AI in business: replacing people or empowering talent? The choice that could define the future of companies
Artificial Intelligence has become the number one topic in boardrooms at virtually every major company in the world. And along with it came a question no CEO can afford to ignore anymore: do we use AI to replace people or to amplify what they do?
It sounds like a simple choice, but it carries enormous strategic weight.
On one side, there is automation — cutting costs, shrinking teams, doing more with fewer people. On the other, the augmentation approach — investing in people, expanding capabilities, creating value in ways that did not exist before.
Two paths, two completely different philosophies. And the long-term results will not even come close to each other.
Over the past few months, some of the biggest names in the corporate world have made it very clear which side they chose. And the data emerging about how employees perceive these decisions reveals something most leaders have not yet considered with the seriousness it deserves.
Spoiler: having a good AI strategy is not enough. People need to believe in it. 👇
Two CEOs, two radically opposite visions
In February of this year, Jack Dorsey laid off more than 4,000 people — nearly half of Block’s workforce. In a letter to shareholders, he was direct: AI tools had changed what it means to build and run a company. According to Dorsey, most other companies would reach the same conclusion within a year. This is the essence of automation: the company keeps doing what it has always done, but with fewer people running the operation.
On the other end of the spectrum, Micha Kaufman, CEO of Fiverr, also made waves — but with a very different message. In an open and remarkably candid letter to employees, he said AI was coming for every job, including his own. But Kaufman was not announcing layoffs. He was arguing that artificial intelligence would radically transform work and that everyone needed to adapt. In an interview with CBS, he added that by automating repetitive tasks, the freed-up time should be invested in areas where humans have special capabilities — nonlinear thinking, judgment calls, matters of taste, strategy. Even if every repetitive task were automated, employees would not be replaceable. Kaufman was betting on human potential.
These two examples are a perfect snapshot of the strategic fork every leader faces today. And the difference between the two approaches goes far beyond a philosophical question — it defines the kind of company being built for the next decade.
What separates augmentation from automation in practice
When we talk about automation, the concept is straightforward: you take a task a human used to do and hand it off to a machine. The process still exists, but the employee is removed from the equation. This cuts operational costs in the short term, simplifies repetitive workflows, and delivers predictability. In sectors like manufacturing, logistics, and basic customer service, automation has already proven it works, and there is no denying the gains it has delivered over the past few decades.
The problem starts when this logic is applied indiscriminately, especially to roles that involve judgment, creativity, relationships, and decision-making in ambiguous contexts. That is where things get a lot more complicated.
Receive the best innovation content in your email.
All the news, tips, trends, and resources you're looking for, delivered to your inbox.
By subscribing to the newsletter, you agree to receive communications from Método Viral. We are committed to always protecting and respecting your privacy.
Augmentation, on the other hand, starts from a different premise. Artificial intelligence is not introduced to replace the professional but to make them more capable than they would be on their own. A lawyer using AI to review contracts in minutes is still the lawyer making legal decisions. A doctor using AI for diagnostic imaging analysis is still the doctor interpreting the patient’s clinical context. A data analyst using AI to identify patterns is still the professional building the strategic narrative from that data. The human stays at the center, just amplified — with more speed, more context, and more capacity to deliver.
The difference between these two models is not just philosophical. It is deeply strategic. Companies that embrace the augmentation model tend to retain talent more easily, generate innovation more consistently, and build organizational cultures where people feel like part of the future — not threatened by it. Those that default to automation as the first answer to every efficiency problem usually harvest quick wins on the quarterly balance sheet, but face serious problems with engagement, internal distrust, and difficulty attracting top talent.
What the data reveals about the current landscape
There is clear evidence that many companies are following the automation path with a focus on cost-cutting. Goldman Sachs bankers estimate that the total headcount among their investment banking clients — spanning multiple industries — is expected to drop roughly 11% on average over the next three years because of AI.
The 2025 Indeed Workforce Insights report, which surveyed approximately 80,000 workers across eight countries, brought another telling finding: time saved through AI was, in most cases, redirected toward doing more of the same type of task or absorbed by other projects. Signs of genuine augmentation — such as creative work and innovation or increased client interaction — did not even appear among the top five use cases. This may reflect both the status quo bias of leaders and the still-untapped potential of AI. It is easier for executives to imagine using AI to optimize what people already do than to reimagine how it could generate entirely new value.
What employees actually think about all of this
A survey conducted earlier this year with 1,294 full-time office workers — in the United States, Canada, and the United Kingdom — produced highly revealing data about employee perceptions. These professionals spent more than 50% of their time on a computer and included individual contributors, managers, and senior leaders across 19 different sectors, with the majority in technology, financial services, healthcare, retail, and professional services.
The results show that approximately 62% of respondents believe their organization uses AI to augment employee capabilities. About 34% believe their company is using AI to automate work and reduce costs. And 4% remain uncertain. An important detail: only 44% of respondents said their organization had formally announced any AI plan.
