There's a race happening right now in almost every service industry. On one side the incumbents have deep domain expertise, years of hard-won process knowledge, and a growing fear that they're about to be automated out of existence. On the other side, well-funded AI-native upstarts are coming in hot, confident that the incumbents' complexity is just inefficiency in disguise.

Both sides are half right.

The incumbents are right that domain expertise matters. What looks like inefficiency from the outside is often decades of edge cases, regulatory nuance, and exception handling that nobody wrote down but that turns out to be essential to delivering a consistent, high-quality outcome.

The AI natives are right that a lot of service work can be dramatically automated. Entrenched processes are often decades old, developed organically without careful oversight, and rely on obsolete toolsets for purely historical reasons.

But automation is not just technology, it’s the application of technology to a process, and successful processes arise only through iteration. Everyone has to tame the edge cases to produce valuable customer outcomes; there is no shortcut, no silver bullet. All else equal, it's much easier to automate an existing process than a hypothetical one, because you're starting with the lights on. Try to automate prospectively, and you'll keep tripping over stuff you didn't know was lurking in the dark - the same edge cases and exceptions that make the service valuable and hard to do well in the first place.

Winning in this environment means blending technology with human expertise, and both the incumbents and the AI natives have a real path to get there. But the structural advantage sits with organizations that already have both a working process and the subject matter experts (SMEs) who hold that process in their heads.

The AI natives have to build three things at once: the automation, a delivery organization that can actually perform the service, and a book of business to perform it for. They talk about automation as the hard part because it's the part they're good at, but it's also the part that's commoditizing. The parts that aren't commoditizing - a team of experts that can deliver and clients who trust them - are the parts an incumbent has already paid for.

If you're an incumbent, the opportunity in front of you is real. The question is how to get there first, and the answer starts with a clear-eyed understanding of the work ahead.

One Step Back, Two Steps Forward

Most people expect automation to feel like acceleration, as though someone stepped on the gas. Same process, running faster, with machines handling the repetitive parts so the humans can focus on the interesting work. Clean, additive, not especially disruptive.

That is not what happens.

The core activity of automation is deconstruction, because machines don't meet you halfway. Think about a chef cooking the same dish for 15 years. They don’t measure anything, they just know when it’s right, by feel, by smell, by the sound of the pan. Writing down that recipe for someone else (or something else) to follow means breaking the cooking apart into steps the chef never consciously tracked. Exact temperatures, exact timings, exact ratios; even exact movements. What to do when the meat sticks or the vegetables start to char. The end result may be the same, but the experience of creating and cooking from the recipe is vastly different than the original instinctual experience.

That’s what automation does to a process. A machine needs the work to be small, clean, and well-defined. LLMs widen the field of opportunity, but they still benefit heavily from small, circumscribed units of work. Human processes are optimized for humans, and they need significant adjustment before they can be refit for machine labor. High-quality automation means taking the process apart and rebuilding it, piece by piece, while it's running, so that the work becomes machine-accessible and machine-achievable.

This means that for a stretch of time, your SMEs aren’t simply making the call anymore. They're recording it, categorizing it, explaining each decision in structured terms a model can learn from. They're labeling data, or at least providing examples, tuning a prompt, reviewing errors, defining guardrails. The judgment that took two minutes now takes five, plus documentation. The work that felt like an expert flow state starts to feel like a hassle, and on a bad day it feels like data entry.

When the automation isn't deployed yet, the throughput has dropped, and your SMEs are telling you this isn't working, the whole thing looks like it might be a mistake. If you don't know that this is what automation looks like from the inside the temptation is to reverse course, but this is precisely the wrong moment to retreat.

Hold the line, and the work pays off. The expertise that lived in one person's head is now encoded in the model, in the decision logic, in the labeled dataset that gets sharper with every run. The judgment that took five minutes and a page of documentation takes the model a second, and your expert is no longer the bottleneck. Freed from the mechanical execution, they bring their full attention to the work that actually needs a human: the strange case, the unhappy client, the decision that shouldn't be automated yet. The business gets smarter in a way that compounds. Each cycle is faster than the last, because the process is better understood, the tooling is more mature, and your experts know what good output looks like and can spot bad output on sight.

