By John Ragsdale, SVP Marketing, Kahuna Labs
Most AI strategies in support are unintentionally building the business case to eliminate themselves.
That’s the uncomfortable reality support leaders need to confront.
Yesterday, Tom Sweeny, CEO of ServiceXRG, published the latest edition of his Support Leadership Unfiltered newsletter, Edition 12: AI Efficiency Is a Start. Not a Strategy, based on his Support Attribution Framework. One point in particular stood out to me: when AI enables support to handle more volume with fewer resources, the business does not automatically conclude that freed-up support expertise should be reallocated to higher-value customer work. More often, the business concludes that support simply needs fewer people.
That conclusion makes sense if efficiency is the only story support leaders tell.
Most AI business cases in support focus heavily on efficiency. Deflect more cases. Reduce handle time. Shorten time to resolution. Improve agent productivity. Lower cost to serve. All of these are valid goals, and in a cost-conscious environment, they are difficult to ignore.
But there is a strategic risk in building an AI plan around efficiency alone.
If the only benefit support leaders can articulate is that the same work can now be done with fewer people, then the obvious executive response is headcount reduction. A 20% productivity improvement quickly becomes a conversation about cutting 20% of the resources. That may satisfy a short-term financial target, but it misses the larger opportunity AI creates for support organizations.
Every support leader I know has a long list of valuable work their teams never have enough time to do. Support engineers are so consumed by managing queues, closing cases, handling escalations, and keeping up with customer communications that strategic work gets pushed aside. Not because it lacks value, but because there is no capacity.
That work includes cross-training newer engineers, sharing troubleshooting knowledge across teams, improving internal diagnostic playbooks, collaborating with engineering on better log analysis, identifying product friction points, documenting customer configuration patterns, supporting customer success with technical insight, and helping product teams understand where customers are struggling.
In many organizations, senior support engineers also have the expertise to contribute directly to revenue-generating activities. They can support Technical Account Management programs, participate in premium support offerings, advise customers on best practices, help with complex onboarding, and identify accounts that would benefit from additional services or training. But these opportunities are often left underdeveloped because the same experts are trapped in reactive case work.
This is where many AI strategies are incomplete.
When an AI tool is proposed, the plan usually includes a productivity assumption. It may estimate that engineers will resolve cases faster, handle more volume, or reduce manual research time. What is often missing is the next question: what will we do with the capacity we free up?
That question should not be an afterthought. It should be part of the AI strategy from the beginning.
Every support AI business case should include two components. The first is the traditional ROI model: how much time, cost, or effort can be reduced. The second is a redeployment model: how freed-up capacity will be redirected toward higher-value business outcomes.
Without that second model, support leaders leave the conclusion to finance. And finance will often draw the simplest conclusion: fewer people are needed.
With a redeployment model, the conversation changes. Instead of saying, “AI will reduce support effort by 20%,” the support leader can say, “AI will free up capacity that we will redeploy into escalation prevention, product feedback loops, customer enablement, premium support, TAM coverage, and proactive account intelligence.” That is a very different business conversation.
It also creates a more complete way to measure the value of AI.
The ROI of support AI should not be limited to cost savings. It should also include the business impact of what newly available capacity makes possible. Did escalations decline because senior engineers were able to intervene earlier? Did customer onboarding improve because support had time to identify common training gaps? Did product quality improve because support partnered with engineering on recurring diagnostic patterns? Did premium support revenue grow because SMEs were available for higher-value customer engagements?
Those outcomes are harder to measure than handle time reduction, but they are much more strategically important.
This is also where support has an opportunity to reposition itself inside the business. If support uses AI only to become more efficient, it risks reinforcing its identity as a cost center. But if support uses AI to free expert capacity for higher-value work, it begins to operate as a strategic source of customer intelligence, product insight, and revenue enablement.
That shift will not happen automatically.
AI will expose capacity. It will not decide how that capacity should be used. That is a leadership responsibility.
Support leaders should be asking a new set of questions every time an AI initiative is proposed. What work will this automate or accelerate? Which roles will gain capacity? How much capacity will be created? Which higher-value activities are currently underfunded or ignored? How will those resources be redeployed? How will we measure the business impact of that redeployment?
These questions should be answered before the tool is implemented, not after savings appear.
The companies that fail to do this will likely use AI to shrink support. The companies that get it right will use AI to expand the strategic contribution of support.
That is the real opportunity.
AI efficiency is important. But efficiency without a redeployment strategy is just a cost-cutting argument waiting to happen.
Support leaders need to make the next move before someone else makes it for them.
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