Originally published in HR Executive
Fears around the job market are mounting right now and for those actively searching after a layoff, recent graduation or life transition, the questions are urgent. Will I find work? Which majors are AI-proof? Which roles are already disappearing?
For those who aren’t searching, there’s an underlying version of the same fear. The loudest one—AI is coming for my job—has faded as AI adoption continues to drive increased, not decreased, workloads. What’s replaced it is a pervasive discomfort about having to adopt AI to stay relevant, and a real question about whether anyone can advance in their career without AI expertise.
The individual question is: “Is my career safe?” The team question is: Is my team built for what’s coming? The good news is that it’s answerable with data most organizations already have. Preparing for AI isn’t just about adopting new tools, but about making better decisions about hiring, team design, role definition and governance.
Here are five recommendations for building future-ready teams.
Treat hiring freezes as a diagnostic window
Here’s the reality of the current low-hire, low-fire labor market: 18 months of frozen hiring, suppressed quit rates and unfilled vacancies, with employers reaching their lowest hiring rate since 2013.
Most leaders pause on workforce decisions during hiring freezes, but what if leaders used this time as a diagnostic window instead?
During normal hiring churn, it’s harder to see how a team actually functions. Onboarding and backfill noise muddies the signal. Strip the noise out, and examine how the work moves through the organization. Focus on areas of inefficiency and workload distribution, dentify patterns and trends around productivity, efficiency, capacity and utilization, and then use these indicators to guide AI investment and where future roles should be added.
The current labor market creates a rare opportunity to see how work moves across an organization. Leaders who use that visibility to redesign teams will be better positioned when hiring resumes.
Leverage behavioral data to understand team needs
To ensure precision and value from ongoing workforce planning efforts, organizations should establish systems to capture broad behavioral data continuously across people, tools, AI agents and workflows. This enables leaders to see how work actually moves through a team, where capacity sits and where gaps open up.
That’s a different kind of analysis than the role-specific focus dominating the AI-and-jobs conversation. The most-cited piece this year, HBS / Suraj Srinivasan’s 900+ occupation mapping, found that since ChatGPT launched, postings for structured, repetitive-task roles dropped 13% while demand for analytical, technical and creative work grew 20%. The Washington Post has an interactive map of which jobs are most exposed to AI.
Market data can tell you which jobs are shifting and can estimate how AI may reshape tasks within occupations, but it can’t show how work is actually getting done inside your organization, where AI often changes workflows that cut across roles rather than replacing individual tasks in isolation. Behavioral data can tell you how your work is shifting—and which processes, teams, and capabilities should evolve as a result. That data, tied with an understanding of business context and goals, is key to successful AI adoption maturity.
Make organizational design a continuous exercise
Use what you’ve learned from diagnosing your team’s actual work patterns to rethink your organizational design process.
As AI reshapes roles and responsibilities faster than annual planning cycles can keep up, it’s time to update workforce design cadences accordingly. Don’t wait for next January. By then your team will have absorbed more work, redistributed more roles and developed more workarounds.
Instead, treat organizational design more like system health — something you monitor continuously, with signals, diagnostics and regular adjustments. The goal is to make workforce planning an ongoing management discipline, not a ritual that leaders endure and employees struggle to see value in once a year.
These questions should be asked continuously: How is work actually flowing? Where are different loads concentrated? What gaps are now visible?
Update team job descriptions frequently
Changes in how work gets done should drive continuous organizational design, which in turn should reshape job descriptions, team structures and expectations.
Imagine someone on a small team carrying outsized expectations. Their job posting was relevant when they joined, and then soon after it wasn’t. That’s not unusual. There will always be a disconnect between a static job posting and the realities of what someone does once they’re hired.
When job descriptions go stale, role labels become a poor proxy for value. Some of the most important work on a team never makes it into a formal job description. It emerges organically, accumulating around the people who solve problems, connect functions and fill gaps. The risk of eliminate-or-augment decisions made at the role level is that leaders remove the very people quietly holding the team together. It’s a misunderstanding where the work really lives.
Here’s a practical place to start: Treat job descriptions as living documents, rather than static records. Use behavioral data and employee input to update them continuously, capturing not just assigned responsibilities, but the work people actually perform. Over time, those descriptions become a more accurate map of where work lives across the organization. Then, when it’s time to hire, restructure or invest, leaders can make decisions based on current reality rather than last year’s assumptions.
Level the AI playing field with governance
AI governance is now a precondition for successful organizational design. Governance should prescribe what tools should be used, in what scenarios and to what extent. This policy should be based on business goals and supported by training and development. Clarity around governance addresses two common execution issues: uneven adoption across teams and inconsistent usage.
High performers are natural early adopters of AI. They’re more likely to try and adopt new tools. The gap widens between high performers and careful integrators, who are great at their jobs but prefer explicit guidance and training on what tools to use for what tasks. The result is an invisible divide. The organization chart suggests one team, but in practice there are two: people who have discovered how to multiply their output with AI and people who are still working the old way. The gap isn’t talent, but rather access to know-how and the confidence to apply it.
Effective AI use depends on where each worker sits on a maturity spectrum, from using AI as an expensive search engine all the way to creating and monitoring autonomous work. Not every employee needs to reach the highest stage, but every employee needs to be coached on which tool to use, in which situation and to what extent. Without that coaching, companies spend needlessly and fail to upskill their workforce.
The fix for both issues is governance—clarity around how employees are expected to use AI. Without governance, the gap increases between workers and undermines the promise and potential of AI adoption.
Design teams worthy of the people you’re hiring
NVIDIA’s Jensen Huang told this year’s Carnegie Mellon graduating class to “build something worthy of your potential.” The leadership version is a harder challenge: design teams worthy of the people you’re hiring into them.
None of this is technically difficult, but it requires paying attention. Look at how work moves through your team. Notice who’s stretching, who’s waiting and who’s carrying responsibilities that exist nowhere on an org chart. Build the systems that allow best practices to spread.
The leaders who hire well when this market shifts won’t be the ones with better instincts. They’re the ones who used this period to understand where work really lives.
The noise will come back. The goal is to see your team clearly now.
