By Katie Merritt, Senior Research and Data Manager, Robert Half
Leaders know their teams are using AI. What’s less clear is what’s changing as a result.
A marketing manager can generate multiple campaign headlines and ad copy variations for testing in minutes. A financial analyst can identify trends and present recommendations for improving financial performance in just a few hours. A Director of Human Resources can identify workforce trends and guide decisions that strengthen performance, engagement and retention faster.
AI is reshaping how some work gets done without changing who does it.
And yet, most roles haven’t changed.
That disconnect is what leaders are trying to make sense of right now. They’re not asking whether AI works. They’re asking where it is actually improving the work and how roles, skills and processes need to evolve going forward.
That uncertainty is fueling distinct perspectives on AI. While many conversations focus on AI boomers and AI doomers, Robert Half’s research highlights a third group—AI bloomers—taking a more measured approach. Together, these perspectives show how leaders are interpreting the pace of change, its impact on their teams and what comes next for the workforce.
AI boomers are optimists, early adopters. They see the speed and plan around it, expecting immediate productivity gains while rethinking how work gets done and how teams are structured.
AI doomers are pessimists and late adopters. They focus on what could be reduced, asking which roles shrink, shift or disappear as parts of the work become easier or automated.
AI bloomers are somewhere in the middle, cautiously optimistic, adopting AI at a measured pace. They stay closer to what has actually changed, testing where AI helps, where it still requires oversight and how those changes are making a business impact.
Each perspective reflects something real, but not the full picture.
This isn’t the first time new technology has raised these kinds of questions.
More than 2,000 years ago, in Plato’s Phaedrus, Socrates warned that writing, a new “technology” at the time, could create the appearance of knowledge without real understanding, allowing people to repeat information without truly grasping it.
That concern shows up again today. AI can produce convincing output instantly, but it doesn’t replace human judgment. Someone still has to interpret it, question it and decide how to act on it.
The real risk isn’t the technology. It’s mistaking output for understanding.
The AI hiring paradox: Why demand for talent remains strong
What the data is (and isn’t) showing yet
When asked about the impact of AI on headcount over the next two years, 54% of the more than 2,000 hiring managers surveyed by Robert Half expect a net increase in jobs at their organization, compared with 23% who predict a decrease and 20% who foresee no change.
That’s not what a shift to leaner teams looks like.
AI is being adopted, but the impact is still difficult to quantify. Gains may be visible at the task level, but they’re harder to demonstrate across roles or the organization as a whole. Teams are working differently, but not necessarily with fewer people.
Part of the reason is the “AI tax”—the added time and effort required to learn new tools, validate outputs, build oversight and integrate AI before meaningful gains begin to show.
Companies of all sizes are still experimenting. They are testing where AI adds value, rather than fully redesigning how work gets done.
If AI were already delivering large-scale gains, we would expect to see reduced hiring demand and leaner organizational structures. And that’s not what our data is showing.
AI is changing how companies hire.
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The hiring paradox: AI isn’t reducing talent needs (yet)
AI is reshaping hiring demand, not reducing it. According to Robert Half research, more than half of leaders at large (56%) and midsize (55%) companies, and 41% at small businesses, anticipate a net increase in jobs at their organization over the next two years as AI adoption grows. At the same time, hiring priorities are shifting: 54% of hiring managers have updated job descriptions to include AI-related skills and experience, 49% are prioritizing more strategic roles and 35% are placing greater emphasis on experienced over entry-level talent.
This shift should feel familiar. QuickBooks didn’t eliminate accountants. It shifted the work from data entry to analysis, advisory and decision-making.
AI is doing the same. Demand remains strong for professionals who can interpret outputs, validate results and apply them in real-world contexts. At the same time, many organizations still report difficulty finding qualified talent, even as AI tools promise to deliver results.
This is increasing the premium on judgment, communication and human soft skills grounded in experience. According to Robert Half research, 65% of hiring managers cite critical thinking as the most important skill to complement AI, followed by adaptability (61%) and creativity (57%). In some cases, organizations moved too quickly, assuming AI could take over parts of roles more easily than it could. Employers underestimated how much human judgment, context and decision making those roles required. Robert Half research shows that 3 in 10 employers eliminated positions after implementing AI but later added those roles back.
Entry-level disruption: Where the impact is becoming more visible
AI is reshaping entry-level work. And that could have long-term consequences.
Tasks that once served as entry-level training grounds—data entry, basic analysis, customer service interactions and administrative coordination—are increasingly being handled or supported by AI.
This creates a long-term risk. Organizations still need to build future talent pipelines, but they’re investing less in the roles that develop them.
At the same time, many early-career professionals—raised on digital tools and more comfortable adopting new technologies and AI—are well positioned for this shift, making the lack of clear development pathways even more consequential.
Over time, that risks weakening the very talent pipeline organizations depend on to grow and lead the future of an organization.
Why gains take longer than expected
Technology adoption curves are rarely linear, and gains don’t come from using new tools alone.
According to Robert Half research, only 27% of tech leaders say their organization has reached advanced AI implementation, with AI deeply integrated into business processes. Most companies are still in early stages, which means integration work is happening before its full impact is realized.
That lag is real. Workflows need to be redesigned. Teams need training. Leaders need to account for accuracy, risk and compliance before AI can be trusted in critical processes.
AI isn’t a quick fix or a shortcut to fewer jobs.
The rise of the AI bloomer mindset
AI bloomers aren’t anti-AI, and they’re not dismissing its potential. They’re focused on what’s actually happening.
As expectations outpace observable results, more leaders are moving toward this middle ground. It reflects a familiar pattern in technology adoption. The Gartner Hype Cycle for Emerging Technologies describes how a technology breakthrough drives heightened expectations, followed by a drop-off as results fall short, before more practical and gradual progress takes hold. AI is in that phase now.
Bloomers aren’t asking whether AI will transform work. They’re asking a more practical question: Where are the measurable results?
What leaders should do now
Focus on where AI improves outcomes, not where it simply replaces effort.
Invest in skills that complement AI—judgment, communication and the ability to apply outputs in real-world contexts. Maintain hiring discipline and avoid assuming roles will disappear before the work actually changes, especially as adoption remains uneven across organizations.
Protect and rethink entry-level pipelines so future talent continues to develop. AI should be treated as an augmenter of talent, not a substitute.
For more insight into how AI is changing candidate evaluation, read The resume illusion: How AI-generated applications are challenging traditional hiring practices.
A measured outlook
AI is likely to reshape productivity, but on a longer timeline than headlines suggest. The changes are real, but unfolding unevenly across workflows, roles and organizations.
For leaders, that makes this moment difficult to read. Early gains are visible but inconsistent and difficult to quantify at scale.
Leaders who move ahead won’t rely on strong opinions about AI. They will test, learn and adapt as the impact becomes clearer.
In the end, AI won’t reward the strongest opinions. It will reward sound judgment.
Which type of leader are you?
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