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consulting firm accenture recently laid off 11,000 workers while expanding its efforts to train workers for use artificial intelligenceIt’s a stark reminder that the same technology driving efficiency is also redefining what’s required to keep a job.
And Accenture is not alone. IBM has already replaced hundreds of roles aye systems, creating new jobs in sales and marketing. Amazon Staff are being cut while also expanding the teams that build and manage AI tools. Across all industries, from banks to hospitals to creative companies, employees and managers alike are trying to understand which roles will disappear, which roles will evolve, and which new roles will emerge.
I research and teach at Drexel University’s LeBow College of Business, studying how technology changes work and decision making. My students often ask how they can remain employable in the age of AI. Executives ask me how to build trust in technology that advances faster than people. In the end, both groups are really asking the same thing: What skills matter most in an economy where machines can learn?
To answer this, my colleagues and I analyzed data from two surveys conducted this summer. Previously, for the Data Integrity and AI Readiness Survey, we asked 550 companies across the country how they use and invest in AI. Secondly, for the College Hiring Outlook Survey, we looked at how 470 employers viewed entry-level recruiting, workforce development, and AI skills in candidates. These studies reflect both sides of the equation: those who are building AI and those who are learning to work with it.
AI is everywhere, but are people ready?
More than half of organizations told us that AI now drives daily decision making, yet only 38 percent believe their employees are fully prepared to use it. This difference is reshaping today’s job market. AI isn’t just replacing workers; This shows who is ready to work with it.
Our data also shows a contradiction. While many companies now rely on AI internally, only 27 percent of recruiters say they are comfortable with applicants using AI tools for tasks like writing resumes or researching salary ranges.
In other words, the tools that companies rely on for business decisions still raise suspicions when job seekers use them for career advancement. Unless this approach changes, even skilled workers will continue to receive mixed messages about what “responsible AI use” actually means.
In the Data Integrity and AI Readiness Survey, this readiness gap appeared most clearly in customer-facing and operational jobs like marketing and sales. These are the same areas where automation is advancing rapidly, and layoffs occur when technology evolves faster than people’s ability to adapt.
Additionally, we found that many employers have not updated their degree or credential requirements. They’re still hiring for yesterday’s resume, while tomorrow’s work demands fluency in AI. The problem isn’t that AI is replacing people; The point is that technology is evolving faster than most workers can adopt it.
Flow and Trust: The Real Foundations of Adaptability
Our research shows that the skills most closely associated with adaptability share a single theme, what I call “human-AI flow.” This means being able to work with smart systems, question their outcomes, and keep learning as things change.
About the author
Murugan Anandarajan is Professor of Decision Sciences and Management Information Systems at Drexel University.
This article was first published by The Conversation and is republished under a Creative Commons license. read the original article,
Across companies, the biggest challenges are scaling AI, ensuring compliance with ethical and regulatory standards, and connecting AI to real business goals. These obstacles aren’t about coding; They’re about good decisions.
In my classes, I emphasize that the future will favor those who can transform machine output into useful human insights. I call this digital bilingualism: the ability to fluently navigate both human judgment and machine logic.
What management experts call “reskilling” – or learning new skills for a new role or a major change in an old role – works best when people feel safe to learn. In our Data Integrity and AI Readiness Survey, organizations with strong governance and high trust were almost twice as likely to report gains in performance and innovation. Data shows that when people trust their leaders and systems, they are more willing to experiment and learn from mistakes. In this way, trust transforms technology from something to be feared into something to be learned, giving employees the confidence to adapt.
According to the College Hiring Outlook Survey, nearly 86 percent of employers now offer in-house training or online boot camps, yet only 36 percent say AI-related skills are important for entry-level roles. Most training still focuses on traditional skills rather than the skills needed for emerging AI jobs.
The most successful companies make learning a part of the job. They create learning opportunities in real projects and encourage employees to experiment. I often remind leaders that the goal is not just to train people to use AI but to help them think alongside it. This is how trust becomes the foundation of growth, and how reskilling helps retain employees.
New rules of appointment
In my view, companies leading the way in AI aren’t just cutting jobs; They are redefining them. To succeed, I believe companies will need to hire people who can combine technology with good judgment, question what AI produces, explain it clearly, and turn it into business value.
At the companies that are putting AI to work most effectively, recruiting is no longer just about resumes. What matters is how people apply qualities like curiosity and judgment to intelligent devices. I believe these trends are leading to new hybrid roles such as AI translators, who help decision makers understand what AI insights mean and how to act on them, and digital coaches, who teach teams to work with intelligent systems. Each of these roles combines human judgment with machine intelligence, revealing how the jobs of the future will blend technical skills with human insight.
That blend of decision-making and adaptability is the new competitive advantage. The future will reward not only the most technical workers, but also those who can turn intelligence – human or artificial – into real-world value.