Webinar recap: Reverse mentorship — why you don’t have to figure AI out alone.

This webinar explores how reverse mentorship can help employers and students navigate AI adoption together. Speakers Amanda Betts, Riipen’s VP of Marketing, and Victoria Hefty, who leads Riipen’s Student Success team, discuss the growing pressure organizations face to adopt AI, the gap between student AI skills and real-world application, and how project-based work can connect these needs. They highlight how clearly defined projects can help employers test ideas, advance priorities, and gain visibility into emerging talent—while giving students meaningful opportunities to apply their skills, build confidence, and develop professional evidence.

Riipen recently hosted an insightful webinar titled “Reverse Mentorship — Why You Don’t Have to Figure AI Out Alone,” featuring Amanda Betts, Riipen’s VP of Marketing, and Victoria Hefty, who leads Riipen’s Student Success team. Together, they explored a question that many employers and students are probably asking right now: How do we actually move forward with AI when no one has all the answers?

Rather than focusing on AI as something organizations need to figure out on their own, Amanda and Victoria looked at how employers and students can bring different strengths to the table. The session explored the growing pressure to adopt AI, the skills gap between education and the workplace, and how real-world projects can create a practical path forward for both sides.

Watch the full webinar recording:

The pressure to figure out AI is real.

The webinar opened with a feeling that will probably sound familiar to many organizations: someone walks into a meeting and asks, “What are we doing about AI?”

The question seems simple, but the answer rarely is. Most teams aren’t sitting around waiting for a new initiative to take on. They’re already managing customers, deadlines, products, budgets, and a long list of priorities. Even when teams know there are opportunities to use AI, finding the time and focus to explore those opportunities responsibly can be difficult.

Amanda and Victoria discussed the tension between wanting to move quickly and ensuring AI is used in ways that are actually useful, accurate, and responsible.

Data shared during the session illustrated the pressure on employers to act. Microsoft’s 2026 Work Trend Index, which surveyed 20,000 AI users across 10 countries, found that 65% feared falling behind if they didn’t adapt quickly. At the same time, 45% said they felt safer focusing on current goals rather than redesigning their work with AI, while just 13% said they were rewarded for reinvention when an initial result didn’t meet expectations.

Students are experiencing a related challenge. The Digital Education Council’s 2026 global survey, which included more than 27,000 active student responses, found that only 15% of students said AI was integrated into their coursework.

Riipen’s own research also found that more than 76% of employers prioritize AI skills, while 42% had not considered student-supported projects or Riipen as part of their AI strategy.

Together, these numbers point to a clear opportunity. Employers are looking for AI skills, students are developing them, but there isn’t always a practical bridge between the two.

Key takeaway: The pressure to adopt AI is real, but organizations don’t have to figure everything out before they begin. Real-world projects can create a practical, lower-risk way to explore what AI can actually do.

Rethinking who brings the expertise.

The conversation then turned to one of the central ideas behind reverse mentorship: expertise doesn’t always come from one person or one place.

There is a common assumption that AI-related work needs to be led by a senior AI specialist, a technical hire, or an external consultant. While there are certainly projects that require that level of expertise, many organizations are starting with much more practical questions.

Could AI help improve a workflow? Can customer feedback be analyzed more effectively? Is there a process that could be streamlined? Could an organization test a new tool before making a larger investment?

These are the kinds of questions that can often be turned into well-defined student projects.

Employers bring something students can’t easily replicate in a classroom: business context, customer knowledge, organizational priorities, quality standards, and responsibility for the final decision. Students, meanwhile, bring current skills, curiosity, fresh perspectives, research capabilities, and hands-on experience with emerging tools.

That combination is what makes reverse mentorship so valuable.

As Amanda explained:

“The question we want to explore today isn't necessarily who knows the most about AI. It's really about how can the right combination of skills, context, and judgment help solve a problem.”

Victoria also spoke to what students are looking for. Students may know how to use AI for research, coding, design, or other types of work, but they don’t always have an opportunity to apply those skills to a real business problem with real consequences.

“What students really want is an opportunity to show that they can understand a problem, take responsibility for contribution, and deliver something that matters so much more beyond the classroom.”

Key takeaway: Reverse mentorship isn’t about making students the “AI experts.” It’s about bringing together diverse expertise so employers and students can learn from each other while advancing meaningful work.

Turning AI skills into real business impact.

The speakers then explored what happens when those different strengths come together around actual business priorities.

A well-scoped student project can help an employer make progress on work that may have been sitting on the roadmap for months. It could be market research that hasn’t had the bandwidth to get done, customer feedback that needs to be analyzed, a product feature that keeps getting pushed back, or an AI use case that needs to be tested before the organization invests more heavily.

The key is that the work is real.

For employers, this can mean getting a useful deliverable, testing an assumption, reducing uncertainty, or simply learning more before making a larger investment.

For students, it creates something equally valuable: evidence.

Instead of saying, “I know how to use AI,” a student can explain the business problem they worked on, what they were responsible for, how they approached the challenge, what tools they used, how they validated their work, and what the organization was able to do with the result.

The World Economic Forum’s Future of Jobs Report 2025 found that 63% of surveyed employers viewed skills gaps as a primary barrier to business transformation. Project-based work provides a way to connect those skills to real business needs and gives employers an opportunity to see emerging talent in action.

Victoria emphasized that students should leave with a strong project that explains not just what they did, but why they did it and what changed because of it.

Key takeaway: Real projects turn skills into evidence. Employers get meaningful work done and a chance to see emerging talent in context, while students gain experience they can confidently speak about beyond the classroom.