Do that a few dozen times, and the end state looks nothing like where you started. The process is optimized for machines now, a series of steps that are smaller, more consistent, and often counterintuitive, the kind of thing you couldn't have drawn on a whiteboard at the beginning. It only reveals itself through iteration, which is exactly why it's so hard to copy. A competitor can see your service from the outside, but they can't see the hundred decisions that shaped it, and they can't skip the work that taught you to make them.

The Human Element

Plenty of your experts will take to this. They're the first to see where a task is ready to be handed off, and some will be genuinely energized by the chance to build something new. But this transition asks something real of people. For a stretch they experience a worse version of the job, in service of something they can't yet see. What they can see is the thing they think of as their valuable skill - the actual doing of the work - starting to look obsolete.

While the discomfort is difficult, the grind is temporary. Once the model is trained and validated, it takes over the work the experts used to do by hand. They move to reviewing the edge cases the model flags, then to auditing a sample, then eventually to something else entirely. The process normalizes. Before long nobody thinks about that phase at all, because it just runs in the background.

The experts never get the original process back. They get elevated past it, and the job changes for good, ideally toward applying their real skill set with more leverage and creativity than they had before.

The permanent change can unsettle people too, and reasonably so. The SMEs bear the brunt of the uncertainty, and it doesn't reliably break in their favor. Cutting costs and cutting jobs is a loud theme in the discourse, even though the companies getting this right are using automation to unleash their experts rather than discard them. When you ask people to trust an opaque process without offering a compelling picture of their future, many will brace for the worst.

All these reactions are common, and they're also yours to manage. Handling them poorly or not at all is how you lose people. Handled well, the disruption becomes the thing that bonds the team together.

The SME Playbook

Managing through the disruption comes down to a few things, done consistently.

Protect your top SMEs. The most valuable people in this transition pair deep expertise with tolerance for the process changing under them. These are the people to build around. They’re exactly who the AI natives are also trying to identify, so they’re the hardest to keep and the costliest to lose. Protecting them is about more than compensation. It's making sure the people absorbing the bulk of the changes are recognized for their effort, informed about where it's going, and rewarded when it pays off.

Bring them along. The automation process depends heavily on their input, which makes it dangerously easy to treat them as a knowledge source to be extracted: pull out what they know, feed it to the model, move on. That’s a short-term strategy; you'll get the data but hollow out the person and ultimately lose them. The alternative is to make them genuine partners in the change. Invest in them, give them opportunities to learn how to work with engineers, expose them to how the automation actually gets built, allow them a real hand in shaping it. They don’t need to become engineers, but by giving them some agency in the process you’ll develop a workforce that treats change as normal instead of a threat.

Anchor to the outcome, not the tasks. Experts often identify with the work itself - specific tasks, manual review, the particular way they've always done things - and those tasks are exactly what's going to change or disappear. Help them re-anchor to the things that don’t disappear: the client's outcome, and the expertise that produces it. Delivery is the ongoing work of getting the client a result they trust, and it outlives any individual task inside it. The SMEs’ judgment, their relationships, their ability to make a client feel confident and taken care of, none of that gets automated away. It gets more leverage, and as automation frees their time the attention each client receives should deepen, not thin out.

Be transparent, even about ambiguity. You're asking your people to trust a process whose end state can’t be fully described, and to spend months making their own jobs feel worse in service of something they can't see. Nobody can honestly promise them what their role looks like in two years. What you can do is be clear about what the transition involves, honest about why it matters, and explicit about what you value in the people who come through it. Trust is earned through actions, so commit to them out loud and follow through.

The Running Start

The AI natives are right that the work can be automated, and the best of them will get there. Your advantage isn't that they'll necessarily fail, it's that you're starting closer to the finish.

Closer still isn't easy: you need engaged experts, and beyond them a capable technical org, clear metrics, committed leadership, and a fair amount of institutional reorganization. You're going to be making hard calls, and you're doing it while running the business and fending off competitors who move fast and carry none of your history.

But the incumbents who get this right won't merely survive the transition. They'll come out of it holding something the AI natives can't easily build: a proven process, a roster of experts who chose to stay, and a service that gets better and cheaper at the same time.

This race isn't won with the fanciest technology or the most expensive dev team. It’s won with the durable, compounding advantage that comes from weaving together tech, process, and people.