But these numbers hide significant variations. In some sectors — like retail and professional services — between 40% and 50% of employees suspect that AI adoption is ultimately putting their job security at risk. Employees who perceive an automation intent also report more frequently that they feel forced to adopt AI rather than encouraged — a distinction that, as the data shows, goes well beyond team morale.
There is also a glaring disconnect between leaders and teams. A recent survey found that 76% of executives believed employees were enthusiastic about AI adoption, while only 31% of individual contributors agreed. In the survey mentioned above, 81% of senior leaders think their organization is fully committed to augmentation, while at the individual contributor level, only 53% perceive augmentation and 40% suspect automation.
This perception gap is a serious problem. Having an augmentation strategy is not enough — employees need to believe in it.
The three behavioral dynamics that change everything
When employee perceptions are taken seriously, three behavioral patterns stand out and help explain how people integrate — or resist — new tools in their daily work:
Fear of layoffs and declining well-being
In the survey, approximately 60% of workers expressed concern about job loss, with 32% reporting moderate to high concern. This threat erodes the sense of job security and, consequently, well-being. Research shows that declines in well-being are directly linked to drops in productivity, retention, and the ability to attract talent. Happy employees are roughly 13% more productive. When well-being drops, productivity drops with it. Leaders tend to underestimate how the ripple effects of layoffs — or even the mere threat of them — can undermine the very efficiencies they are trying to achieve.
Adoption without purpose creates workslop
When employees are told to use AI without clear guidance on why or how it will improve their work, engagement stays shallow. Instead of becoming pilots of the technology — actively taking control of the process — they behave like passengers, following instructions with limited conviction. This shallow engagement, combined with smaller teams and growing workloads, creates perfect conditions for workslop: the proliferation of low-effort, low-quality AI-generated work. Employees who feel forced to adopt AI show a 65% higher rate of producing workslop and significantly greater intent to leave the company.
Erosion of the junior talent pipeline
AI strategies focused on cost-cutting can hollow out the junior talent pipeline over time. Entry-level positions are where future leaders build judgment, professional networks, and expertise. Recent research from both Harvard and Anthropic shows that generative AI protects senior roles while compressing or eliminating junior ones. The result: fewer future leaders, aggressive dependence on external hiring, erosion of institutional knowledge, and a weakening organizational culture.
The productivity J-curve and what it means for your company
There is an economic concept called the Productivity J-Curve, developed by Erik Brynjolfsson and colleagues, that helps a lot in understanding this dynamic. When a new general-purpose technology is adopted, there is an initial period in which productivity actually drops — because the organization needs to invest in new processes, training, data infrastructure, and management practices before the gains materialize. Previous research suggests that organizational reorganization and skill development can require roughly ten times the investment of the technology implementation itself.
In the case of automation, this initial dip is relatively shallow and short. You replace human labor with AI on well-defined tasks, achieve similar or higher output with smaller teams, and the gains show up quickly in cost savings and increased throughput.
In the case of augmentation, the initial dip is deeper and longer, because it requires genuine organizational transformation, role redesign, and the development of effective human-AI coordination routines. However, the J-curve logic also suggests that augmentation carries far greater long-term potential: once the complementary investments are absorbed and the new sociotechnical routines stabilize, performance rises to reflect not just efficiency gains but an expansion of the organization’s productive frontier.
Augmentation is about inventing the future. Automation is about automating the past.
The automation path: a predictable downward spiral
When organizations signal that AI is primarily a tool for cost-cutting and headcount reduction, a predictable behavioral sequence unfolds:
- Resistance disguised as adoption: adoption appears to rise because it is mandatory, but engagement remains shallow. Instead of pilots, the organization generates passengers.
- Declining well-being: when layoffs begin, anxiety spreads, focus deteriorates, and initiative disappears.
- An explosion of workslop: smaller teams and low morale lead overloaded employees to use AI to fill gaps in workflows, often without adequate training or context. This produces low-quality work that undermines efficiency instead of improving it.
- Talent flight: high performers are usually the first to leave. Institutional knowledge dissipates. Innovation slows down.
- Deteriorating employer reputation: as layoffs, disengagement, and turnover accumulate, it becomes increasingly difficult to attract the kind of talent that drives growth.
- Loss of the leadership pipeline: junior roles disappear, continuity erodes, and future leaders simply are not cultivated within the organization.
What starts as an efficiency play can turn into a capability deficit that weakens innovation and growth, corroding the very talent and adaptability needed to realize AI’s full potential.
The augmentation path: a virtuous upward spiral
In contrast, when organizations signal genuine commitment to their people and pair that with careful AI integration and investment, a positive cycle takes shape:
- Engagement driven by curiosity: when employees believe AI was introduced to improve their work, they engage with curiosity and a sense of agency. Adoption rises from intrinsic motivation, not obligation.
- Well-being preserved, productivity rising: without the threat of layoffs undermining morale, well-being holds steady and productivity climbs with it. Sharper focus, greater motivation.
- High-quality collaboration: employees invest in collaboration, using judgment about when and where to use AI. With clear expectations and a culture of trust, workslop is kept to a minimum.
