Right now, a company rehiring people it let go when it bet on AI to do their jobs. This won’t make the news, but it should. The original cuts made headlines with executives touting efficiency gains, AI-powered operations, and a leaner future. Now, the reversal is just a line item.
Forrester estimates that “over half of layoffs attributed to AI will be quietly reversed.” The pattern behind those reversals is consistent: Organizations reached for AI as a cost lever without considering how it might also serve as a capability lever. Doing so shifts the lens to AI as a growth engine.
AI can do more than automate yesterday’s work. With vision and bold leadership, AI augmentation can free human capacity to do the work and achieve greater outcomes than were possible yesterday.
The hiring reversals tell us something we can’t afford to miss: Understanding what AI can do and knowing how to strategically deploy it are entirely different things. Many organizations are discovering that truth the hard way.
Businesses pulling ahead of the pack are asking not what AI can eliminate, but what AI can make better, more efficient and, more importantly, possible.
Currently, an augmented team working with AI is outperforming a team that automated yesterday’s work with AI. Every hour they spend is an hour of purely human-added value, doing what they couldn’t do without AI and what AI couldn’t do without human creativity, instinct, relationships, and empathy.
The mundane work that used to fill their days has been offloaded. What’s left is the work that matters and the net new outcomes possible as a result of unlocking even more creative thinking and innovation.
The World Economic Forum estimates that by 2030, 92 million roles will be eliminated while 170 million new jobs will emerge. That’s a global net gain of 78 million jobs, fueled especially by AI, robotics, and other technological advancements.
The question those numbers raise isn't whether new roles will exist, but which organizations will be positioned to fill them with talent that’s trained and ready for success.
That’s the hard part. Most organizations are investing in helping employees become AI-fluent, but they’re not looking to augment people for tomorrow’s jobs. That needs to change.
Revenue data already backs this up. PwC research shows that industries moving fastest on AI deployment, beyond status quo automation, are growing revenue per employee three times faster than sectors that are lagging.
What does deployment look like in practice?
One tech company made AI fluency a baseline performance expectation embedded in every workflow, from planning to prototyping. It also instituted a policy requiring managers to demonstrate AI couldn't perform a role before hiring for it.
Another company drove active daily AI usage across 97% of its workforce and built a four-tiered fluency framework to measure and develop capability at every level.
Both treated AI not as a headcount lever, but as a mechanism for expanding what employees can contribute.
Research confirms this, showing AI works in the enterprise setting when it augments rather than replaces human capabilities. A field study involving nearly 800 professionals found that individuals working with AI achieved the same performance outcomes as two-person teams working without it.
Automation creates space. What you do with that space determines whether you're building competitive advantage or just cutting short-term costs to compete in the near term.
The value of AI comes from rewiring how businesses run, according to research by McKinsey: “Out of 25 attributes tested for organizations of all sizes, the redesign of workflows had the biggest effect on an organization’s ability to see EBIT [earnings before interest and taxes] impact from its use of gen[erative] AI.”
Something wonderful happens when organizations pair AI with people intentionally, not to reduce headcount, but to expand what people can do.
Workflows that were once bottlenecked by human bandwidth open. Employees take on work that was previously out of reach. Value emerges that neither the technology nor the people could have created in silos. Getting there requires moving through three stages:
- Automating the right kind of work
- Augmenting human capability with what's freed
- Reaching outcomes that wouldn't have been possible under either the old playbook or an automation-only AI strategy
Most organizations are stuck at stage 1 due to the pressures of quarterly earnings targets, boards demanding visible cost reductions, and competitors announcing headcount cuts and calling it transformation. The path of least resistance is using AI to scale what already exists rather than reimagining what could.
To move past those pressures, start with a clear statement of intent: a deliberate answer to why AI and people should be brought together and what new value that combination is meant to create. It would help immensely to ask questions such as:
- What outcome is currently out of reach that AI could unlock within 12 months?
- Where does every hour that automation saves get reinvested?
- Who needs to be aligned on the purpose before the first pilot launches?
Together, the seven phases provide the organizational infrastructure to keep AI gains from eroding.
The organizations asking what AI makes possible aren't waiting for 2030 to find out. They’re working through the phases now. The window for catching up closes a little more each quarter.
Get more insights to reimagine what your workforce can achieve in Work reimagined: The human + AI blueprint for exponential performance.
