You’d be hard pushed right now to find a board or a senior executive team that isn’t considering leveraging AI to help them reduce operational costs, improve productivity, and boost efficiency.
As a result, many operational leaders and executives are under pressure to deliver on those board-level and C-suite aspirations.
Nowhere is this more true than in the service and support space, where the cost of labor has always played an outsized role in operating budgets compared with other departments and functions.
For example, research shows that headcount/labor, on average, accounts for approximately 70% of all operating costs of a service organization, while labor costs across other business functions often range from 15% to 30%.
Therefore, it should come as no surprise that service and support leaders are under pressure to reduce labor costs, with research by Gartner showing that 79% of service and support leaders expect to operate with a lower headcount in 18 months’ time, primarily through increased use of AI.
But what if those expectations were not realistic or accurate?
I don’t mean expectations around efficiency or productivity. It’s clear that the right AI tool or system, implemented effectively and fueled by the right data, will, if pointed at the right problem, in all likelihood deliver efficiency and productivity benefits.
That’s not the bit I am questioning.
I’m talking about cost-saving expectations and the implicit assumption from boards and executive teams that goes something along the lines of: ‘We want you to do way more for way less.’
I say this for two reasons:
One, with the advent of AI, the nature of enterprise software has fundamentally changed. Historically, maintenance and development of a software platform largely sat with the vendor. Now, with the advent of AI-powered systems, that responsibility is shared with buyers, who are increasingly responsible for the customization, development, and management of their own instance.
This is having an impact, particularly in the service and support space, on the types of people and skills that service and support leaders need to train or hire to support and develop their AI platforms.
And, two, while we are still relatively early in this new AI-powered era, I don’t believe we have yet achieved a clear picture of the true economics of AI operations that captures the costs of compute, inference, licensing or usage.
The Hidden Cost Equation
My concerns are supported by a recent Gartner research report, The State of the Human-AI Workforce in Service and Support, which finds that as automation levels approach 50% across a service and support estate, additional licensing costs, increased expenditure on specialized labor to support AI implementation, management, and development, and usage costs may exceed the savings from any frontline headcount reductions.
As the Gartner report notes:
“When the total cost of ownership is accounted for, a GenAI-powered service organization may be more expensive to operate than the equivalent human workforce.”
The problem, according to Emily Potosky, Senior Director, Analyst in the Gartner Customer Service and Support Practice and one of the report’s lead authors, is two-fold.
First, Potosky says that “there’s research out there to suggest that (AI) prices are invariably going to have to go up because the costs are currently being highly subsidized,” but, secondly, suggests that when it comes to labor costs, “people are looking at AI as a way to reduce service and support costs. It’s not a one-to-one.
“Organizations are not going to be able to fully recoup headcount reduction because that spend is going to have to go right back into the technology investment, the infrastructure modernization for some of these companies, the net new talent. And in some organizations, they may end up with a more expensive service operation if they go that AI-first route than they would have if they had stuck with their existing talent pool.”
This is a paradoxical finding given AI’s promise.
But it aligns with another of Gartner’s recent findings, which suggests that by 2030 it may be cheaper to outsource your support function to an offshore partner than to power it with AI.
Picking the Right Tool for the Right Job
So, what should service and support leaders do? Potosky recommends a couple of things:
One, she suggests that service and support leaders shouldn’t rush to use AI for everything, as that could end up being an expensive route to go down, but rather leaders should be thinking carefully about picking the right tech for the job at hand.
Second, Potosky suggests that service and support leaders lean into this complex and evolving cost picture.
They should start by setting their own strategy and working to set senior executive and board expectations about the realities of delivering a future-ready service operation, with AI at its heart, and what budget they will need to achieve that. Because if they don’t, then there is a risk that their strategy will be set for them.
Service Leaders Must Own the Narrative
This is sound advice, and is something I have been advocating for for years.
I remember speaking at the annual Contact Centre Management Association (CCMA) conference in the UK about five years ago and suggesting that service and support leaders should advocate more for themselves and for the importance of their work, because only they know the true nature of the work they do and what is needed to deliver the best outcomes for customers, employees and the business.
It was true then, and it’s becoming even more important now.
As a result, service and support leaders must take control of the AI narrative for their area of the business, moving past the myth of ‘more for less’ to proactively define a budget-conscious, deliberate strategy that truly reflects the total cost of ownership. Only by stepping up and clearly setting expectations with their boards and C-suite can they ensure their future-ready service operation succeeds without succumbing to the emerging AI cost paradox.
