The latest ad from AI company Narwhal Labs it featured a picture of a woman who was a half-human cybernetic machine, alongside the caption: “She Works for Everyone. And She’ll Never Ask for a Lift.”
I’m not here to add to the sexist flak the company has already taken for this ad (although it’s well deserved), but to highlight an even bigger point. This campaign revealed something big about how many companies are approaching AI with the silent part out loud.
Wouldn’t business be easier if you didn’t need people?
Assumptions lie at the bottom of today’s AI business strategy. Companies are seeing unprecedented profit opportunities and envisioning a future with fewer employees, lower labor costs and less operational friction. However, much in common with all efficiency trends that came before it, are the negative impacts on people, and the unintended consequences of business that result when efficiency becomes its goal.
Let’s look at history. Remember the open office floorplan? This is highly recommended by managers and consultants as the best thing since sliced bread. Remove the physical barriers, thinking went, and creativity will flourish, teams will communicate naturally and productivity will reach unprecedented levels. Shared spaces caught on like wildfire, not because it’s a good idea, but because it saves money.
In fact, many companies adopted open-plan offices for a very simple reason: they were cheap. The workers hated them. Introverts struggle. The workers were competing to be kept hidden in the meeting rooms. Complaints about disruptions and tensions at work are on the rise. The dreaded shared environment fueled the trend toward working from home, even an early epidemic. Productivity gains are often unsuccessful.
And yet the companies stuck to the narrative for years before they finally accepted the basic economic logic: this set-up reduced the cost of real estate, whether the workers liked it or not.
The wave of outsourcing in the late 1990s followed a similar pattern. The offer was enticing: highly skilled work at minimal cost. All departments were moved offshore under the assumption that companies could treat the organization’s ability as a black box—to serve needs, reduce labor costs and achieve seamless results on the other side. Again, business underestimates the greatness of man.
Many outsourcing efforts have struggled until companies realized a basic truth: remote workers are still people. They needed management, communication, context, accountability and motivation. Successful outsourcing ultimately depends on investing more in communication, leadership and relationship building than most managers initially expect.
Anticipated cost savings often diminish as companies add layers of local managers to bridge time zones, communication gaps and cultural differences with remote teams. Dreamed cost savings never really materialized. Outsourced teams end up being efficient extensions of organizations, with the same people needs and work processes as local teams. When we finally figured out how to work together cross-culturally, the payoff was more staff growth than cost savings. Because the only way to get cheap foreign projects to work was to make them more expensive and take care of people.
Now we are here and AI companies are looking at the holy grail of efficiency and trying to remove human dependence entirely. When we say the silent part out loud, like in Narwhal Labs’ ad, it says: “Imagine a workforce that doesn’t need annoying, expensive people.”
Companies are tempted by the promise of workers who don’t need food and sleep, who don’t agree with you and who don’t need to be cared for and fed, not to mention asking for a raise. What could be better?
We are already experiencing the early stages of what is not working in terms of this evolution of AI efficiency. Across industries, organizations are laying off people before AI systems are mature enough to reliably replace them. Teams are instructed to “implement AI” without clear workflows, operating models or expectations. So the programs die, and the people who are left need to carry the heavy burdens. One employee recently described it to me this way: “It’s like being in The Hunger Games. Everyone is being judged on how they use the AI, not knowing what to do with it and wondering who’s going to be kicked out next. It’s a mess.”
Companies are discovering that AI service agents have them real limitations in solving human customer problems. AI readers are also shown for display bias in AI written resumes compared to those written by humans. In many cases, both employees and customers find it difficult to serve effectively.
Again, this kind of efficiency-automation that AI promises has companies overjoyed with the kind of black-box magic outsourcing once promised, but it also undermines the human and business results of a transformation strategy that only works well. They confuse downsizing with strategic change.
While AI offers transformative capabilities, few companies—or jobs—are likely to remain competitive without learning how to use it effectively. But if companies want real change, they can benefit greatly by saying the silent 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, management should say so clearly. If the strategy is to free employees from repetitive work so they can focus on solving high-value problems, companies must define how that change will work and invest meaningfully in employee development. If the strategy is to add your secret sauce to product development with 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 that the technology will come later,” leaders have to be honest about that, too. At the very least, it would force a realistic discussion about the risks.
Many organizations try to simultaneously promise a better customer experience, lower costs, fewer employees, faster growth and happier employees, without agreeing to trade-offs or explaining the rules of the game to employees.
All great efficiency changes have eventually faced the same reality: organizations still work by motivating people. Successful people build thriving businesses and passionate customers. Because, by the way, customers are still people.
AI does not remove leadership’s responsibility to understand, respect and connect with people. If anything, it increases it. The opportunity to win with AI must increase leadership’s focus on ensuring that people use it with clear goals, rules and support—because the people who are most charged with AI, the least motivated will be the real competitive advantage.
The companies that win with AI won’t be the ones that quickly displace people. They will be the ones who decide most clearly where people are most important, where AI can truly add power and how the two will work together to deliver on the business promise.
