You still have the deadlines, meetings and people waiting for answers. You already had enough work to fill your day.
Now here is another thing: you are also expected to learn AI.
Perhaps your organization has introduced a tool, offered a demonstration or encouraged everyone to attend a workshop and experiment with something new. You can't really see the possibilities because something new is being introduced every few months. You may already have found something useful in what was introduced last quarter. But finding the time to become truly productive with it is another matter.
Where, exactly, is the time to learn, to use, to be productive and truly satisfied with your work product supposed to come from?
That question deserves a serious answer. Learning to use AI well takes attention, practice and judgment. Dare I say, it also takes time to explore and absorb what might be possible.
We need to reconsider the expectation that learning AI will follow the familiar pattern of learning a software application: attend the training, learn the functions and return to work ready to use them. Even with conventional software, that process takes practice. With AI, the capabilities themselves can change while you are still learning what to do with them.
AI can feel like a living, breathing presence because the possibilities keep moving. The technology is not alive. The tools, their connections and what we can attempt with them are continually evolving. That is what makes the experience different from learning a fixed set of functions.
If you've watched Star Trek, you know the appeal of exploring beyond what you thought was possible. AI can invite that kind of curiosity. A working day filled with deadlines leaves little room to follow it.
Now put that inside a law firm, where time is accounted for and the next matter is already waiting. How does a professional make room to learn?
What counts as work tells you what matters
Ropes & Gray offers one concrete example of how an employer can recognize that time. The law firm says its attorneys can devote up to 100 creditable hours a year to developing AI capabilities, whether connected to a client matter or not. Its first-year associate program allows 20 percent of creditable time for AI learning and innovation. The firm also requires attorneys and business support professionals to integrate AI into daily work. [1]
The significance is in what the firm is prepared to count. Learning has a recognized place alongside the work attorneys are expected to deliver.
When professional performance is measured through hours and output, recognizing learning within those expectations gives people a practical reason to make room for it. It helps answer the question that otherwise hangs over every training invitation: What happens to the work I am measured on while I am doing this?
An allocation of hours does not establish whether a training program succeeds. It does acknowledge that capability takes time to develop. That time has to be accounted for before the benefits of using AI can reasonably be expected.
That principle has relevance well beyond law firms.
If you manage a team, support clients, teach, train colleagues or keep operations running, your existing responsibilities do not disappear when a new tool arrives. Someone has to decide how learning fits alongside them.
You need time to see what is useful
It is difficult to get excited about another demonstration when you have barely had time to use what you already have. Each new introduction can compete with the attention you need to understand the previous one.
You may recognize that a tool has potential and still struggle to see where it belongs in your work. That understanding often develops when you have time to try an idea, ask another question and examine the result.
You need to learn how to give the tool enough context, recognize an incomplete answer and check whether a convincing response is accurate. You also need clarity about which information you may use and what requires review.
That takes more than watching someone else produce a good result. You need room to discover what is useful in your own responsibilities, with the support to work through what you do not yet understand.
Workday's October workforce report points to a gap between employees' understanding of their needs and the support available. In its September survey, 79 percent said they knew which skills they needed to succeed, while 66 percent said their employer helped them develop those skills. The survey covered AI and wider workforce change; this is not a measure of AI training alone. [2]
For a professional already trying to keep pace, the distinction matters. Knowing you need to learn something does not create the conditions to learn it.
A quick result still has to meet your standards
Being productive with AI involves more than getting an answer quickly. You also need to be satisfied that the work is accurate, appropriate and good enough to put your name to.
Consider a manager preparing a staff briefing. An AI tool may produce a polished summary quickly. The manager still needs to determine whether it reflects the source material, leaves out an important qualification or recommends something that will not work for the team.
Learning how to assess and improve that result is part of becoming productive. If the draft takes substantial correction, you need time to understand why. Was the source material inadequate? Did you leave out important context? Is the task a poor fit for the tool?
Your experience matters here. You understand the people, consequences and standards involved. You need opportunities to learn how to bring that knowledge into your use of AI, so the result reflects the quality you expect of your work.
Practice that reveals a limitation can also be useful. It gives you a clearer basis for deciding when to use the tool and when another approach serves the work better.
What you learned last quarter needs time to settle
If you found something useful in the tool introduced last quarter, you need a chance to build on it. Repeated use helps you understand where it works, what needs checking and how to fit it into the day.
Introducing another capability before that learning has settled can leave you with more options and little confidence about which ones deserve your attention.
A changing technology does not require every employee to chase every development. It does require employers to make priorities clear and allow people to become capable with what matters to their work. Otherwise, learning becomes a succession of introductions with too little opportunity to turn any of them into a useful practice.
The people delivering training and support need that time, too. They have to explore capabilities, test examples and understand limitations before they can help colleagues use the tools well.
Otherwise, the pressure simply moves: employees are told to ask for help, while the people responsible for helping are trying to learn between their own deadlines.
Make the learning expectation specific
If you are being asked to develop AI skills, begin with the work you are expected to improve. “Become better at AI” is difficult to plan around. Learning to prepare a reliable briefing with an approved tool gives you somewhere to begin, with room to explore other possibilities as your understanding grows.
A productive conversation with your manager could sound like this:
“I want to use this tool well and produce work I can stand behind. Can we agree on where to begin, set aside time to explore and practice, and decide what can be adjusted in my workload while I learn?”
That makes the learning expectation concrete. It gives you a way to discuss what you need before an expectation of greater productivity is added to an already full day.
If you lead a team, make those decisions visible. People need to know which capabilities deserve their attention, where to get help and how learning fits into the workload you are asking them to carry. Recognize the time needed to explore a possibility and the practice needed to turn it into reliable work.
Professionals have a responsibility to keep developing. Employers have decisions to make about the conditions in which that development happens.
If learning AI is an ongoing part of doing the job, it needs an ongoing place in the work plan. The time to understand what is possible, become productive and produce work you trust has to come from somewhere.
Sources
1. Ropes & Gray — Artificial Intelligence: Equipping Every Attorney for AI-Powered Practice
Firm description of its Innovation Hours, first-year associate learning allocation and AI expectations. Accessed October 20, 2026. The stated creditable-hour allocations apply to attorneys; the source does not establish an equivalent allocation for business support professionals.
2. Workday — Global Workforce Report: AI Is Rewriting Jobs More Than It's Cutting Them
, October 5, 2026. The skills-confidence and support findings come from its global survey of 6,001 employees and business leaders. These are self-reported findings, not a causal evaluation of training effectiveness.