Seer Interactive built an AI lab for its own teams. Then clients started asking whether they could buy it.
The lab became SeerAdvance, a productized consulting offering, and it now sits alongside seven other rebuilt agency models in a Marketing AI Institute piece published ahead of MAICON 2026. All eight are conference sponsors, and the Institute discloses that up front, which is the right call. Read as a set, the profiles describe an industry splitting in two. One side bolts AI onto the old service menu and watches margins compress. The other rebuilds what it sells, who does the work, and where the profit actually comes from.
If you run any kind of services business, the second group deserves a slow read.
Why AI-assisted services end badly
The default agency response to AI so far goes like this: keep the service menu identical, deliver everything faster, charge the same or slightly less. It feels like a win for a quarter or two. Then clients notice the speed, start questioning the retainer, and the work becomes commodity work. Commodity work goes to whoever bids lowest.
The economics underneath are unforgiving. When a task that used to take ten hours takes two, an agency paid by the hour has just cut its own revenue by eighty percent on that task. Every efficiency gain lands as a pay cut unless the pricing model changes with it.
Every agency in the Institute’s piece started at that dead end and moved past it. The interesting part is where they moved to, because no two landed in the same place.
Eight models, six rough shapes
The embedded co-builder: Algomarketing
Algomarketing puts senior practitioners inside client teams to build AI-native agents alongside them, then coaches the in-house staff until they can run everything without outside help. The agency is selling the transfer itself. Done honestly, the engagement ends with the client needing you less, and the relationship survives anyway because they trust you with the next hard problem.
AI-native delivery: Intercept
Intercept runs proprietary tools across research, content production, personalization, and campaign execution. Strategy and creative direction stay with people, and a skunkworks called Intercept Labs tests whatever comes next. Humans decide, machines produce. The delivery cost drops every quarter as the tools improve, which is exactly the wrong business to be in if you bill by the hour.
The lab turned product: Seer Interactive
This is the Seer pattern again, and it’s the one I would steal first. Build the lab for yourself. Learn in public, document what works, then package that hard-won internal knowledge as something clients can buy. SeerAdvance exists because Seer ate its own cooking long enough to write the recipe down.
There is a sequencing lesson buried in it. Most agencies try to sell AI consulting before they have internal scars. Seer spent years collecting the scars first, and the consulting product is just the scar tissue with a price tag.
Owning AI search: Soarion Digital
Soarion Digital builds citation-driven content and lines up third-party validation so that when someone asks an AI engine a question in a client’s category, the client is the answer it gives. That is a genuinely new service line. It did not exist before chatbots started answering purchase questions, which means nobody has legacy pricing expectations for it yet. New service categories are where healthy margins hide.
Orchestration: Level Agency
Level Agency frames its approach as “AI orchestration”: repeatable systems that connect strategy, execution, and data across long, complicated buying journeys. The pitch underneath the label is repeatability. Once you have built the system for one client, the second deployment costs a fraction of the first, and the tenth is nearly pure margin.
Governance and fractional operators: Demand Spring and EMMIE Collective
Two quieter entries round out the picture. Demand Spring helps marketing teams move past the experimentation stage with governance and operationalized workflows. EMMIE Collective supplies fractional MarketingOps and RevOps people embedded directly in client teams. Both sell capability where most vendors still sell deliverables. That distinction is doing a lot of work across this entire piece.
The copyable playbook
Strip away the branding and the Seer pattern is a sequence any services business can run:
- Pick one internal workflow you repeat weekly. Research sprints, content briefs, campaign reporting, anything with a repeatable shape.
- Build the AI-assisted version for yourself first. Track the hours saved, because that number becomes your pricing evidence later.
- Document the system until someone else could run it from your notes alone.
- Give it a name and a fixed price. Sell the system, and keep the hours it saves as your margin instead of your invoice.
The quiet point in that math: a deliverable you build once and deploy repeatedly is software economics wearing an agency badge. Hours do not behave that way. Hours reset to zero every Monday.
Run it against your own stack
The Institute wrote about marketing agencies, but the split applies to freelancers, consultants, and in-house teams too. Look at your last month of work and ask which tasks you repeated often enough to systematize. Then ask which of those systems could carry a product name and a fixed price. The parts left over, the ones that genuinely need you in the room, are your actual job now. Everything else is a candidate for the lab.
For a content operation, the honest answers usually look like this: research and formatting systematize well, voice and judgment do not. That split, named and priced, is a business model. Left unnamed, it is a slow margin leak.
The Marketing AI Institute piece rewards a full read if only to see how differently eight companies answered identical pressure. And if you want to argue about it in person, MAICON 2026 runs October 13 to 15 in Cleveland.
