Beyond Job Security: Employability in the AI Era

For more than 37 years, I have been in leadership, Board, investor, advisory, and advocacy roles focused on enhancing the critical role employers play in population health.

Public health researchers like Sir Michael Marmot, who popularized the concept of “social determinants of health,” have shown that career wellbeing is foundational to employee health. Former Gallup executives Tom Rath and Jim Harter, in Wellbeing: The Five Essential Elements, went further, arguing that career wellbeing may be the first among equals in ranking the five essential elements.

If that is true, then artificial intelligence—by upending how we define and sustain work—poses one of the most urgent challenges to human health and corporate responsibility.

In every era of technological disruption, fear has been constant. But AI is different: faster, deeper, and more personal. When it can outperform doctors at diagnosis, lawyers at document review, or analysts at financial forecasting, even highly educated professionals are asking: "Do I still have a future?"

For business leaders, the moral and strategic challenge is not to reassure employees that their jobs are safe—because many are not—but to define employability in a world where AI is rewriting what “work” even means.

1. From Job Protection to Capability Expansion

Leaders must abandon the illusion of lifetime job security and embrace an updated compact: employability. The goal is to help people become continually re-employable, inside or outside the organization.

When Pitney Bowes used employment transition training and counseling, we assumed that, while employees might not remain with us, they could find a job either exactly like the one we were eliminating, or one reasonably adjacent to it. AI is cutting such a wide swath that big chunks of the kind of work employees do today will disappear across the board. They will have to think more expansively about what will be available and what they can do.

In the AI-first era, “employability” now means mapping human capabilities that technology cannot replicate—critical thinking, collaboration, contextual judgment, empathy, storytelling, and ethical reasoning—to the emerging tasks that AI will amplify rather than erase. Every role must be reimagined as a learning journey.

A modern enterprise must evolve its employees' capabilities and match them to emerging requirements, not preserve outdated job structures.

What do employees and those who guide them need to understand about AI?

As a practitioner of AI software-as-a-service, I have learned a few things about it:

1. Asking the right questions is as important, if not more important, than knowing the right answers. AI’s risk and opportunity is that it gives those without prior domain knowledge an ability to find a path to learning by questioning large language models until they yield understandable and defensible answers.
2. Mastery matters less than continuous learning, because what we know today is much less settled than it once was.
3. AI connects more data from much bigger systems and bodies of knowledge; humans need to understand the bigger systems in which AI can operate to spot patterns humans take much longer to see.
4. AI should drive work transformation, not automating what we do today. Humans need to think ahead about what that transformed workflow looks like and find their place in it.

Building Transitional Pathways

When the goal is to transform work, as opposed merely to automating it, leaders must build bridges to the newly transformed work. AI transformation should create transitional work pathways that redeploy employees into new projects instead of discarding them.

A customer service representative might train chatbots for usability; a marketing professional can do more rapid testing of different messages, channels, and pricing schemes; an auditor might use AI to enhance forensic analysis.

These bridge roles send a powerful message: you’re not disposable—you’re developing.

2. Make AI Literacy Universal

AI fluency must become as fundamental as spreadsheet literacy once was. Every employee, regardless of title, should understand how to use AI as an assistive tool, not fear it as a competitor.

Organizations can host AI learning and creative sessions tailored to specific functions—marketing, logistics, HR, compliance—focusing on real performance gains. Shared learning sessions, where employees share what they’ve discovered, can democratize expertise and dispel the myth that only data scientists can thrive.

Training must also strip away jargon. When someone claims not to use AI, I ask whether their phone suggests words or phrases as they type. When they say yes, I remind them: that’s predictive AI. Making the invisible visible helps demystify the technology and lower fear.

3. Reward Learning Agility, Not Tenure

Fear flourishes when employees believe they are judged only on current output, not future potential. Leaders can counter this by changing what they measure.

Incorporate learning adaptability into performance reviews. Reward those who experiment, learn new tools, and cross functional boundaries. Publicly celebrate reinvention—someone moving from finance to data science, or from field sales to digital marketing. Each success story becomes a morale vaccine against despair.

4. Recognize That Individuals Learn Differently

We learned at Pitney Bowes that one-size-fits-all training doesn’t work. Some people learn best by reading, others by listening, watching videos, or manipulating physical objects. Understanding how people learn is as critical as knowing what they must learn.

AI can actually assist in this assessment. Adaptive learning systems can tailor modules to a person’s preferred mode and pace, reinforcing knowledge through multiple channels. Using diverse formats—text, visuals, discussion, simulation—ensures deeper retention and broader inclusion.

5. Lead with Psychological Safety and Honesty

Technology transformations fail not because of technical complexity but because of emotional opacity. People can handle bad news; they cannot handle unknowns. Transparent communication—regular briefings about which functions are changing, why, and on what timeline—builds trust.

Create open forums where employees can voice fears without stigma. The goal is not to soothe people into complacency but to help them channel anxiety into curiosity. The healthiest organizational question becomes: What can I learn next?

6. Rebuild Employability Beyond the Company

Leaders have a broader obligation: to ensure employees remain valuable to the wider economy, not just their current employer. Partner with local governments, universities, and nonprofits to build AI-reskilling hubs that keep communities resilient.

Some argue that helping workers become employable elsewhere does not enhance shareholder value. The opposite is true. Investing in shared human-capital infrastructure generates positive network effects—sustained consumer demand, a stronger reputation, and reduced social volatility.

Handled skillfully, even reductions in force can reaffirm organizational values. When leaders demonstrate empathy, fairness, and accountability, employees remember that the company lives its principles. Supporting community partnerships during transitions reinforces moral credibility and brand trust—both essential to long-term shareholder returns.

7. Model What You Preach

Executives cannot delegate curiosity. They must learn in public—demonstrating how they use AI to improve decision-making, simplify communication, or deepen insight. When leaders share their own learning journeys, they grant permission for everyone else to start theirs.

The message is powerful: if the CEO is experimenting, so can I.

The New Social Contract

In the age of AI, leadership will not be measured by how many jobs are preserved, but by how many futures are prepared. The companies that thrive will be those that transform fear into agency and anxiety into adaptive intelligence.

If leaders act with empathy, transparency, and courage, the central question for employees will shift from “Will I have a job?” to “How will I keep growing?” That shift is not just a moral imperative—it’s a strategic one.

A workforce that believes in its own resilience produces stronger innovation, deeper engagement, and lasting shareholder value.

AI is rewriting the nature of work, shifting leadership’s responsibility from preserving jobs to preparing people for continuous reinvention. Companies that build human capability, enable learning agility, and create honest, psychologically safe cultures will transform fear into engagement. Those that invest in employability today will thrive tomorrow.