A real-world example: My AI Pathway.

The webinar brought this idea to life through the experience of Riipen employer My AI Pathway, led by founder and CEO Claudius Thomas.

My AI Pathway is developing FRED, an AI-driven marketplace that connects farmers and food entrepreneurs with customers. Rather than creating projects simply as learning exercises, Claudius turned genuine business priorities into defined projects covering areas such as market research, user experience testing, product development, mobile deployment, and AI-related data work.

According to Claudius’s estimate shared during the webinar, the student support helped the company avoid falling approximately two years behind its roadmap.

One of the most interesting examples involved FRED’s ability to understand different names and terms for agricultural products across communities and regions.

The company initially tested a general-purpose AI translation service, but the results showed why human context matters. One example discussed during the webinar involved the term ewedu, which should have been identified as jute leaves but was instead returned as cucumber.

The technology produced a confident answer—but it wasn’t the right answer.

That distinction is incredibly important when working with AI. A tool can generate an answer quickly, but it doesn’t necessarily understand the cultural, linguistic, customer, or industry context behind that answer.

My AI Pathway created a defined project through Riipen and worked with students to build the dataset needed for more accurate, dialect-aware functionality. Claudius brought the product vision, customer knowledge, language context, and understanding of what an accurate result needed to look like. Students brought the technical skills and focused project support needed to structure and test the data.

The resulting dataset became the basis for a language capability the company was developing.

Key takeaway: The strongest AI projects combine technology with human context. Students can bring valuable technical skills and fresh thinking, while employers provide the real-world knowledge needed to determine whether the work is accurate, relevant, and useful.

Responsible AI starts with human judgment.

A major theme throughout the webinar was that using AI responsibly doesn’t mean using AI everywhere.

The goal isn’t to add AI simply because it’s available. It’s about understanding where it can genuinely help—and recognizing when it shouldn’t be used.

Claudius captured this approach with a simple but powerful statement:

“I encourage students to use AI, but the work they present they must own.”

That ownership matters.

Students need to understand the sources behind their work, explain their methodology, verify AI outputs, identify limitations, and be able to answer questions about the decisions they made.

Amanda also highlighted Microsoft research showing that 86% of surveyed AI users viewed AI output as a starting point rather than a final answer.

That idea applies directly to project-based work. AI can assist with research, analysis, coding, drafting, and other tasks, but people still need to evaluate the results and take responsibility for what ultimately gets delivered.

Sometimes the most responsible decision may even be not to use AI. If information is too sensitive, the output isn’t reliable enough, or a decision requires human judgment, that needs to be recognized as part of the process.

Key takeaway: Responsible AI isn’t about maximizing AI use. It’s about using it thoughtfully, checking the results, understanding its limitations, and keeping people accountable for the final work.

Finding the right starting point.

The speakers outlined three practical ways employers can think about starting an AI-related project: discover, test, and improve.

At the discovery stage, an organization may know that a workflow is repetitive, inefficient, or frustrating, but isn’t sure whether AI is the right answer. A student project could help map the process, research available approaches, identify potential use cases, or determine whether AI is even necessary.

At the test stage, an organization may have a more specific idea. Students could analyze customer feedback, test an AI-assisted workflow, compare different tools, or evaluate where an AI output becomes unreliable.

At the improve stage, an organization may already be using AI and want to improve an existing system or workflow. Projects could focus on user experience, quality assurance, governance, market research, workflow integration, or other areas that strengthen what is already underway.

The important thing is that AI-related work doesn’t automatically mean hiring an AI engineer.

Organizations also need people who can understand users, conduct research, analyze workflows, communicate findings, identify problems, and recognize when a tool is actually helping.

For students, Victoria offered a helpful way to think about it: instead of asking, “Am I technical enough to work on AI?”, ask, “What skills does this business problem require, and how can I contribute with what I know?”

For employers, the question is similarly practical: instead of asking whether the organization is “advanced enough” to host an AI project, ask “What defined piece of work could a capable student help us move forward?”

Key takeaway: You don’t need a perfect AI strategy to get started. A clear business problem and a well-defined project can be enough to take the first step.

Wrap-up & resources.

The webinar concluded with a simple message: neither employers nor students have to figure out AI alone.

A well-prepared employer creates the conditions for students to contribute effectively by clearly defining the problem, the desired outcome, the deliverables, the available information, and the boundaries. This gives students enough direction to understand what success looks like while still giving them room to think, experiment, and take ownership.

For students, the opportunity is to recognize that they don’t need to arrive as AI experts. Their existing skills, curiosity, willingness to learn, and ability to take responsibility can be just as valuable.

Ultimately, reverse mentorship creates a relationship where both sides have something to gain. Employers can make progress on real business priorities while getting a clearer view of emerging talent. Students have the opportunity to apply their skills to work that matters and to build a professional story based on what they have actually accomplished.

As Victoria summed it up:

“A well-prepared employer creates the conditions for students to contribute effectively. And a well-prepared student turns that opportunity into work that the employer can genuinely use.”

Want to keep learning? You can catch the replay of this webinar and explore previous Riipen webinars here.

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About the author:

Bisola Ogamba is the Senior Manager of Programs, Communications & Content at Riipen, where she supports program communications and content while leading the organization's events and partnerships initiatives. She is passionate about creating engaging experiences and meaningful collaborations that connect learners, educators, employers, and partners with Riipen's mission. Through strategic communications, impactful events, and strong partnerships, Bisola helps elevate awareness of Riipen's programs and expand access to work-integrated learning opportunities across Canada.