- Talent retention: professionals who perceive investment stay and thrive. Institutional knowledge accumulates across teams.
- Talent attraction: as the organization’s commitment to people becomes visible through culture, reputation, and career development, it creates a virtuous cycle of retention and attraction.
- Strengthened leadership pipeline: junior roles are preserved and reimagined, serving as training grounds for future leaders. Continuity builds, culture strengthens.
Real-world examples of companies that chose augmentation
One standout case is Aon, the global professional services firm. CEO Greg Case publicly emphasized the company’s commitment to its 60,000 employees, prioritizing AI literacy and treating headcount as a pillar of the future growth strategy. Aon’s approach focuses on training employees to work with AI and gain digital fluency, rather than replacing them. In 2025, Managing Director Lisa Stevens stated that the only job losses due to AI would be among those who refused to learn the technology.
This stance is not new for Aon. During the Covid-19 pandemic, Case made a public promise that there would be no layoffs for what was, at the time, a 50,000-person workforce — a decision funded by temporary salary cuts for top executives. So when Case signals today that AI will expand, not erode, opportunities at Aon, his team has concrete evidence that those promises are real. Laying people off during Covid would have been much simpler and, in the short term, more profitable.
Another relevant historical example comes from Microsoft. When Satya Nadella became CEO in 2014, the company faced a strategic inflection point: optimize its legacy software business or reinvent itself around cloud computing and AI. Nadella chose transformation — and paired it with investment in people. As described in his 2017 book Hit Refresh, Microsoft rebuilt its culture around continuous learning, shifting from a know-it-all organization to a learn-it-all one. The company redesigned roles across engineering, sales, and product to align with cloud and AI, investing heavily in reskilling. Rather than relying primarily on automation or headcount reduction, Microsoft focused on equipping employees to work alongside new technologies — an approach that helped fuel its resurgence as a leader in cloud and AI.
The numbers that back the augmentation argument
The survey data cited throughout this article reinforces the argument quite clearly. Employees who perceive augmentation intent report higher AI engagement, stronger collaboration, and 32% less intent to leave the company compared to those who perceive automation intent. Employees who feel ordered to adopt AI report lower well-being, higher intent to quit, and greater suspicion that the company is actually prioritizing automation.
These are early signals of a progression that could intensify over time. And they point to something that should be obvious, but that many leaders still overlook: the relative advantage of augmentation over automation in the long run is a function of human behavior. And that behavior is largely shaped by employee perceptions.
Why the right strategy starts with a clear choice
Companies that are winning the artificial intelligence race are not necessarily the ones with the most advanced model or the biggest technology budget. They are the ones that made a clear choice about why they are using AI and who this technology is working for. When that choice is explicit, communicated consistently, and backed by concrete actions, it creates internal alignment worth far more than any technology tool on its own.
This choice also has direct implications for how business strategy is designed. A company driven by augmentation will invest in training, redesign roles so professionals can better leverage AI capabilities, and create spaces for experimentation where teams can test new approaches without fear of failure. A company driven primarily by automation will invest in reducing headcount, centralize decisions in algorithms, and treat human capital as a cost variable to be optimized. These two approaches create radically different organizational cultures, and the difference becomes evident in results over two or three years.
What makes this especially interesting is that the two approaches are not mutually exclusive in every scenario. There are processes that genuinely make sense to automate — particularly those that are highly repetitive, low in added value, and that nobody particularly wants to do. The problem arises when automation logic starts creeping into territory where human judgment is still essential, and when that expansion happens without transparency, without dialogue, and without a clear plan for what happens to the professionals who held those roles. That is when employee perceptions plummet and the strategy starts cracking from the inside. 💡
The harder path is the smarter one
The augmentation path is not the easiest one. It demands a credible commitment to existing employees, even if that means a longer dip in the J-curve at the start. That includes deep investment in capability development as part of the AI technology rollout.
In practice, it can mean co-developing tools and business processes with employees to improve how they work, even if that requires change management, reskilling programs, and, when necessary, respectful right-sizing through natural attrition rather than layoffs. Employees will notice that commitment — or the lack of it — in the day-to-day decisions about which tasks are handed to AI and which stay with humans.
Choosing the path of human potential is the more imaginative and strategically demanding option. It requires leaders to take a leap of faith in their existing teams. It requires articulating a credible commitment to people, investing in their ability to use AI well, and redesigning workflows so that technology augments, rather than replaces, human judgment. It demands consistent, transparent communication grounded in trust. And it demands patience, because compounding advantages — just like compounding returns — are only visible to those who look beyond the next quarter.
AI is a test of whether leaders truly believe their people are costs to be minimized or potential to be amplified. The first approach produces a brief spike in perceived efficiency before a chain of behavioral dynamics begins to erode engagement, talent, and performance. The second produces durable long-term gains rooted in engagement and innovation.
At the end of the day, the AI revolution will not be won by whoever replaces people the fastest, but by whoever empowers people the best. It may be the road less traveled, but it is the one that will make all the difference. 🚀
