A recent Garner report has revealed that over 50% of customer service organizations will double their technology spend by 2028.
As more enterprises increase technology investment to reduce labor costs with AI, many companies are underestimating the ongoing need for skilled employees alongside the technology.
Instead of replacing departments with technology, enterprises should focus on realigning the workforce with higher-value activities.
Kathy Ross, Vice President Analyst in the Gartner Customer Service & Support practice, argues that many leaders now expect quick savings from AI, but most organizations still need human input as workforce requirements shift rather than decline.
“Leaders are hoping that AI will deliver immediate cost savings, but most organizations are understating the talent required to make AI successful,” she said.
“Technology spend is rising rapidly, yet talent needs are evolving – not disappearing.”
AI Investment Grows as Executives Target Lower Labor Costs
As organizations continue to invest further in customer service technology, many executives expect AI to lower labor costs, anticipating efficiency gains from automation, improved self-service, and faster resolution paths.
By expanding AI, analytics, routing tools, and automation, these tools can shift work away from human agents and reduce the volume that requires staffed support.
However, many enterprises are keeping their headcount but reallocating people to new tasks instead of removing roles.
And whilst only 20% of organizations have reduced their headcount because of AI, Gartner warns that many other enterprises may be underestimating the value of human talent.
Despite the current number of organizations keeping their overall headcount, many large companies that have decided to make layoffs to invest in AI have done so in the thousands.
At the end of March, 30,000 Oracle employees were emailed their immediate resignation from the company so that the vendor could continue investing in AI.
By pursuing heavy capital investment in AI technology and data center build-outs, Oracle hopes to free up significant cash flow to support these initiatives, raising questions about the future availability of support services and human expertise.
Why Human Judgment Still Matters in Customer Service
Companies that decide to remove customer support teams in favor of larger AI investments often face service gaps and higher recovery costs later down the line.
When AI can be used to handle routine queries, many customer problems still require context, negotiation, or exceptions, meaning human agents remain important to provide judgment and flexibility that automated systems cannot match.
Reduced human staffing can also lengthen customer resolution times for non-standard issues, likely leading to repetitive contacts, complaints, and regulatory attention in sectors with strict compliance needs.
Customers are now also expecting reliable access to a person when needed, so removing that option completely can lower satisfaction and harm retention, especially during high-stress or high-value interactions.
During the early stages of these next AI deployments, these models will require training, monitoring, and correction from skilled human employees, as well as requiring oversight for integration, data quality work, and ongoing fine tuning.
By cutting teams too quickly, this can reduce the internal capacity needed to maintain accuracy and safety, and an enterprise may struggle to manage the implementation effectively during those early stages.
When automation errors occur, these issues can spread across channels, meaning with no human team input, these issues cannot be contained or corrected before affecting a large customer base, causing damage to the interaction experience.
Whilst AI can improve workflow efficiency, customer support teams remain important for quality, stability, risk management, and long-term customer relationships.
Balancing Automation With Human Expertise
Enterprises that choose to increase spending on CX-focused technology should follow strategic actions to implement these capabilities effectively rather than focusing solely on automation and employee removal.
Emily Potosky, Senior Director, Analyst in the Gartner Customer Service and Support practice, suggests that companies reducing agents to fund AI should instead focus on shifting staff into higher-value roles that support growth.
“Organizations aren’t cutting agents because AI is fully ready to take over,” she said.
“They’re cutting agents to fund AI. Instead of replacing the workforce, leaders should prioritize reshaping it – shifting resources toward higher‑value activities that support growth.”
To ensure companies remain strategic in their AI investment efforts, organizations should prioritize tools that improve specific outcomes, such as resolution speed, routing accuracy, and knowledge management, rather than adopting AI just because it is fashionable.
Organizations should also balance automation capabilities with human skills, aligning technology to targeted staff rather than eliminating roles completely.
Furthermore, quick headcount reduction can disrupt operations, degrade customer experience, and lead to costly reversals from rash judgements.
As a result, organizations should plan workforce changes carefully, keeping humans where judgment and empathy are needed.
Gartner’s report reveals that most customer service leaders expect roles to evolve with AI, meaning companies should be reskilling workers for oversight, governance, quality control, and handling complex or escalated interactions rather than planning complete automation.
By treating AI adoption as an end-to-end change, technology spend must match with investment in implementation and oversight capabilities, ensuring operational readiness, governance structures, clear data practices and monitoring processes for successful adoption.
Combining strategic adoption of technology with workforce evolution will allow organizations to utilize technology to enhance service quality and free humans for higher-value tasks, rather than using it solely to reduce headcount.
