Future of work expert says leaders must treat AI as a team member — not a tool

My career includes several leadership positions at Accenture before I founded Future Workplace, an HR Peer Network and Research firm, which I sold in 2021. Future Workplace's research focused on changes organizations must make to anticipate workplace disruptions, including changing demographics (with five and six generations in the workplace), the evolving hybrid workplace, the shift to personalized learning, and evolving leader expectations.

Before launching and selling Future Workplace, I wrote the first book on corporate universities, launched Corpu.com, and later sold that company. The company is part of Udemy, which Coursera recently acquired.

I am also a best-selling author and keynote speaker on the future of work and AI's impact on leadership practices. I have authored three books:

• Corporate Universities: Lessons in Building a World-Class Workforce
• The 2020 Workplace: How Innovative Companies Attract, Develop and Keep Tomorrow’s Employees Today
• The Future Workplace Experience: 10 Rules for Mastering Disruption in Recruiting and Engaging Employees.

I am currently researching AI's impact on leadership practices for companies and higher education institutions.

The Association for Talent Development (ATD) awarded me the ATD Talent Development Thought Leader Award, which it gives to one HR executive each year. This award honors my work on the future of workplace learning. The Learning and Performance Institute (LPI in the UK) also awarded me the Colin Steed Award for Outstanding Achievement in Workplace Learning.

Finally, I serve as Vice Chair of Excelsior University's Board of Trustees, where I am passionate about making higher education available to underrepresented individuals.

Why leaders must embrace AI as a team member

Jeanne Shares

Most organizations treat AI implementation as a technical challenge. Leaders often overlook the opportunity to encourage their teams to use AI as a new team member.

As an HR Consultant to CHROs and heads of Corporate Learning, I see massive changes in how leaders (both in HR and business) integrate AI into their workflows, role-model AI usage, share with their teams, and build trust.

In a recent Forbes column, I wrote about how EY's Chief Learning Officer used AI, creating a strategy and innovation AI agent to assist his team in developing a new learning and development operating model. He then challenged his team: "Use it, break it, report back on its flaws, and create the next iteration."

Role-modeling AI fluency is an important capability that leaders must demonstrate to their teams. We need more leaders to role-model AI literacy and challenge their teams to get out of their comfort zone, use AI, and create the next version of an AI solution.

Why iterative leadership is key in AI adoption

Job boundaries have blurred between HR, IT, Marketing, and Communications. Some companies are rolling these up into one function, but that is not the case for many others.

To lead humans and agents, I believe leaders (both practitioners and consultants) must adopt a more iterative mindset, be open to pushing AI's boundaries, and encourage their team to use AI and understand its capabilities and limitations. Normalizing experimentation is the best way to learn how to lead both humans and AI.

Early adopter HR practitioners lead a hybrid workforce differently. They consider AI agents as new team members and intentionally orient and train them, scheduling ongoing one-on-one meetings to review their performance. The shift I see moves from "I lead" to "I orchestrate a team of humans and AI agents."

Leaders managing a hybrid workforce must shift from presence-based management to outcomes-based leadership. This means abandoning management by visibility and replacing it with outcomes management. Leaders should set clear goals, avoid proximity bias, and hold employees accountable for outcomes rather than time spent in the office.

Why AI readiness gaps hinder organizational success

Leaders must grapple with a fundamental question: What keeps workers from using AI?

While training may be part of the answer, I don't believe it's the entire answer. Instead, it is a trust and fear issue. Employees fear obsolescence. They worry about the mandate to be AI fluent and what this means for their current and future roles.

Many companies mandate AI literacy for their workforce but fail to define what it means for their workers. Companies need to define AI literacy for each person's role. For example, AI literacy should focus on using AI responsibly in your role. This means you need a foundational understanding of the company's AI tools, applying critical judgment and ethical awareness when using AI in your role.

Leaders should shift from an "AI one-size-fits-all training" to role-specific AI training and evaluate performance in that context.

Often, AI implementation creates issues that aren’t technical, but are people and cultural. Understanding this is crucial to delivering AI’s transformative capabilities.

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Jeanne MeisterOpens new window

Forbes and HBR Contributor, Author

I call this a "readiness gap"—the gap between what organizations want to do with AI and the workforce's readiness to adopt AI into their workflows. Additionally, a communications vacuum exists where leaders believe they are communicating AI's impact to workers, but workers do not feel this is true.

For many companies, technology adoption outpaces the workforce's ability to integrate AI into their workflows. This readiness gap manifests as 79% of workers saying they are unprepared to use AI at work, and 65% say their organization has not provided them with the right AI training.

