Why the biggest threat in the age of AI isn’t automation, but the choices business leaders make and how companies can lower costs, enhance performance, and keep people employed.

For much of the past two years, the public conversation around artificial intelligence has been dominated by predictions of mass unemployment. With 2026 approaching, those warnings feel louder than ever. But it’s more complicated than that. AI doesn’t have to result in job cuts

In November, MIT released the Iceberg Index, a simulation analyzing 151 million U.S. workers, 923 occupations, and 32,000 skills to determine which tasks today’s AI systems are technically capable of performing. Their findings showed AI could theoretically handle work equal to 11.7% of all U.S. wages, about $1.2 trillion worth of labor.

“Automation takes the easy interactions and humans are left with the hardest ones.”

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The researchers stressed this is task-level exposure, not a forecast of job losses. It represents what AI can do technically today, not what employers will actually choose to automate. That distinction, between technological capability and organizational choice, defines the moment companies now find themselves in.

Few people sit closer to these decisions than Gal Rimon, CEO and founder of Centrical, an employee performance intelligence platform that uses AI to translate real-time data into personalized learning, coaching workflows, and daily guidance that maximize frontline performance.

Before AI entered the picture, the company, founded in 2013, already worked to elevate frontline performance by linking goals, coaching, and learning in one integrated employee experience platform. Rimon says the business leaders he works with are no longer choosing between reducing costs and supporting their teams. “Companies aren’t choosing between efficiency and employee growth anymore. They are trying to do both at once,” he explains.

What Does Hierarchy Look Like When the AI Becomes a Coworker?

We are mixing industries and functions (contact center) here. Maybe we can rewrite as:
Across cost-pressured industries, companies are applying AI in customer-service operations (both in-house contact centers and BPO our outsourced centers) to flatten layers, expand manager spans, and shift administrative work to automation.“We’re seeing frontline managerial spans grow because AI can automate analysis, QA, summaries, and admin,” Rimon says. “In some major financial services organizations, customers are increasing coaching activity 3x while cutting supervisor admin overhead by up to 70%, because AI does the prep work that used to eat up so much of their time.”

That shift forces leaders to rethink how work is designed. It also raises a question that organizations didn’t have to confront before: what does hierarchy look like when AI becomes a co-worker?

That tension is already emerging at the executive level. In a recent interview with The Wall Street Journal, Cisco Chief Information Officer Fletcher Previn said: “That will be an interesting frontier where we’ll work closely with HR and figure out, ‘Does [the previous] chain of command still make sense in an AI agent world?’” It’s a sign that AI isn’t simply automating tasks, it’s reshaping the very structure of how work flows.

Beyond structure, AI is changing how performance is defined and measured. Before AI, frontline teams were evaluated on volume, handle time, and adherence. Now, Rimon says, leaders are shifting to outcomes: “Did we solve the problem? Did we prevent escalation? Did we improve accuracy or customer satisfaction and retention? AI can enable predictive KPIs and real-time behavioral indicators that were impossible at scale just a few years ago.”

Exposure Does Not Automatically Equal displacement

And as companies adopt these new models, HR’s role is expanding. This deeper integration of HR and technology teams has already appeared in several industries. In 2024 and 2025, Amazon implemented automation across parts of its logistics, advertising operations, and AWS support. Some routine administrative roles were reduced, but many employees were redeployed, not eliminated, into roles supervising AI systems, handling exceptions, or managing escalations. Amazon’s restructuring demonstrated a core reality of modern automation: exposure does not automatically equal displacement.

A separate Fortune report on Amazon’s restructuring noted that AI is reshaping the need for middle management. As one executive explained, the company is working to “reduce bureaucracy” as AI takes on tasks that once required multiple layers of oversight, prompting Amazon to rethink which managerial functions remain essential.

Rimon sees this across many organizations: “Automation takes the easy interactions and humans are left with the hardest ones. That shift creates a talent gap in empathy, judgment, and adaptability. If you shrink without upskilling, complexity overwhelms the remaining workforce and performance drops.”

The companies that manage this transition successfully invest in capability alongside automation. “Our customers that pair AI automation with on-the-job upskilling and supervisor readiness programs see 50% faster onboarding and skill ramp-up, with better retention because people are prepared for what AI can’t do.”

One of the strongest examples comes from Deutsche Telekom, which used Centrical’s platform to support thousands of independent sellers across 900 shops. The core challenge wasn’t performance, it was the inability to communicate with a highly dispersed frontline workforce. Until then, everything flowed through shop managers, making consistent training, upskilling, and alignment nearly impossible. With unified communication, structured learning paths, and real-time recognition, engagement surged to 89% satisfaction and sales rose by 10–20% across several product lines. It’s a clear example of how technology can strengthen connection and capability when deployed thoughtfully.

Rimon’s view is that the next phase of AI adoption won’t replace frontline teams but will pair them with AI agents in a hybrid model. Across customer service and back-office operations in large enterprises, especially in finance, telecom and trava.

AI is beginning to handle coordination and routine analysis while employees focus on higher-value work. Much of the real efficiency comes from streamlining managerial workflows, not eliminating roles. Early adopters, Rimon shares, are already building these models, using integrated performance and training systems that allow frontline teams to operate alongside AI agents more productively and with greater insight.

Still, the risks are real. As AI becomes more integrated into workflows, some companies are applying it mainly for monitoring. Rimon calls this “algorithmic burnout.”
“AI can absolutely drive performance measurement, but if it’s used mainly for surveillance, it backfires. The industry is already seeing ‘algorithmic burnout,’ where constant real-time scoring increases stress and distrust.”

That’s why many business leaders are reframing AI not as a replacement, but as a partner.
Netskope Chief Digital & Information Officer Mike Anderson was quoted saying: “What we’re trying to help people understand is that [an AI agent] is a co-worker that’s going to help you be more productive, not someone who’s going to replace you.”

Make the Transition Feel Like a Bridge, Not a Cliff

With the landscape changing so quickly, business leaders need a clearer roadmap. Rimon offers three practical recommendations:

1- “Start with the outcome, then redesign work around humans+AI. Don’t automate randomly. Decide what customer and business outcomes matter, then allocate tasks between AI and humans accordingly.”

2- “Invest in ‘human-centered AI’ alongside automation. If AI removes the routine, humans need support to master the complex. Pair automation within the flow of work learning, coaching, and mobility.”

3- “Don’t underestimate change management. AI-first transformation is cultural and operational. Leaders have to bring people along, build trust, and make the transition feel like a bridge,not a cliff.”

AI is capable of performing a significant share of today’s work, but the future may depend on whether employers choose elimination or elevation.

The early evidence suggests AI can reduce costs and improve outcomes without destroying careers, if companies redesign work intentionally and invest in people.

The future of work won’t be defined by a wave of mass unemployment. It will be defined by how business leaders choose to build the bridge between automation and human potential.