The Accenture consulting firm recently laid off 11,000 employees while step up efforts to train workers in the use of artificial intelligence. It’s a stark reminder that the same technology that drives efficiency is also redefining what it takes to keep a job.
And Accenture is not alone. IBM has already replaced hundreds of roles with AI systems, while creating new jobs in sales and marketing. Amazon is reducing its workforce although it expands the teams that build and manage AI tools. In all sectors, banks has hospitals And creative businessesworkers and managers alike are trying to understand which roles will disappear, which will evolve and which new ones will emerge.
I research and teach at Drexel University LeBow College of Businessstudy how technological change and decision-making work. My students often ask me how to stay employable in the age of AI. Executives ask me how to build trust in technology that seems to be evolving faster than people can adapt to it. Ultimately, both groups are actually asking the same thing: Which skills are most important in an economy where machines can learn?
To answer this question, I analyzed data from two surveys that my colleagues and I conducted this summer. For the first, the Data Integrity and AI Readiness Surveywe asked 550 companies across the country how they are using and investing in AI. For the second, the College Hiring Outlook SurveyWe looked at how 470 employers viewed entry-level hiring, workforce development, and candidate AI skills. These studies show both sides of the equation: those who build AI and those who learn to use it.
AI is everywhere, but are people ready?
More than half of organizations told us that AI is now driving everyday decision-making, but only 38% believe their employees are fully prepared to use it. This gap is reshaping today’s labor market. AI doesn’t just replace workers; this reveals who is willing to work alongside him.
Our data also shows a contradiction. While many companies now rely on AI internally, only 27% of recruiters say they are comfortable with candidates using AI tools for tasks like writing resumes or researching salary ranges.
In other words, the same tools that companies trust to make business decisions still raise doubts when job seekers use them to advance their careers. Until this view changes, even skilled workers will continue to receive conflicting messages about what is happening.responsible use of AI” really means.
In the Data Integrity and AI Readiness Survey, this readiness gap showed up most clearly in operational and customer-facing jobs, such as marketing and sales. These are the same areas where automation is advancing rapidly, and layoffs tend to occur when technology changes faster than people can adapt.
At the same time, we found that many employers have not updated their degree or credential requirements. They are still recruiting for yesterday’s CVs, while tomorrow’s work requires mastery of AI. The problem is not that people are being replaced by AI; it’s that technology is changing faster than most workers can adapt.
Mastery and confidence: the true foundations of adaptability
Our research suggests that the skills most closely related to adaptability share a theme, what I call “human-AI fluency.” This means being able to work with intelligent systems, question their outcomes, and continue to learn as things evolve.
Across businesses, the biggest challenges lie in developing AI, ensuring ethical and regulatory standards are met, and connecting AI to real business goals. These obstacles are not about coding; it’s about good judgment.
In my classes, I emphasize that the future will favor people who can translate machine output into useful human knowledge. I call this digital bilingualism: the ability to fluently navigate both human judgment and machine logic.
What management experts call “reskilling” – or learn new skills to adapt to a new role or major changes to an old one – works best when people feel safe to learn. In our Data Integrity and AI Readiness Surveyorganizations with strong governance and high levels of trust were nearly twice as likely to report gains in performance and innovation. Data suggests that when people trust their leaders and their systems, they are more willing to experiment and learn from their mistakes. In this way, trust transforms technology into a source of learning, giving employees the confidence to adapt.
According to the College Hiring Outlook SurveyAbout 86% of employers now offer in-house training or online boot camps, but only 36% say AI skills are important for entry-level positions. Most training still focuses on traditional skills rather than those needed for emerging AI jobs.
The most successful companies integrate learning into the work itself. They create learning opportunities through 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 along the way. This is how trust becomes the foundation of growth and retraining contributes to employee loyalty.
The new hiring rules
In my opinion, leading AI companies are not just cutting jobs; they redefine them. To succeed, I think companies will need to hire people who can connect technology with common sense, question what AI produces, explain it clearly, and turn it into business value.
In companies that put AI to work as effectively as possible, recruiting is no longer just about CVs. What matters is how people apply characteristics like curiosity and judgment to smart tools. I think these trends are leading to new hybrid roles, such as AI translators, who help decision-makers understand what AI insights mean and how to leverage them, and digital coaches, who teach teams to work alongside intelligent systems. Each of these roles connects human judgment with artificial intelligence, showing how future jobs will combine technical skills and human insight.
This blend of judgment and adaptability is the new competitive advantage. The future will not only reward the most technical workers, but also those who can turn intelligence – human or artificial – into real value.
