Gartner explains why support teams are still essential in the age of artificial intelligence
Artificial intelligence is transforming customer service at a breathtaking pace.
Companies around the world are ramping up their technology budgets with promises of cutting costs, automating processes, and delivering a faster, more efficient customer experience. It is no exaggeration to say we are living through one of the biggest technological shifts in corporate history, and the support sector is right at the center of this transformation. AI-powered tools are taking over tasks that once depended entirely on people, from answering simple questions to resolving complex technical issues in real time.
But is this race toward automation actually being done the right way?
A new report from Gartner raises a serious red flag: more than half of customer service organizations will double their technology spending by 2028. That number is significant and shows that technology investment has moved from optional to a core part of business strategy. But alongside this acceleration, a real concern is starting to gain traction in conversations among operations leaders and human resources managers.
There is a detail that many companies are overlooking in this whole equation.
Laying off support teams to fund this technological push could backfire badly — and the cost of that premature decision could be far greater than any short-term savings. 🚨 The report makes it clear that human talent is not disappearing from the corporate landscape. It is evolving. And ignoring that evolution could put at risk the entire support operation a company spent years building.
What the Gartner report is really saying
Before jumping to any hasty conclusions, it is worth understanding the full context of what Gartner mapped out. The report is not a manifesto against automation — quite the opposite. It acknowledges that artificial intelligence will handle an increasingly larger share of service interactions, especially those that are repetitive, predictable, and low complexity. Chatbots, virtual assistants, and automated triage systems are already delivering concrete results for companies that implemented them thoughtfully.
The core point of the warning, however, is about what happens after AI enters the picture. Many organizations see automation as an opportunity for immediate cost reduction, and the logic seems straightforward at first glance: if the machine can handle it, why keep so many humans around? But that math does not add up when you look at the data more closely.
Kathy Ross, Vice President and Analyst in the Customer Service & Support practice at Gartner, pointed out that many leaders expect quick wins from AI, but most organizations still need human contributions, since workforce requirements are changing — not shrinking. According to her, leaders are expecting AI to deliver immediate cost savings, but most organizations are underestimating the talent needed to make AI successful.
The report shows that the interactions reaching human agents after passing through AI tend to be the most difficult, the most emotionally charged, and the ones requiring the most skill to resolve. In other words, the work left for humans gets more complex, not simpler. Technology spending is climbing rapidly, but talent needs are evolving — not disappearing.
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On top of that, Gartner makes it clear that organizations that aggressively cut support teams experienced rising customer dissatisfaction rates, longer resolution times for critical cases, and a noticeable deterioration in customer experience overall. These are indicators that directly impact retention, customer lifetime value, and ultimately, the company’s revenue. Short-term savings turn into medium- and long-term losses — a cycle many companies only recognize when the damage is already done.
AI investment grows as executives target lower labor costs
As organizations continue investing in customer service technology, many executives expect AI to reduce labor costs, anticipating efficiency gains through automation, improved self-service, and faster resolution paths. With the expansion of AI tools, analytics, intelligent routing, and automation, a significant portion of work can be directed away from human agents, reducing the volume of interactions that require staffed support.
However, many companies are keeping their headcount and reassigning people to new tasks rather than simply eliminating positions. And while only 20% of organizations have actually reduced their employee count because of AI, Gartner warns that many other companies may be underestimating the value of human talent.
Despite the current number of organizations maintaining their overall headcount, many large companies that decided to conduct layoffs to invest in AI did so in the thousands. At the end of March, 30,000 Oracle employees received their immediate termination by email so the company could continue investing in artificial intelligence. By pursuing heavy investments in AI technology and data center construction, Oracle hopes to free up significant cash flow to sustain these initiatives, raising questions about the future availability of support services and human expertise.
Cases like this show that the pressure for quick financial results can lead to drastic decisions that do not always account for the full impact on operations. The pursuit of efficiency is understandable, but when carried out without proper planning, it can destabilize the entire service chain.
Humans and AI: why partnership matters more than replacement
The narrative that artificial intelligence will completely replace human workers in customer service is, at best, a dangerous oversimplification. What the data shows — and what more mature companies on this journey already understand — is that the most efficient model is not about replacement but about collaboration. AI is extraordinarily good at speed, consistency, and scale. It can handle thousands of interactions simultaneously, without getting tired, without mood swings, and with a data-processing capability no human can replicate. These are real assets that justify technology investment.
On the other hand, human support teams bring something no language model can authentically reproduce: genuine empathy, contextual judgment, and the ability to navigate ambiguous situations where the rules do not apply cleanly. A customer who missed an important deadline, who is frustrated by an incorrect charge, or who is dealing with a technical issue affecting their business does not just want a quick answer. They want to feel that someone on the other side truly understands the situation and is committed to resolving it. That human connection has a direct impact on brand perception and long-term customer loyalty. 💡
Companies that choose to remove customer support teams in favor of larger AI investments frequently face service gaps and higher recovery costs down the road. Reducing the human workforce can extend resolution times for non-standard issues, generating repeat contacts, complaints, and even regulatory attention in industries with strict compliance requirements.
Beyond that, customers today expect reliable access to a real person when they need one. Removing that option entirely can reduce satisfaction and hurt retention, especially during high-stress or high-value interactions.
