A recent ad from AI company Narwhal Labs featured an image of a woman who was half human, half cybernetic machine along with the tagline: “She outshines everyone. And she’ll never ask for a raise.”
I’m not here to add to the gender bias point the company has already made for this ad (though it’s well-deserved), but to make an even bigger point. This campaign revealed something bigger than how many companies are approaching AI. She said the quiet part out loud.
Wouldn’t business be easier if you didn’t need people?
The assumption underlies much of today’s corporate AI strategy. Companies see the possibility of unprecedented efficiency gains and envision a future with fewer employees, lower labor costs, and less operational friction. However, what is much the same as any efficiency trend that has come before it are the negative impacts on people and the unintended consequences for business that result when efficiency becomes the goal in itself.
Let’s look at the story. Remember the open plan office? This was widely praised by executives and consultants as the best thing since sliced bread. Remove physical barriers, thought flowed and creativity would flourish, teams would communicate more naturally and productivity would reach unprecedented levels. Shared spaces took off 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. The employees hated them. Introverts struggled. Workers would compete for reservations to hide in conference rooms. Complaints about distractions and tension in the workplace increased. The eerie shared environment sparked a work-from-home trend, even before the pandemic. Productivity gains often failed to materialize.
And yet companies clung to the narrative for years before finally accepting the underlying economic logic: this arrangement lowered real estate costs, whether employees liked it or not.
The outsourcing wave of the late 1990s followed a similar pattern. The promise was enticing: highly skilled work at a fraction of the cost. Entire departments were moved offshore under the assumption that companies could treat organizational capabilities like a black box—feed demand, reduce payroll expenses, and get seamless results on the other end. Once again, businesses underestimated the human dimension.
Most outsourcing initiatives struggled until companies recognized a fundamental reality: remote workers were still human. They required management, communication, context, accountability and motivation. Successful outsourcing ultimately depended on greater investment in coordination, leadership, and relationship building than many managers initially anticipated.
The hoped-for cost savings were often narrowed as companies added layers of local management to overcome time zones, communication gaps and cultural differences with remote teams. The dreamed-of cost savings never materialized. External teams eventually became extensions of well-functioning organizations, with the same human needs and work 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 make low-cost outsourcing work was to make it more expensive again by catering to people.
Now here we are again with AI Companies are looking at the holy grail of efficiency and trying to remove human dependency altogether. If we say the quiet part out loud, like in the Narwhal Labs ad, it’s: “Imagine a workforce that doesn’t need annoying, expensive people.”
Companies are being tempted by the promise of a workforce that doesn’t need to eat or sleep, never agrees with you, and doesn’t need any nurturing or feeding, let alone asking for a raise. What could be better?
We’re already experiencing the early stages of what doesn’t work with this AI efficiency revolution. Across industries, organizations are laying people off before AI systems are mature enough to reliably replace them. Teams are being instructed to “use AI” without clear workflows, operating models or expectations. So the programs get stuck, and the remaining people have to deal with massive workloads. An 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 the AI, with no idea what to do with it and wondering who’s going to be eliminated next. It’s chaos.”
Companies are discovering that AI service agents have real limitations in solving human customer problems. AI youth readers are shown to tell bias against AI-written resumes compared to those written by man. In many cases, both workers and customers are becoming more difficult to serve effectively.
Once again, this kind of efficiency-utopianism that AI promises has gotten companies even more excited about the kind of black-box magic that outsourcing once promised, but again underestimates the human and business consequences of an efficiency-only transformation strategy. They confuse job reduction with strategic transformation.
While AI 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 benefit greatly from saying the quiet part of what to do with people out loud.
If a company’s AI strategy is to accept lower levels of customer service in exchange for lower costs, executives should make that clear. If the strategy is to free employees from repetitive work so they can focus on solving higher-value problems, companies need to explain how this transition will work and invest meaningfully in employee development. If the strategy is to add your secret sauce to product development through AI-enabled engineering teams, organizations must 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 need to be honest about that, too. At the very least, 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 accepting the trade-offs or explaining the rules of the game to employees.
Every great efficiency revolution has ultimately faced the same truth: organizations still work on human motivation. Thriving people create thriving businesses and motivated customers. Because, by the way, customers are still people.
AI does not eliminate leadership’s responsibility to understand, respect and communicate with people. If anything, it increases it. The opportunity to win with AI must increase leadership focus to ensure that people use it with clear goals, rules and support – because it is the people charged with AI and still motivated who will become a real competitive advantage.
The companies that win with AI won’t be the ones that fire people the fastest. They will be the ones to decide more clearly where people matter most, where AI truly adds leverage, and how the two will work together to deliver on the business promise.
