A.I. Transformation Fails When Companies Treat People as Costs

A recent ad from the A.I. company Narwhal Labs featured an image of a woman who was half human, half cybernetic machine alongside the tagline: “She Outworks Everyone. And She Will Never Ask For a Raise.”

I’m not here to add to the gender-oriented flak the company has already taken for this ad (though it’s well deserved), but to highlight an even bigger point. This campaign revealed something bigger about how many companies are approaching A.I. It said the quiet part out loud. 

Wouldn’t business be easier if you didn’t need people?

The assumption sits beneath much of today’s corporate A.I. strategy. Companies see the possibility of unprecedented efficiency gains and imagine a future with fewer employees, lower labor costs and less operational friction. However, what is very much the same as every efficiency trend that came before it, are the negative impacts on people, and the unintended consequences for the business that result when efficiency becomes the goal unto itself.

Let’s look at history. Remember the open office floorplan? This was widely lauded by executives and consultants as the best thing since sliced bread. Remove physical barriers, the thinking went, and creativity would flourish, teams would communicate more naturally and productivity would reach never-before-seen levels. Shared spaces sprang up like wildfire, not because it was a good idea, but because it saved money. 

In reality, many companies adopted open offices for a much simpler reason: they were cheaper. Employees hated them. Introverts struggled. Workers would compete for reservations to hide in conference rooms. Complaints about distractions and workplace tension increased. The dreaded shared environment sparked a work-from-home trend, even pre-pandemic. Productivity gains often failed to materialize. 

And yet companies clung to the narrative for years before finally admitting the underlying economic logic: this setup reduced real estate costs, whether employees liked it or not. 

The outsourcing wave of the late 1990s followed a similar pattern. The promise was seductive: highly skilled labor at a fraction of the cost. Entire departments were moved offshore under the assumption that companies could treat organizational capability like a black box—feed in requirements, reduce payroll expenses and receive seamless output on the other side. Once again, businesses underestimated the human dimension. 

Most outsourcing initiatives struggled until companies recognized a basic reality:  remote workers were still people. They required management, communication, context, accountability and motivation. Successful outsourcing eventually depended on investing more heavily in coordination, leadership and relationship-building than many executives originally anticipated. 

The hoped-for cost savings often narrowed as companies added layers of local management to bridge time zones, communication gaps and cultural differences with remote teams. The dreamy cost savings never really materialized. The outsourced teams eventually became well-functioning extensions of organizations, with the same human needs and working practices as local teams. When we eventually figured out how to work together across distance and culture, the benefit became more about workforce growth than cost savings. Because the only way to get low-cost outsourced projects to work was to make them more expensive again by taking care of people.

Now here we are again with A.I. Companies are eyeing the holy grail of efficiency and attempting to remove human dependency altogether. If we say the quiet part out loud, just like in Narwhal Labs’ ad, it’s: “Imagine a workforce that doesn’t need annoying, expensive humans.”

Companies are being tempted by the promise of a workforce that doesn’t need to eat or sleep, never disagrees with you and needs no care and feeding whatsoever, let alone ask for a raise. What could be better?

We are already experiencing the early stages of what is not working about this A.I. efficiency revolution. Across industries, organizations are removing humans before A.I. systems are mature enough to reliably replace them. Teams are being instructed to “use A.I.” without clear workflows, operating models or expectations. So programs stall, and the remaining humans need to shoulder massive loads. One employee recently described it to me this way: “It’s like being in the Hunger Games. Everyone is being judged on how well they use A.I., with no clue what we should be doing with it and wondering who gets eliminated next. It’s chaos.”

Companies are discovering that A.I. service agents have real limitations in solving human customer problems. A.I. resume readers have been shown to show biases toward A.I.-written resumes compared to human-written ones. In many cases, both workers and customers are becoming harder to serve effectively. 

Once again, this sort of efficiency-utopianism that A.I. promises has companies even more excited about the kind of black-box magic that outsourcing once promised, but it again underestimates the human and business consequences of an efficiency-only transformation strategy. They confuse labor reduction with strategic transformation.

While A.I. offers transformative capabilities, few companies—or careers—are likely to remain competitive without learning to use it effectively. But if companies want real transformation, they would greatly benefit by saying the quiet part about what to do with the humans out loud.

If a company’s A.I. strategy is to accept lower customer service levels in exchange for lower costs, executives should say that clearly. If the strategy is to free employees from repetitive work so they can focus on higher-value problem-solving, companies should explain how that transition will work and invest meaningfully in employee development. If the strategy is to augment your secret sauce in product development through A.I.-enabled engineering teams, organizations should train employees accordingly and build systems that support that goal. 

But if the real strategy is simply, “We want to reduce labor costs by 40 percent and hope the technology catches up later,” leaders should be honest about that, too. At minimum, it would force a more realistic conversation about the risks. 

Many organizations are trying to simultaneously promise better customer experience, lower costs, fewer employees, faster growth and happier workers, without acknowledging the trade-offs or explaining to employees the rules of the game. 

Every major efficiency revolution has eventually faced the same truth: organizations still run on human motivation. Thriving humans create thriving businesses and motivated customers. Because, by the way, customers are still humans.

A.I. does not eliminate the leadership responsibility to understand, respect and communicate with humans. If anything, it increases it. The opportunity to win with A.I. should increase leadership’s focus on ensuring people use it with clear goals, rules and support—because it’s the A.I.-supercharged, still-motivated humans who will become a true competitive advantage.

The companies that win with A.I. will not be the ones that remove humans fastest. They will be the ones who decide most clearly where humans matter most, where A.I. genuinely adds leverage and how the two will work together to deliver on the promise of the business.