By Sarah Nicastro, Founder and Editor in Chief, Future of Field Service

You have my word that this isn’t turning into a situation where every article I publish is an AI article, BUT I came across a LinkedIn post last week that I had to share some thoughts on. It was discussing an example that perfectly illustrates a point I’ve been trying to make about how important mindset is in AI innovation. So, yes, this week’s article is (another) AI article – but a worthwhile one, I promise!

Earlier this year, I wrote about my belief that the future of service success hinges on understanding the distinction between a mindset of optimization versus one of amplification.

As I state in that article, while optimization is about perfecting the present, amplification is about reimagining what’s possible.

Amplification asks a fundamentally different set of questions than optimization does:

• How do we deliver exponentially more value, not incrementally more efficiency?
• How do we scale expertise, not just output?
• How do we elevate the role of service from execution to impact?

There’s Risk in a Narrow View of AI Impact

For a long time, we’ve talked about technology as a great enabler – and I think this exacerbates the tendency to stay focused on optimization. AI, however, is far more than an enabler – it’s an amplifier.

One of the biggest risks organizations face right now is underestimating the role of AI.

Too often, we view modern AI through the same lens as we’ve viewed previous iterations of technical innovation. As such, it is considered as a tool for efficiency:

• Reducing costs
• Automating tasks
• Streamlining operations

While those benefits are real, they are also limiting.

AI is far more powerful when viewed as an amplifier – a force multiplier that expands what your people and your business are capable of.

When considered as an amplifier, AI allows organizations to:

• Scale expertise across the workforce
• Deliver proactive and predictive service at scale
• Enhance decision-making in real time
• Create richer, more personalized customer interactions

But I’m not here to regurgitate these previously made points; I’m here to share some excerpts from the post I came across on LinkedIn that offered a real-world example of the amplification approach.

Chris Donnelly, co-founder of Searchable.com, posted about IKEA’s use of AI which caught my eye because it’s a compelling example of the sentiment I’ve been trying to impart. His post explains that IKEA’s largest franchisee, Ingka, launched an AI assistant called Billie (in 2021) that – within two years – was handling 47% of customer queries to its call centers.

IKEA Creates New Service Revenue by Redeploying AI-Replaced Talent

But rather than limit its view of what’s possible at the obvious option of reducing headcount, IKEA thought about how this could amplify its business potential. The company retrained 8,500 call center workers as remote interior design advisers. In its 2022 financial year, that channel brought in $1.4 billion in sales and Ingka is aiming for it to reach 10% of total revenue by 2028.

Instead of investing in AI to replace humans and reduce cost, IKEA saw the opportunity to leverage AI to free up its human talent for work AI can’t do – keeping employees in roles while creating a new service for customers and new revenue stream for the business. This is amplification at its finest.

While Donnelly’s post didn’t offer a source, Fortune published an article in July of this year on “Ikea’s Big Bet on Humans in the Age of AI.”

IKEA’s story shared by Chris caught my eye, but the comments kept me around for quite a while as there were some excellent and diverse points made. Here are a few that offer important insights to consider:

• Leaders must think about how to protect tacit knowledge (especially when customer-centered). Abhishek Mishra said, “The retraining part is what most companies skip. Moving call center agents into design advice works because they already know the customer's questions better than anyone else in the business. Cutting those roles would have thrown that knowledge out along with the headcount.”

Caleb Ramsey adds, “The detail that matters is that IKEA didn't let those 8,500 people go. AI handled 47% of the questions, and the people who used to answer the phone moved into design advice because they already knew the products and the customers. Knowledge is the last thing I'd cut. AI can learn your answers in a few weeks, but it takes people years to learn your customers. I am curious to see how far consumers will allow customer service to automate. I have a feeling a lot of corporations might overshoot then slowly retract until they find that line. Will be an interesting few years ahead!”

• AI will change jobs, innovative companies will know what to do with people when it does. Asad Muhi Ud Din says, “The part of this story that matters isn't the AI replacing roles — it's IKEA redeploying 8,500 people into remote interior design advice and building a €1.3 billion channel from it. That's the model: use AI to clear repetitive work, then move people to the work AI can't do, where their product knowledge actually sells. The question for every service leader isn't whether AI changes jobs, but where you put your people once it does.”

• Cost-cutting needs to give way to reimagining human potential. Bola Matel-Okoh said, “The most important part of this example is that AI adoption became an opportunity to rethink how human talent creates value, rather than simply a way to reduce costs. Redeploying people successfully requires organizations to identify new customer needs and invest in the skills needed to meet them. The real competitive advantage comes when technology makes existing talent more valuable by freeing people to do work that requires human judgment, creativity, and connection.”

Vladyslav Shabalov adds, “The smartest part of this story isn’t replacing repetitive work with AI. It’s creating a new revenue stream from the human skills that were previously underused. I think many companies will miss that second step. They’ll celebrate lower support costs while competitors use the same technology to create better customer experiences and new ways to sell. There’s a catch, though: redeployment only works if customers actually value the new service. IKEA connected efficiency to a real buying need, not just a headcount target. That’s the difference between using AI to cut costs and using it to grow the business.”

• The capacity AI creates demands thoughtfulness. Joe Donnely says, “Automating a task creates capacity. Turning that capacity into better service takes a deliberate decision about how the work and people’s roles will change. Kudos to IKEA.”

• Judicious AI innovation builds trust and sets the stage for sustainable competitive advantage. Danielle Basely said, “Trust is the part people underprice. A bad first deployment does not just fail on its own terms; it makes the next three harder because everyone has already decided what AI means in that building. Adoption on tool two is mostly a function of how you handled tool one.”

Gabor Markus adds, “An interesting illustration of why AI adoption and AI transformation are two very different things. What makes this case particularly interesting isn’t the automation itself, but the organizational capabilities behind it: workforce reskilling, process redesign and strategic resource reallocation. AI creates opportunities. It’s how organizations reorganize around those opportunities that may determine their competitive advantage.”

And, finally, Dr. Martha Boeckenfeld weighs in with an important point saying, “We also have to admit that this is not possible for all companies but shows that there is still huge potential.” IKEA’s ownership and a variety of other factors may make companies with different structures or in other industries feel this example isn’t relevant for them. And while there may be aspects of it that isn’t, I do believe it underscores a mindset around AI innovation that’s really important – that if we think about how AI can amplify the potential of our human talent and our business, we’ll get much farther than if we maintain a narrow view of what the technology makes possible.