budget.
If you want to change what your organization gets out of AI, start with one recurring job that takes too long, produces too many errors or has significant revenue generating potential. Put the person who knows that job beside someone who can drive agents. Give them time and a real budget to learn, compare the finished work with the existing process, and expand what proves useful.
Every new technology looks disappointing when we insist on fitting it inside the habits and budgets of yesteryears. Stop trying to ration a little bit of AI across your entire org chart. The ceiling right is capped by what you allow your people to learn not what the models can do.