When you start a new job there is a lot to learn. There are projects, clients, and processes that are likely to be new to you. You may have been pointed at the employee handbook and maybe even given a mentor. But you probably still have more questions than answers for the first few months. As trite as the phrase “drinking from a firehose” may have become, it is a pretty accurate description of those first eight or 10 weeks.
You should take advantage of all of the resources given to you to get up to speed. Read the documents you’re given. Familiarize yourself with key processes before you need to implement them for real. Meet with your mentor(s) and get to know them. You’re going to need friends and advisors at work.
At the same time, AI may be a useful tool as well. Before you begin, find out what kinds of resources you can count on as an employee. For one thing, if you have access to an enterprise license for an artificial intelligence platform, you don’t have to worry about the system ingesting proprietary data that it should not have access to. For another, the company may already have set up bots and agents to help you get answers to key questions and to deal with repetitive tasks. You don’t want to reinvent tools that are already in place.
Once you know what is available already, here are three ways to use artificial intelligence productively as you ramp up.
1. Build some agents and skills
Get to know the tasks that will be a regular part of your work. If there are elements that are repetitive, see if you can build an agent or AI skill that will handle these for you so that you can focus your efforts on using your expertise. Even if you don’t know any programming, most AI systems will build these agents for you from a description. It may take a little practice to get it working the way you want it to, but the effort is well spent.
When I started my new job at Minerva Project in February, I knew that part of my work was going to be writing stories about ways to improve higher education. I wanted to be able to attach those stories to things that people were thinking about, so I built an agent that scans news sites on the internet every Monday and finds three stories that relate to improvements in higher education and suggests angles for stories. I still need to write the story, but the agent helps me find ways to connect to current events.
Make sure you walk the line between using AI to make you more productive and using AI to do the work that requires your expertise. In the example I just gave, I don’t let large language models (LLMs) write stories for me. AI-generated text is still easy to spot and never quite captures the insights you want to convey. If AI wrote my stories, they wouldn’t be as good. Even if I let LLMs draft stories and tried to edit them later, too much AI slop would likely seep through. After all, it is bad enough that I have always liked em dashes; I don’t want to sound like a bot as well.
2. Create a thought partner
When you are new to a job, you probably don’t yet think like someone who has been at the company for a long time. While your fresh perspective will be valuable, there are methods that people in the company use to be successful, and you’ll need to internalize them.
One thing that can help is to set up an AI project that you can use to talk through complicated problems at work. You can feed it documents that relate to the mission of the company, examples of previous work, and information about projects and clients. Then, give the AI instructions for how you would like it to engage with you. You might ask it to suggest ways to think about a problem, or to critique your reasoning. You might want it to ask you questions rather than giving you answers. You can even create a few different projects with different personalities depending on what you’re trying to accomplish.
Then take a problem you’re struggling with at work, and engage in a conversation. Because the model will draw from information relating to the company and its mission, the responses you get are likely to help you refine the way you’re thinking. In my experience, the specific things the model responds with are not usually the solution to a problem. Instead, they get you thinking differently about the situation you’re discussing in ways that lead you to an approach you would not have thought of alone. This team of AI advisors can help you contribute to projects more quickly.
3. Give yourself opportunities to practice
Your new job may also require you to do things you’ve never had to do before. If you’re a first-time manager, you might need to critique someone’s performance. If you’re working in a client-facing role, you might have to negotiate or sell when you have never done that before. Handling situations like these is complicated, and the stakes can be high. In addition, other people don’t always respond the way you expect them to, which can fluster you the first time you try to engage.
Another great use of AI is to create your own batting cage. Build a project that plays the role of another person in a scenario that you will have to face. If you’re worried about giving criticism to a direct report, then set up a system that plays the role of an employee you have to critique. You can instruct the system to decide how it wants to respond so that you get practice with people who do and do not take criticism well. You can also instruct the system to give you feedback on your performance afterward. In this way, you not only get to practice how to do something difficult in a simulation, but you also get immediate feedback about how well you did and what you could do differently in the future. That will give you more confidence to engage in this interaction when you do it for real.