The model that is working for the most advanced companies is exactly this: using artificial intelligence to absorb volume, filter out simple interactions, and deliver relevant information to human agents before they even start the conversation. This means that when a human enters the interaction, they already have the full context, the customer history, and AI-generated solution suggestions. The result is a customer experience that combines speed with depth, and agents who can resolve more in less time without sacrificing service quality.
The human role in the early stages of AI deployment
There is an aspect many companies underestimate when they decide to cut teams quickly: in the early phases of every new AI deployment, these models require training, monitoring, and correction performed by qualified human employees. Additionally, integration demands constant oversight, data quality work, and ongoing fine-tuning.
By cutting teams too fast, a company reduces the internal capacity needed to maintain the accuracy and reliability of its systems. When automation errors happen — and they do happen — those failures can spread across multiple channels. Without a human team ready to step in, these problems cannot be contained or corrected before they affect a large customer base, causing real damage to the interaction experience.
While AI can improve workflow efficiency, customer support teams remain essential for quality, stability, risk management, and long-term customer relationships.
Smart cost reduction: the approach that actually works
Cost reduction is a legitimate and necessary goal for any service operation. The problem is not the goal itself but the strategy chosen to achieve it. Cutting people without first understanding which functions can truly be automated is like performing surgery without a diagnosis — the risks are high and the consequences can be irreversible. Technology investment needs to be accompanied by a clear mapping of where AI adds value and where humans are irreplaceable, and that analysis takes time, data, and a strategic vision that goes beyond the next quarter.
Emily Potosky, Senior Director and Analyst in the Customer Service and Support practice at Gartner, emphasizes that companies reducing agents to fund AI should instead focus on reallocating those teams to higher-value roles that support growth. According to her, organizations are not cutting agents because AI is fully ready to take over — they are cutting agents to fund AI. Rather than replacing the workforce, leaders should prioritize reshaping it, redirecting resources toward higher-value activities that support growth.
Companies that are successfully balancing these forces generally follow a similar path: they start by automating the highest-volume, lowest-complexity workflows, measure the real impact on customer experience before making staffing decisions, and reinvest part of the savings into training and developing the human agents who remain in the operation. This virtuous cycle creates a smaller but far more capable team, working alongside artificial intelligence tools that amplify every interaction. The result is a leaner operation without sacrificing what truly matters to the customer.
To ensure their AI investment strategies are effective, organizations should prioritize tools that improve specific outcomes, such as:
- Ticket resolution speed
- Interaction routing accuracy
- Knowledge management and information databases
- Governance and oversight of automated systems
- Training and reskilling of the existing workforce
Adopting AI simply because it is trending, without a clear improvement objective, is a trap that many companies still fall into. Technology needs to serve a measurable purpose within the operation.
Another point that tends to be underestimated in this equation is the cost of replacing talent. When a company lays off its support team en masse and later realizes it needs to rehire, the investment required to recruit, onboard, and train new agents is significant — not to mention the time those professionals need to reach the desired productivity level. Maintaining a solid core of human support teams during the technology transition is not an unnecessary cost. It is a strategic safeguard that ensures the operation keeps running while AI matures and integrates consistently into the service environment. 🎯
Rapid headcount reductions can disrupt operations, degrade customer experience, and lead to costly reversals triggered by hasty decisions. That is why workforce changes should be planned carefully, keeping humans in place where judgment and empathy are needed.
Reskilling as a core strategy
The Gartner report reveals that most customer service leaders expect roles to evolve alongside AI. This means that instead of planning for full automation, companies should be reskilling their workers for supervisory, governance, quality control, and complex or escalated interaction roles.
By treating AI adoption as an end-to-end change, technology spending should be matched with investments in implementation and oversight capabilities. This includes operational readiness, governance frameworks, clear data practices, and monitoring processes to ensure successful adoption.
Combining strategic technology adoption with workforce evolution allows organizations to use technology to raise service quality and free up humans for higher-value tasks — rather than using AI exclusively to reduce headcount.
What to expect for the future of customer service
The landscape taking shape over the next few years points to a profound transformation, but not a total disruption. Artificial intelligence will continue evolving in capability and accuracy, taking on increasingly complex functions within the service cycle. More advanced language models are already handling negotiations, return processes, and even emotionally sensitive situations at a level of sophistication that would have been unthinkable three years ago. This will keep happening, and companies that fail to keep pace with this technology investment will face real competitive disadvantages.
At the same time, human support teams will transform into high-level specialists, responsible for the most complex interactions, for overseeing AI systems, and for building relationships with strategic customers. This new agent profile demands different competencies from those valued ten years ago, including analytical skills, fluency with AI tools, and the ability to interpret data in real time. Companies already investing in developing these competencies today are building an advantage that will be hard to replicate in the future.
The customer experience will also change significantly in this context. Customers are already getting used to interacting with AI systems and, in many cases, prefer that option for resolving simple issues quickly and independently. But when the problem is serious, when the situation is urgent, or when emotions are involved, a human presence remains the differentiator that determines whether a customer stays loyal to the brand or moves to a competitor.
This balance between automation and humanization is not a passing trend — it is the new standard of excellence in customer service. And the companies that understand this sooner will reap the rewards of that vision for much longer. 🚀
