Is AI supposed to be labor-serving or labor-saving?” is one of the most often asked questions when talking about AI transformation. Time and time again, we (at MTS Solutons) emphasize the same point:
AI is meant to be labor-serving, not labor-saving.
Henry Ford – founder of the 121-year old Ford Motor company – addressed a similar concern a century ago. As the mechanized assembly line revolutionized industry, many feared machines would replace workers entirely. In 1929, Ford put it plainly [1]: “For unless machinery is labour-serving, it has no excuse for being.” We can adopt that sentiment and say:
“For unless artificial intelligence is labor-serving, it has no excuse for being”
We see the same fears today with AI. Some predict that artificial intelligence will inevitably take over many human jobs, leaving workers with no place in the economy. But history tells a different story.
When stronger, faster, higher-powered machines emerged in the early 20th century, it didn’t eliminate work — it transformed it. Businesses became more productive, industries expanded, and new jobs emerged that were previously unimaginable. Furthermore, the new jobs required more human intelligence, not less.
Ford also noted that the power generated to run the machines by itself, is meaningless. You need people to extract the benefits from the abundance of power through the use of machines.
The same applies to AI today. The raw computing power of modern AI chips is useless unless it’s used to develop algorithms that enhance human productivity, helping people perform their jobs better, not eliminating them.
That said, of course some tasks and jobs will be fully automated by artificial intelligence — but only where automation makes sense. A good rule of thumb is this: if a task requires no judgment, no creativity, and no decision-making, then it’s better handled by AI. There’s no point in having a person spend time on repetitive, mindless tasks when they could be focusing on higher-value work — work that requires out of the box thinking, intuition, problem-solving, or relationship-building.
“The old method works just fine—so why change it?“
… is probably the biggest hurdle we face when introducing new technologies. It’s not the technology itself that holds companies back — it’s our natural resistance to change. Many hesitate to shift away from familiar ways of working, often because they see new systems as disruptive, unnecessary, or at that moment even performing worse. But adopting technology for the sake of novelty isn’t the goal. The real question isn’t whether something is new, but whether it’s better.
The only way to break through this resistance is through results. Time and time again, we must prove that new technologies aren’t about replacing people or cutting corners — they’re about making work more effective, reducing inefficiencies, and allowing teams to focus on what truly matters: serving the public.
Why AI?
If we look beyond the buzzword, AI is really just about using the immense raw computing power of modern computer chips to mimic and automate human activities — whether it’s writing stories, painting pictures, answering customer questions, or making sense of trends and data. The automation makes sense if past events accurately predict future events.
The reason why we can do today what we couldn’t do twenty years ago is the availability of immense, raw computing power. With the help of modern AI chips we can define and describe human activities using billions or trillions of parameters – something impossible by humans. So, no matter how complex we find an activity, if past events accurately describe future behavior, we can use chips to turn the process into a (very big) algorithm, and then leverage the algorithms to automate the process.
Now, imagine applying that same idea to running a business. In theory, any process — from hiring employees to managing inventory to responding to customer inquiries — can be learned and automated by AI. We’re already seeing this in action. Chatbots handle customer service, AI-powered tools draft emails and reports, and smart software helps businesses detect fraud, optimize supply chains, and predict sales trends. But right now, most of these AI tools work in isolation—each one doing its own specific task.
That’s about to change. Over the next couple of years, Agentic AI will evolve from handling individual tasks to managing entire workflows. Instead of just answering customer service chats, it’ll track orders, schedule follow-ups, highlight anomalies, and recommend actions — all without direct human intervention.
Think of it like the early days of factory automation—first, machines replaced individual manual tasks, and eventually, entire assembly lines became automated. This transformation isn’t just about efficiency — it’s about re-imagining how businesses operate. Just like factories in the 20th century scaled up production like never before, AI-driven businesses will be able to scale decision-making, creativity, and problem-solving at levels we’ve never seen. And that’s where things start to get really exciting.
[1] Ford, H. (1930). Moving forward. Doubleday, Doran & Company.