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Mentoring in the Age of AI: Why Gen Z Wants Mentors, Not Just Answers
Mentoring in the age of AI is the practice of pairing employees with human mentors for the parts of career growth that AI cannot handle, such as sponsorship, advocacy and navigating unwritten rules, while AI handles routine questions, session preparation and program administration.
AI has not made mentoring less important. It has made mentoring more focused.
For learning and development (L&D) teams, the shift is practical. Roles, teams and skills now change faster than an annual mentoring cycle can follow. Your mentoring program needs to keep up.
What Mentoring Looks Like in the Age of AI
AI handles transactional guidance. Explaining concepts, reviewing drafts and rehearsing conversations no longer require a mentor.
Mentors handle what AI cannot. Sponsorship, institutional knowledge and lived experience still come from people.
Gen Z demand is high. 86% of Gen Z respondents in Deloitte's 2025 Gen Z and Millennial Survey emphasized the need for mentorship and guidance.
Static programs fall behind. Annual, spreadsheet-based matching cannot keep pace with changing roles and skills.
Dynamic matching closes the gap. Multi-criteria matching on current HRIS data keeps mentoring pairs relevant as the organization changes.
What Does AI Change About Mentoring?
AI changes what mentoring time is spent on, not whether mentoring is needed.
Generative AI is already part of the early-career workday. According to Deloitte's 2025 Gen Z and Millennial Survey, 57% of Gen Z respondents use generative AI in their daily work to some extent.
An employee can now ask an AI assistant to explain a concept or review a draft at any hour. That frees mentoring for the work only people can do:
What AI handles | What human mentors handle |
Explaining concepts and processes | Sponsoring someone for a stretch assignment |
Reviewing drafts and resumes | Explaining unwritten rules and team dynamics |
Rehearsing difficult conversations | Introducing mentees to decision-makers |
Answering questions at any hour | Sharing lived experience of setbacks and burnout |
An AI tool cannot put its reputation behind someone. A mentor can.
What Does Gen Z Want From a Mentor?
Gen Z wants mentoring that is personal, frequent, practical and reciprocal.
The demand is clear. In Deloitte's 2025 survey of 23,482 respondents across 44 countries, 86% of Gen Z respondents emphasized the need for mentorship and guidance.
The supply is not. Gen Z is not looking for occasional advice from a distant senior leader.
They expect:
Personal connection: a mentor who cares about them as people, not only about their output
Frequent conversations: regular development time, not feedback saved for an annual review
Practical guidance: help with real work, career paths and networks (88% of Gen Z respondents in Deloitte's survey value on-the-job learning and practical experience)
Two-way learning: reverse mentoring, where they share perspective on new tools and digital culture
Purpose and belonging: a clear line between their work and its impact
Managers cannot carry all of this alone. Deloitte, citing its Human Capital Trends research, reports that managers spend only about 13% of their time developing their people.
A structured mentoring program makes growth part of how the organization works, not a matter of luck.
How to Build a Mentoring Program for the AI Era
These six steps help L&D teams move from static, annual mentoring to a dynamic program that adapts as the organization changes.
1. Retire the Annual Spreadsheet
Static programs follow a familiar pattern. One sign-up survey. Spreadsheet matching. Pairs that run for a year with little follow-up.
That model assumes roles stay still. They no longer do. In Deloitte's survey, 59% of Gen Z respondents said generative AI skills are somewhat or highly required for career advancement, and 86% said the same of soft skills.
If matching happens once a year, the program is already out of date.
2. Match on More Than Job Title
Strong matching looks at several criteria at once:
HR data: job level, department, location, time zone and language
Survey data: skills offered, skills wanted, development goals and preferred format
Program rules: keeping mentors within two job levels of mentees, for example, or making matches cross-functional on purpose
Pull this data directly from your HRIS. Matching should reflect today's organization, not last year's.
3. Keep Program Leads in Control
Algorithms recommend. People decide.
Set a minimum match quality, review suggested pairs and match remaining participants by hand. This matters most in programs where every pairing counts, such as leadership or high-potential tracks.
4. Offer More Than One Format
One format does not fit every goal:
One-to-one mentoring for career direction
Peer mentoring for onboarding and shared challenges
Group mentoring and mentoring circles for reach at scale
Flash mentoring for quick, focused advice
Reverse mentoring for two-way learning on new tools
5. Use AI to Support Pairs Between Meetings
Busy mentors run out of things to discuss. AI can suggest agendas, icebreakers and follow-up actions. It can also flag pairs that have stopped meeting before they drop off entirely.
AI should make mentoring conversations better, not replace them.
6. Measure What Mentoring Changes
Participation counts are not enough. Connect mentoring data to retention, internal mobility and engagement so you can show leadership what the program delivers.
Why Enterprises Choose Teleskope for Mentoring
Teleskope's mentoring software, is built for dynamic mentoring at enterprise scale:
Configurable matching: use any HRIS attribute from systems such as Workday, SAP or ADP, or custom attributes from a program join survey, and set each to match, not match or be ignored
Transparent recommendations: participants who choose their own mentor see a match score and how it was calculated
AI Team Builder: program leads set team composition and a minimum match threshold, accept or reject suggested matches, and pair anyone left over with drag-and-drop
Every program format: run one-to-one, peer, group, circles, flash, reverse and networking programs on one platform
AI support with Ask Skope: generate icebreakers and agendas, flag pairs that have gone quiet and turn program data into reports
The results show at scale. One Fortune 500 financial institution grew mentoring participation from 10,000 to more than 30,000 users and consolidated 250 programs into three.
Book a personalized demo to see how Teleskope can support your mentoring programs.
Frequently Asked Questions (FAQs)
What is mentoring in the age of AI?
Mentoring in the age of AI is a model where AI handles routine questions, session preparation and program administration, while human mentors focus on sponsorship, advocacy, institutional knowledge and lived experience. Programs also use current HR data to match and rematch participants as roles and skills change.
Will AI replace workplace mentors?
No, AI will not replace workplace mentors. AI can answer questions, help employees prepare for conversations and reduce program administration. It cannot sponsor an employee, explain unwritten rules or share lived experience. Those remain the work of human mentors.
What does Gen Z want from workplace mentoring?
Gen Z wants workplace mentoring that is personal, frequent, practical and reciprocal. In Deloitte's 2025 Gen Z and Millennial Survey, 86% of Gen Z respondents emphasized the need for mentorship and guidance. They value mentors who invest in their growth and are open to learning from them in return.
What is dynamic mentor matching?
Dynamic mentor matching pairs mentors and mentees on several criteria at once, such as job level, skills, goals and time zone, using current HRIS and survey data. Program leads can review, adjust and rerun matches as roles and goals change.
How can L&D teams use AI in mentoring programs?
L&D teams can use AI in mentoring programs to recommend matches, generate session agendas and conversation prompts, flag pairs that have stopped meeting and turn program data into reports for leadership.
About the author
Priyanka Gujar is a Senior Marketing Manager at Teleskope who has covered HR technology since 2022. She holds an MBA in Marketing and writes about ERG, mentoring, and employee experience programs, translating platform capabilities into practitioner guidance. Read more about Priyanka →
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