A few months ago, I walked into the office of one of our customers, a publicly traded vertical software company with tens of thousands of small business customers. I expected to meet a traditional support team with rows of agents on the phones, sitting at computers triaging tickets. Instead, it looked more like a control room.
There were specialists monitoring dashboards, tuning AI behavior, debugging API failures, and iterating on knowledge workflows. One team member who had started their career handling customer questions over chat and email (resetting passwords, explaining features, troubleshooting one-off issues, and escalating bugs) was now writing Python scripts to automate routing. Another was building quality-scoring models for the company’s AI agent.
This seemed markedly different from the hyperbole I’d been hearing about customer support roles going away in large part due to AI. What I was seeing across our customer base looked more like a shift in how support work is defined.
So I decided to take a closer look. I analyzed 21 customer support job postings across AI-native companies, high-growth startups, and enterprise SaaS. These jobs run the gamut from technical support for complex software products to more transactional, commercial support involving billing and other common issues.
What I found was that customer support is being rebuilt around AI-native workflows and systems-level thinking. Yes, responding to individual tickets is still important, but roles are designing and operating the technical systems that resolve customer issues at scale.
The result is a new kind of support role, one that’s part operator, part technologist, part strategist.
AI Skills Are Now Table Stakes
For most of the last two decades, support hiring optimized for communication skills and product familiarity. But that baseline is now gone.
Across the 21 job postings I analyzed, nearly three-quarters explicitly required experience with AI tools, automation platforms, or conversational AI systems.
These roles are about configuring, monitoring, and improving the AI systems over time. They are reviewing conversation logs, auditing AI behavior, and identifying failure modes.
In other words, AI literacy has become the baseline for modern support work. If you don’t understand how AI systems behave, you can’t support the customers relying on them.
More than half of the roles I analyzed required candidates to debug APIs, analyze logs, write SQL queries, or script automations in Python or Bash. Many expected familiarity with cloud infrastructure, observability tools, or version control systems like Git.
That would have been unthinkable in support job descriptions even five years ago.
But it makes sense. When AI systems fail, they fail at scale. Diagnosing those failures requires technical fluency like understanding how models interact with external systems and when an issue is rooted in configuration versus product logic.
The job has evolved from fixing problems ticket by ticket to preventing the next thousand tickets.
Humans are Needed to Solve Harder Problems
Once AI becomes part of the support workflow, the nature of the work becomes more technical. One support leader I spoke with at a company that now contains more than 80% of its tickets with AI put it plainly: once automation handles the easy questions, the work left behind gets harder. The same frontline agents who used to focus on quick wins are now handling the most frustrated customers and edge cases, and they’ve had to scale up their skills accordingly.
In practice, this often looks like a customer trying to complete a critical workflow, like syncing data between systems before running billing. An AI agent starts by working off documentation that a subject matter expert has synthesized from multiple functions across the company. From there, the AI agent can confirm that everything is configured correctly. However, the AI agent may not be integrated to the right underlying system that failed silently hours earlier. The customer follows the guidance, only to discover downstream that data didn’t move as expected. When the issue escalates, the subject matter expert has to reconstruct what happened across systems, reason through what the AI agent missed, and help the customer recover without losing trust.
This is the kind of end-to-end work that AI still can’t do on its own. It requires both technical fluency to trace failures across disparate systems, in addition to human judgement to decide what can be fixed immediately versus what needs deeper product or engineering intervention. In this way, support has become less about answering questions out of the manual, and more about creating the manual and solving the problems that it doesn’t cover.
The Hybrid Human–AI Model Is the Default
Despite widespread fear about AI replacing support jobs, not a single posting I analyzed suggested that support would be 100% automated in the future.
Instead, nearly every role gravitated toward a hybrid model where AI handles routine interactions, while humans oversee quality and continuously improve the system.
This makes sense when you consider the fact that 95% of customer support leaders said they would retain human agents in their operations to help define AI’s role when surveyed by Gartner last year.
Titles like “AI Support Specialist,” “AI Quality Analyst,” and “Support Operations Specialist” were almost entirely focused on orchestration, designing escalation logic and defining when humans step in.
This is where the earlier “control room” image becomes reality. The work of humans changes from simply answering questions to actually shaping systems.
Taken together, these trends point to a single conclusion: customer support is specializing. The repetitive work is going away, but the judgment-heavy, technical work is expanding. That shift is already visible in how companies hire. The question now becomes whether organizations (and workers) are ready to adapt fast enough.
