I see a lot of Customer Support job posts now, as I'm keeping an eye on the market with an eye to reentry. They all have a theme: "You will be responsible for reducing support volume, keeping the team headcount growth at zero (or negative), and deploying AI in every facet of the customer experience." They often have numbers for volume reduction, team size, documentation changes. Or they want you to handle 10-20x volume increase while still shrinking your team. The unwritten piece is that you're probably expected to shrink the team down to 1 or 2 max -- yourself and an operator/ML engineer. The pay is great. The incoming stress is greater.

The conversation that forms around these types of posts focuses on the language in the job description. Or on the new tools you'll have to bring. Or, in higher EQ conversation, about the human impact of shipping these programs. The tone is very – “Is the goal to improve the appearance of efficiency? Is it to reach the most barely sustainable level of understaffing? Are you a good or bad person for taking this role?” Quite often the comments devolve into pointing fingers over who is using AI to formulate their arguments in the LinkedIn thread.

Everyone is wrong though. We're missing the point. We're looking at the entire landscape with a magnifying glass when we need to snag a drone and head up, up, up and get a better view of the landscape.

We've been here before. Twenty years ago, software companies were optimizing their development process by looking at headcount, lines of code shipped, and tooling. Then Agile came along -- continuous deployment, communication, and tools that made the whole process more visible. Teams really did become much faster. So fast, they started being able to pull the customer into the conversation. A 2 week sprint meant small changes that added up, and progressive product managers who started talking to customers and asking "What do you want?" UX interviews, Solutions Engineers sitting between Sales and Engineering increased the feature request flywheel. Product teams built real power because they had real data about users and their wants and needs. A two week timeline gave customers confidence that their preferences would show up in their user interface. Product-led growth fundamentally changed how software companies operate.

Customer teams (Support, Success, Onboarding) already have a wealth of rich, nuanced data. The data is not just in what customers are submitting tickets about, but in what they are asking, when they are asking it, how often they are speaking up about it, and the things they want that they don't really know how to ask for. Every frustrated customer, every email/phone/chat/social media post multi-ticket blast, every ghosted support thread, every long-winded "Anyway what I'm really trying to do is X" is felt keenly by support teams. We hire people into these roles because they care. They care deeply. The support pros have the added trait of loving a good mystery. Imagine an entire team of Nancy Drew, Frank and Joe Hardy, and Sherlock Holmes, all holding real empathy for the customer and a need to get to the bottom of every question bordering on obsession. (A very intense, maybe even diagnosable, obsession in some cases. I did mention Sherlock.)

So what makes Support different from Product? They are both talking to the same customers, presumably.
The answer is authority. Product has a real seat at the table. Their entire job is to design and plan the product that gets shipped. They write the roadmap. They tell Engineering what to work on, who works on it, and when it needs to be done. They define "good."

Support? Support is a cost center. An entry-level team. The place you get "promoted from" if you're lucky. They are often called the "Frontline Team." Do you know what "Frontline" means? It means the people who protect the rest of the company from interacting with the people they don't want to interact with. They protect the "important" people from having to mix with the peasantry. They take a bullet for the troops behind them. (Seriously, listen to how people respond when someone says they are in support – "OMG I could never. You must have SO MUCH patience. I would burn out in like, a day.")

One thing I want to acknowledge here is that this does somewhat fall on support teams. For a long time, teams were completely overwhelmed with building the processes to handle large volumes of support. I've watched companies go from phone and mail support (yes - as in USPS 1st rate postage letter) to launching live chat and answer bots, all the way to AI agents who can handle questions. I've overseen documentation projects, video support launches, and the tedious growth from human-based tier 1 support to a queue full of really unique support requests -- the kind of stuff you can't automate and macro your way out of. Being so close to the micro problems has stunted the growth of our ability to learn to communicate in the P & L language the exec suite lives in. We also had to spend our time simply begging for other teams to look at our data; to see what it is that customers are struggling with and why that one bug is so pernicious. Fun fact: many people move from Support to Engineering not because of some big dream of becoming a software engineer, but because someone had to fix that bug and they got tired of no one doing so. So they learned how to DIY that sucker. Tripling your pay is a nice benefit.

Enter AI. Support and Success teams are now designing their own tools to connect systems, to give them full 360 views of customers, to automatically tag conversations with useful data, and update documentation, to track minor actions that indicate potential churn. They are no longer touching simple questions like "how do I ..." and "this is broken". Now they are reviewing documentation with the question "Can AI use this in all the situations I would expect?" They are looking upstream and asking "Why?" more than ever before. AI was promised as the breakthrough technology that would finally reduce support volume and eliminate the need for entire teams. But it seems to be having the exact opposite effect. Customers are not stuck on finding the doodad in the UI. They figured that out on their own, or very soon the UI will just adjust to the user. Now customers are asking "What if?" Or "If A+B = C how about X+Y? Does it always equal Z, even if I throw in a W?" These are not questions you can set up the documentation to feed into an AI agent. It takes real creative thinking. It requires deep collaboration with Product, with Engineering. Support teams are more often collaborating with Marketing and Sales. Sometimes even with Revenue. They are going far beyond the silo of just answering some questions.

We talk a lot about signal, and now we're overloaded with signal. We suddenly have a tool where we can say "take a look at the last 6 months of support enquiries and give me 3 variations of the theme of [thing I want to know about]," and we can format the resulting data in language our colleagues can understand, translated from Support Speak.
The loop from Product - Customer - Feedback - Product is now almost instant. Just like Agile sped up Engineering teams with unprecedented data, AI is on the verge of speeding up the entire company. It touches every department from GTM to Tech to G&A.

We can keep talking about support volume and team size. Or we can step up to a more appropriate view of the entire machine. What would it look like to put the customer experience at the center of your decision tree? What if we acknowledged that we are building things for customers to purchase? That people purchase things because they want them, and that they keep purchasing them not just for the product but for the entire experience of using and owning the product?

I propose a radical shift in SaaS -- empower a new department (if you don't already have this one) of Customer Experience. The hierarchies are going to look different and that's ok. Silos aren't going to survive the rapid information flow that AI is giving us. They're too narrow, too restrictive, and too archaic to be honest. Your teams may become fluid and be initiative or project-based. They will be composed of people with all the special skills needed to meet the needs of the team – data, engineering, design, user intelligence, sales – using their skills together to build and iterate at a speed never imagined before.

The real power is going to be with the companies who know what their customers want before they ever reach out. Truly leveraging knowledge means eliminating friction. It means working together as a team that is not defined by the old functions, and instead by project, by goal, or by initiative. People aren't going anywhere. In fact, your company might need to hire more people. But the companies who can nail this -- true understanding of the customer -- are going to be the one who win.