Set Expectations for Employees at Every Experience Level

Same expectations. Different support.

Article Summary: To set expectations for employees with different experience levels, keep the outcomes and standards the same for everyone, ask each person what they already know and what is new, and vary your support rather than your standards. Huckleberry can play each person back to you before the conversation, so you hear how your expectations land.

You’ve inherited a team, or you’re building one. Two people are a few months out of school. One has been in the industry for 20 years. Another sits somewhere in between and goes quiet in meetings. You want to be fair, so you write one set of expectations and send it to everyone.

Then it lands in different ways. The newest people read “own your projects” and freeze, because nobody has shown them what good looks like. The veteran reads the same page and hears that you don’t trust them to know their job. The quiet one says nothing, and you find out how it landed when the work comes back.

That’s the hard part. Treat everyone identically and you under-support some and over-manage others. Treat everyone differently and the team wonders whose standards are real. You also want everyone heard, and the people with the least tenure have the least reason to speak first.

How to set expectations for employees at every experience level

1. ‍Fix what’s the same for everyone. Write down the outcomes, standards and behaviors that don’t flex, in specific terms. “Client reports reviewed and sourced by Thursday noon” does more work than “high-quality output.” Goal-setting research finds that specific, difficult goals lead to higher performance than telling people to do their best, as long as they get feedback, commit to the goal and have the ability to reach it (Locke and Latham).‍
2. Ask before you adjust. Don’t tailor your approach from a birth year or a job title. A meta-analysis of nearly 20,000 workers found generational differences in work attitudes to be small (Costanza et al.). Ask each person three things: Which part of this have you done before, and which part is new? How do you want feedback? What would stop you telling me something looks off?‍
3. Vary the support, not the standard. In research on learning, techniques that help inexperienced learners lose their effect with experienced ones, and can backfire (Kalyuga et al.). Give newer people examples, a checklist and scheduled check-ins. Role clarity is one of the things that carries new hires’ early adjustment through to satisfaction, performance and staying (Bauer et al.). 
   Give experienced people the outcome, the deadline and room to choose the route. Managers who support autonomy have teams with stronger self-directed motivation and less distress (Slemp et al.). Adjust by task, not by person. Even your veteran may be new to your tools.‍
4. Say what’s the same, what’s different and why. “Everyone owns the Thursday deadline. Chris gets a weekly walkthrough this quarter because the client work is new to her. Dev brings me the plan and the risks, and I stay out of the how.” Said out loud, a difference in support reads as a decision you made for a reason.‍
5. Make it safe to push back. Invite a reaction from each person, starting with the newest or quietest. Leaders whose words and actions invited and appreciated others’ input had teams with higher psychological safety, and it softened the effect of status differences (Nembhard and Edmondson). Ask what they’d change, then use something they say.

How to use an AI coach to set expectations with your team

To set expectations with an AI coach, use one that plays the person on the other side, not one that only drafts your memo. It should ask about each person, help you separate what stays fixed from where your support flexes, and respond the way they might. Open Huckleberry and say:

“I’m setting expectations with my team. These are the same for everyone: [outcomes, standards, behaviors]. Here are the people: [name or role, how long they’ve done this work, what’s new for them, how they take feedback].

First, ask me what you need to know about each person. Then help me work out what stays the same for everyone, where my support should differ, and why.

Next, play [person] and respond to how I explain their expectations, one reply at a time.

Then tell me where I was vague, where I assumed, and where my approach would feel unfair to someone else on the team. Let me try again.”

Try the two-person test first. Pick the two people furthest apart in experience. Write the same expectation for both in one sentence, then one sentence on the support you’d give each. Run both through your coach and listen for the reply that surprises you.

Still choosing a tool? Compare the main AI coaching platforms.

Rehearsed first, more confident going in

One Huckleberry user was preparing their yearly expectation-setting conversations about the year ahead. They used the coach to plan approaches for different types of people on the team, rather than for individuals: a couple of people early in their careers, and a few who’ve been with the company more than 20 years. 

These are different development conversations, and the coach helped them talk through an approach and plan how to draw out each person’s own thinking about their development. They said Huckleberry really helped them to consider different angles.

Picture your version. Before the kickoff, you describe the people furthest apart in experience and hear how each might take the same standard. 

Presenting your plan to senior leaders next? Browse more Huckleberry use cases.

Sources

• Building a practically useful theory of goal setting and task motivation: A 35-year odyssey, Locke & Latham, 2022.
• Generational Differences in Work-Related Attitudes: A Meta-analysis, Costanza et al., 2012.
• The Expertise Reversal Effect, Kalyuga et al., 2010.
• Newcomer adjustment during organizational socialization: A meta-analytic review of antecedents, outcomes, and methods, Bauer et al., 2007.
• Leader autonomy support in the workplace: A meta-analytic review, Slemp et al., 2018.
• Making it safe: the effects of leader inclusiveness and professional status on psychological safety and improvement efforts in health care teams, Nembhard & Edmondson, 2006.

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