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Key Takeaways
- In the current AI era, leaders are continuously redesigning their workforce, with roles always in motion rather than “set” for the next planning cycle.
- Most companies still see people as headcount and cost centers, not as a dynamic portfolio of skills, capacity and teams
- The only way to use AI responsibly is to get a single, living view of who does what today, how work is shifting, and where humans versus machines add the most value.
I’ve seen the way we work turned on its head before. Way back in the 1990s, I witnessed the boom of client-server computing. Later on, we traded answering machines for emails, on-premises software for SaaS, and computers for cell phones.
All of these qualified as business transformations, changing how we work and forcing companies everywhere to adapt. But AI is categorically different.
It cuts across sectors, departments, roles and skill levels all at once. Right now, every leader is trying to figure out how AI is threatening or helping their business — their value proposition, how they make money and their cost structure. This is especially true of white-collar businesses built on human capital.
AI represents a fundamental shake-up of what people do, what machines do, and how the two collaborate. I work with thousands of companies that are in the thick of navigating this change. And I see CEOs everywhere wrestling with the same question: not whether to transform their workforce, but how to do it responsibly, quickly, and at scale.
But this isn’t a single shift. We’re at the dawn of a new era: one of constant workforce transformation that will require companies to continuously understand how work is changing, redesign the workforce around strategy, and translate insight into action.
The good news is that while AI is the catalyst, it can also be a trusted partner in the process. Here are five steps any business can take to keep pace with the workforce transformations accelerated by AI.
1. Understand your people
We’re already seeing the first wave of AI boomerang hires — workers who were cut loose, only to be rehired later… when management discovered that bots alone fell short. Few companies today truly understand their existing workforce. They might have a grasp on headcount, but when it comes to real capacity, they’re flying blind.
Finance sees cost. HR sees roles. Sales sees pipeline. Few leaders can connect workforce size and cost to the roles, skills and teams that actually drive business results.
Real transformation starts with addressing these blind spots. That means breaking down silos and bringing workforce data together in a single platform. So when it comes time to make crucial decisions, everyone has a holistic view of current costs, skills, talent, and gaps.
2. Design tomorrow’s workforce
Once you understand the workforce, the next step is mapping out where it needs to go. Traditionally, this has been the realm of annual planning exercises. But in the AI era, the pace of change has outrun the old planning calendar.
Periodic, consultant-led org design projects don’t cut it. Instead, leaders need to embrace an always-on planning approach. That means modeling scenarios continuously — tweaking span of control, management layers, cost trade-offs and acquisition options as new conditions emerge.
This kind of planning also has to extend beyond full-time employees. AI rarely eliminates roles neatly, with gains scattered across pieces of work. So it’s key to look past job titles and catalog the tasks inside each role: which require judgment, empathy, creativity or relationships, and which can be automated, augmented or eliminated.
As headcounts and business conditions shift, new AI-powered tools allow for on-the-fly plan adjustments, keeping forecasts and budgets in sync. They also bridge the gaps between people and finance teams, enabling collaborative planning using real-time data.
3. Execute with context and confidence
So now you’ve got a roadmap. But how do you get your org from point A to point B? Knowing which initiatives to fund and cut, and how to sequence changes, has traditionally been the domain of analysts and consultants. I’ve seen months of effort spent winding down a single underperforming division.
Complex, multithreaded problems — like reengineering a 50,000-person org on the fly — are exactly what AI was built for. Today’s tools can wade through endless variables and map out paths in seconds, not months.
But this only works if one key condition is in place: context.
An off-the-shelf LLM might suffice if you’re firing off a quick email or coding a new app. But when it comes to transforming your workforce, context is everything: team history, budget constraints, compliance boundaries, skill gaps, and target business outcomes.
4. Activate your managers
Ultimately, any workforce transformation lives or dies with frontline managers. HR may own talent, but managers own performance outcomes. That’s why empowering them to drive better people decisions is critical.
Getting there means closing the “last-mile” gap that leaves so many managers flying blind. Caught up in the day-to-day, most managers lack a holistic view of their team, let alone how it impacts business performance. The core problem: no access to the data that enables true workforce insights.
Fixing this starts with democratizing workforce data and insights. This is a cultural challenge, first and foremost — too many departments hoard their data, and not enough leaders trust frontline managers enough to share it.
But it’s also a technical one. Managers are already swamped with dashboards and reporting tools. What’s missing is real-time intel they can act on.
New AI-powered tools deliver just that, letting managers ask questions in plain language. Who should I have a retention chat with this week? Which of my top performers are showing early signs of burnout? How long has my open role been vacant, and what’s it costing us?
Pulling together people and business data from across HR systems, CRM and revenue platforms, and everyday employee apps, AI gives managers a clear, reasoned answer.
5. Keep improving
With the first four steps in motion, the final one is all about making workforce transformation a habit. AI keeps moving the goalposts, so the cycle never stops. To stay ahead, companies need to build continuous improvement into their operations.
Here, too, AI can prove an ally, not merely a challenge. Workforce plans and projections provide a useful baseline. But today’s tools ensure progress is continually monitored, with gap-to-plan and opportunity-to-outperform continually flagged for follow-up.
Agentic AI can also provide a needed push. Always-on agents keep working in the background, nudging program owners when they’re off-track and surfacing the next priority before leaders have to ask.
This is what turns workforce transformation from a one-time project into an ongoing discipline. The companies that master it will be in a much stronger position: not just understanding how work is changing, but moving with it.
Key Takeaways
- In the current AI era, leaders are continuously redesigning their workforce, with roles always in motion rather than “set” for the next planning cycle.
- Most companies still see people as headcount and cost centers, not as a dynamic portfolio of skills, capacity and teams
- The only way to use AI responsibly is to get a single, living view of who does what today, how work is shifting, and where humans versus machines add the most value.
I’ve seen the way we work turned on its head before. Way back in the 1990s, I witnessed the boom of client-server computing. Later on, we traded answering machines for emails, on-premises software for SaaS, and computers for cell phones.
All of these qualified as business transformations, changing how we work and forcing companies everywhere to adapt. But AI is categorically different.
It cuts across sectors, departments, roles and skill levels all at once. Right now, every leader is trying to figure out how AI is threatening or helping their business — their value proposition, how they make money and their cost structure. This is especially true of white-collar businesses built on human capital.
AI represents a fundamental shake-up of what people do, what machines do, and how the two collaborate. I work with thousands of companies that are in the thick of navigating this change. And I see CEOs everywhere wrestling with the same question: not whether to transform their workforce, but how to do it responsibly, quickly, and at scale.