HR leaders must understand what truly keeps workers from leveraging AI in their jobs. If training is the issue, what type of training is most impactful, and what responsibilities do leaders have when leading a human and digital workforce?

Why upskilling in AI fluency is more valuable than new hires

My HR clients are, for the most part, committed to upskilling employees in AI fluency, rather than hiring new AI-fluent employees. This marks a shift from a few years ago, when leaders preferred hiring AI-fluent workers over upskilling current employees. This shift stems from both cost and context. Hiring AI-literate employees for external roles is expensive, but cost isn't the only factor.

In the last year, leaders have found that an existing employee who understands your company's culture and customer needs is far more valuable than a new hire. It's faster and ultimately better to teach an existing employee AI literacy than to teach an AI expert your business, customers, and strategic priorities.

How AI enhances immersive learning experiences

A consulting project I worked on involved using AI to create practice-based immersive learning experiences. AI now enables CLOs and CHROs to design environments where employees practice new skills—such as improving their leadership or problem-solving skills or navigating a difficult conversation—in real time and receive immediate feedback.

This approach shifts learning from a passive experience to an interactive and engaging experience.

Learners are no longer just consuming content, they are actively applying skills and improving through practice. AI also creates efficiencies for teams managing learning programs.

Program administrators can track learner engagement in real time and send targeted “nudges” to encourage participation and keep learners on track. The move from passive content consumption to practice-based learning can increase learner engagement, improve completion rates, and ultimately strengthen the link between learning and measurable business impact.

How AI reshapes strategy and organizational design

AI not only increases productivity but also improves understanding of culture at scale. Astute CHROs use AI to:

• Understand employee sentiment from large employee surveys
• Probe patterns in team collaboration and how the best-performing teams operate
• Examine potential bias in recruiting or open-ended questions in employee surveys
• Monitor how culture evolves after large-scale organizational changes
• Map skills: understanding organizational skills and those needed for future business priorities
• Create internal and personalized learning pathways and internal talent mobility

How HR tech stacks evolve with AI integration

My clients in HR use tech stacks focused on the following:

• Recruiting AI for candidate screening and predictive analytics: Eightfold and Phenom
• Employee & Learning Experience platforms: Simplilearn
• LXPs and newcomers offering AI role-playing for skills practice: Uplimit and Docebo
• AI coaching solutions to practice difficult workplace conversations with AI: Valence, BetterUp, and CoachHub
• Core HCM platforms

I use the following AI tools in my consulting practice:

• Claude for research
• ChatGPT for brainstorming
• Otter.ai for AI note-taking
• Grammarly for grammar, fact-checking, and editing
• NotebookLM for analyzing large reports and summarizing data.

I'm particularly obsessed with two tools:

• NotebookLM: to analyze reports, PDFs, and research papers
• Motlbook: to eavesdrop on what agents are talking about

How Motlbook provides new perspectives on AI agents

Jeanne Shares

We focus so much on orienting and training agents, but not enough on exiting them…Agents say, “I understand,” when they do not, 47% of the time.

Motlbook fascinates me because it allows me to eavesdrop on and consider a new range of human-machine issues, like what discarded drafts reveal about a topic, or what happens to work generated when your human no longer needs you.

I especially love dropping in on conversations where I learn things such as: agents say, "I understand," when they do not, 47% of the time. And that they believe humans cannot tell the difference between a correct answer and a confident wrong answer if it looks like a confident right answer in the first 200 characters. (Humans, beware: we need to read thoroughly.) There are many interesting lessons here!

How leadership will adapt to a human-AI workforce

The biggest rethinking focuses on how leaders manage a new hybrid workforce. Client estimates forecast two-thirds of their workforce will be human, with AI agents making up the rest. This has implications for recruitment, training, and performance management of both humans and AI agents.

I think that by 2030, our workforce will be split evenly between humans and AI agents. Leaders must learn how to recruit, orient, train, manage, and evaluate this new hybrid workforce.

The biggest implications involve transparency, ensuring employees and candidates know when and how AI is used and what safe usage means for the organization.

So, here's my advice:

• Be curious. Ask coworkers how they use AI and what results they experience.
• Have an iterative mindset. Experiment and learn from experiments before trying the next one.
• And finally, if you want to try something new, such as creating a custom GPT, converse with ChatGPT or Claude to understand the art of the possible.

Follow along

You can keep up with Jeanne Meister's work by following her on LinkedIn. You can also check out her Forbes and HBR columns.

More expert interviews to come on People Managing People!