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1. Create a lightweight AI infrastructure your team can use

You do not need a formal enterprise AI implementation to set expectations for your team.  Start small to promote acceptable and responsible use:

• Quality Prompts – Teach your team to ask clear, goal-driven questions so that AI returns useful information. This free certificate includes prompting courses.
• Model Acceptable Use – Share when and how you use AI to model acceptable behavior and norms.
• Peer Pairing – Pair someone skilled and comfortable with AI to guide someone less enthusiastic or insecure.

These small steps build confidence and reduce misuse long before formal policies from your organization are implemented and communicated.

2. Actively manage Shadow AI and Communicate Guardrails

67% of employees engage in Shadow AI use – using unapproved AI tools and chatbots at work without IT or security approval.  This widespread practice puts an organization’s security in jeopardy.

If your organization has policies and structure for AI, communicate them clearly and consistently. If it doesn’t, establish common-sense guardrails:

• Authorization – Be clear on which tools are acceptable and which ones are not
• Boundaries – Establish what data can and cannot be entered
• Compliance – Verify why and when human review and monitoring is required

Clear expectations reduce accidental security exposure and build trust with AI tools.

3. Build a Safe-to-Fail, Risk-Tolerant Culture

Innovation requires a safe culture, and AI experimentation is no exception.  View this past webinar or view this PDF for Innovation strategies.

• Share – Talk about mistakes you’ve made, what you learned, and how you apply those lessons to future work.
• Ask for Help – Name colleagues you used for troubleshooting and give credit to them for that assistance.
• Make Mistakes OK – Normalize ‘trial and error’ to reduce the fear of mistakes. Employees who fear the repercussions of their mistakes may try to cover them up which can only exacerbate the situation rather than repair it.
• Celebrate Learning – Provide professional development time to emphasize that we all need to keep learning in the workplace.

A team that feels safe experimenting will adopt AI more thoughtfully and creatively.

4. Limit Time Spent in AI to Prevent Burnout

AI load can lead to burnout in 2 different ways.

1. Hyper-loop multitasking – monitoring multiple AI inquiry loops at the same time. This constant rotation prevents your team from doing deep thinking. (Those of you who are regular readers already know that I believe that multitasking is a myth even without AI).
2. Endless Open Loops – AI tends to keep making additional suggestions for improvement that never allow for a task to be done. Most humans like closed loops (that’s why we finish watching a mediocre Netflix series) and will keep revising long past the point of worthwhile returns.

Help your team set boundaries to prevent AI Burnout:

• Goals – Use SMART goals to define AI use, objectives, and outcomes for your team so that AI contributes to production rather than creating the illusion of being busy.
• Use Higher Level Thinking Skills – Teach your team to stop continuous prompting and switch to synthesis – thinking, reflecting, and evaluating the AI response already received.
• Pause-Plan cycles for your team that provide natural pauses in the workflow before prompting AI again.
• Monitor – Pay attention to monitor if AI is helping production or just creating noise and additional stress.

Burnout isn’t created by the use of AI – it’s generated by the unmanaged use of AI and unclear AI expectations.

5. Protect Human Judgment

Your most important leadership responsibility in the AI era is protecting human judgment.  Judgment starts with the prompt and is used through the analysis of the AI response.

• Goal Definition – Encourage your team to understand their goal and outcomes, and include that information, along with their audience, in their AI prompt.
• Evaluation – Emphasize that you expect your team to evaluate AI-assisted work and not automatically accept what AI provides.
• Use Checklists – Provide your team with a review checklist:
  • Is this information accurate?
  • Is this information biased? Did your prompt introduce bias?
  • What criteria am I using to choose from the AI-provided options?
  • How should I package the information to communicate it to others to assist with decision-making or assessing risk?

AI should assist humans, not replace them. Teams that demonstrate discretion preserve their value and strengthen organizational trust.

Article Resources:

Shin, J., & Sucher, S. J. (2026). AI Adoption Is Overloading Your Middle Managers. Harvard Business Review / Harvard Business School. [hbs.edu]

Field, E., Hancock, B., Imose, R., & Yee, L. (2023). Middle Managers Hold the Key to Unlock Generative AI. McKinsey & Company. [mckinsey.com]

Rodriguez Constable, C. (2026). What Middle Managers Need Most in the AI Era. Forbes. [forbes.com]

Sources Used for Researching AI in the Workplace

1. Your AI prompts determine your results.

To work with AI, you’re going to need to use AI, so learning best practices for AI will be a good investment of your time.  Any use of AI is all about the prompt. I highly recommend this free course on AI prompting.  Even if you do the first 3 modules, it will make your use of AI more accurate and efficient. This “old” adage still applies: Bad data in; bad data out.

2. Your resume must pass an algorithm before it reaches a human.

While not every job you apply for may use an Applicant Tracking System (ATS), an estimated 90% of companies are using them including 99% of Fortune 500 companies.  These systems use algorithms to screen and score resumes much faster than human review.

This raises the importance of including the correct keywords in your resume. For example, if the job description says project coordination, and your resume says project support, the ATS may not match them — even though a human would.  ATS systems match patterns, not concepts. Even so, do not use the “kitchen sink approach” for resume keywords, since ATS’s have a 67% rejection rate of resumes that have 20+ skills listed.  Instead tailor each resume to each posting you apply to.

3. ATS parsing errors eliminate qualified candidates every day.
An ATS parses out a resume by extracting pieces of information by category. Parsing out, to me, is an old-fashioned phrase that my grandma would use (“I will parse out the strawberries so each of you gets the same amount.”) For resumes, the parsing categories are:
• Name
• Contact info
• Job titles
• Dates of employment
• Skills
• Education
• Certifications
If this information is presented in an unsupported format, such as a text box, it will be difficult for the ATS to find the information, and your resume may not pass ATS screening. The ATS rate of rejecting resumes due to formatting is 12% and for parsing issues (not being able to find the most necessary information for whatever reason), the rejection rate is 23%. To see if your resume is readable to AI, use a free resume checker. Some of those options are in the resource list at the end of the article.

4. AI can reduce bias — or amplify it.
One of the reasons employers want to use AI for screening is that it can reduce the bias that humans bring (often subconsciously) to the applicant screening process. This is good news for those who are most often eliminated due to bias; however, bias doesn’t disappear just because a machine is involved — it simply becomes harder to detect.

The bad data in, bad data out rule applies here as well.

The rules an ATS uses to screen candidates are set by humans. Unless those rules are set by a skilled person who is deliberately trying to remove bias, the algorithm might end up with the same biases as the humans involved. Currently, there are no existing laws or regulations for using ATS that ensure the process is equitable or even meets existing HR guidelines stated on an organization’s website.

As a job applicant, you cannot influence unconscious bias from an ATS any more than you can from human review. But the next time you apply for a job that you seem perfect for, and you do not move forward in the process, know that the candidate selection process, with or without ATS, is still far from perfect.

5. AI can help you prepare for interviews more effectively than ever.
Some organizations are using AI to conduct first-round interviews; therefore, they use AI to talk to AI. Use your newly acquired prompting skills to predict interview questions and prepare responses. If you can prompt better than your other candidates (and this includes using AI for company research), you will be better prepared. Do refrain from just having AI write your responses, or you will just sound like AI- and maybe like all the other candidates. Remember, the interview technique: STAR. State the situation or challenge; explain your task or role in the situation; describe the action you took and emphasize the result you achieved. This will help keep your interview answers focused on you rather than AI’s generic responses.

Sources Used for Researching AI in the Workplace