Your marketing team is not creating. It is assembling.
The fortieth resize of the same creative. The weekly numbers, pulled by hand. The follow-up nobody sent because nobody was watching. The brief that waits seven days for a review. Below is what that work looks like today, what it looks like once Silow has mapped it, and what it gives back in hours, in money, and in people.
How the work looks today. How it looks after.
Seven processes, in the order we would take them. The left-hand column is not a caricature of a marketing team — it is what two recorded teams were observed doing. Where a row is tagged Measured, the number came out of that recorded work, not out of a benchmark.
One approved master becomes dozens of near-identical children by hand: every placement size, every locale, every channel cut, every A/B copy line. A designer’s week disappears into resizes and re-exports, and the fiftieth variant gets the same care as the first — which is to say, less.
The variant matrix is generated from the approved master and your brand rules. A human approves the batch instead of building each file. The steps your designer actually performs — which crop, which safe area, which copy slot — are read out of the recording and become the agent’s specification, so the output looks like your work, not like a template.
The funnel exists as a slide. In the recording it is a person noticing a lead, writing a follow-up from memory three days later, and an email sequence in the ESP that nobody has owned since the person who wrote it left. The messages that actually convert live in one rep’s head and in their sent folder.
The sequence is built from the language your best people already use at each stage — captured from real threads, not invented by a model. Triggers fire off real events instead of someone remembering. You sign the sequence off once; after that it runs, and every deviation lands as a draft for a human rather than as a surprise in a customer’s inbox.
The weekly ritual: pull from the ad platforms, pull from analytics, paste into the sheet, fix the sheet, rebuild the deck, notice the number that looks wrong, chase it. Fixed sources, fixed shape, fixed output — done by hand, every week, by someone senior enough to know what the numbers mean.
The pull, the assembly and the format run themselves. What arrives on the human’s desk is the part that needs judgement: what moved, what broke, what is worth explaining. This is the single most repeated ritual we see in a marketing team, and the definition of an agent spec.
One PDF was manually located in Finder and re-attached across two messengers on five separate occasions in three weeks. No shared link for it existed. Briefs start from an empty page rather than from the last one that worked, because nobody can find the last one that worked.
Assets have one retrievable place, and the brief starts pre-filled from the campaign that actually performed. The five-times-attached file is not an anecdote — it is a category, and it is the cheapest hour on this page to get back.
Twenty-four live threads were on screen. Not one had been entered into the CRM. One person spent 190 minutes over three weeks moving them across by hand — and elsewhere in the same capture, the team was building a browser extension to scrape their own leads into the CRM. Building, by hand, the thing the recording already filled in.
Conversations become rows a human confirms. We are deliberate about what this is: it fills an activity board, not a deal board — in the go-to-market team we recorded, roughly four rows were genuinely hard. We would rather tell you that now than after you have bought it.
A brief waits on a review. A review waits on an approval. Work that needs a second human waits a median of 7 days; work one person can finish alone waits 1.1. Two thirds of your calendar is on the far side of a handoff — and it is not time zones, because the least-overlapping pair still shares a quarter of the working day.
Every handoff gets a defined shape: what is attached, what "done" means, who is waiting and since when. The queue becomes visible, which is most of the fix. This is the largest single block of recoverable time on the page and the cheapest to change.
You report adoption from licence seats and app names. Measured that way the recorded team used AI 11.7% of the time. Measured from the actual work: 14.9%. One person was undercounted sevenfold, because his assistant runs inside his editor and the telemetry only sees the editor.
The share of real work that actually ran through a model, measured from the work itself. Not seats sold, not logins — the version a CFO can defend to a board when they ask what the AI budget bought.
The teams behind these rows: a 9-person go-to-market team, 5 weeks, ~325 hours of work recorded, and an 8-person cross-border operations team. Both anonymized. Rows tagged Measured carry a number computed from that real recorded work; rows that were not measured say so in their own tag. Every figure was computed before a model was allowed to write a sentence about it, then each sentence was checked back against the figure.
Time, money, and the people you already have.
The rule for this section: not one invented percentage. Every figure below is either measured in a recording or supplied by you — because a saving you cannot defend in a board meeting is not a saving, it is a slide.
We do not quote you an industry percentage. Every candidate above is scored in hours against your recording, then ranked by payback — so the first thing you build is the one that gives back fastest, not the one that demos best. The anchors we can already point at: 190 minutes over three weeks retyping threads into a CRM, by one person, on one ritual. A file re-found and re-attached five times. A median seven-day wait every time work crosses a desk.
The first is hours × your loaded cost — your rate, not a benchmark we made up. The second is the one nobody counts: the AI you already pay for and nobody opens. In the operations team we recorded — an operations team, not a marketing one — the company had built its own internal assistant and recommended it to the team. Recorded usage across eight people: seven at zero percent, one at one. That licence line is being paid every month right now.
No new system to adopt, no data to move, no process change while we look — Silow runs on the tools the team already uses. The capacity comes back inside the same team, doing the same job with the assembly work removed. What you then do with that capacity is a decision for the people running the business, taken with better information than they have ever had.
Two thirds of the elapsed time sat in a queue between two people. No vendor sells that, because it is not a tool category — and it is the largest number we found.
From forty ideas to a ranked map to the layer underneath.
The automations are the visible part. The thing that makes them possible — and that keeps paying after they ship — is the record of how your company actually works.
You have AI licences, a list of forty ideas, and no way to say which is worth doing. Adoption is reported from seats. The busywork is invisible because it is fifteen minutes, fifty times a week, across eight people.
The real work, reconstructed from the recording: where the hours go, where the queue is, what the team actually does all day — ranked by impact, effort, risk and payback against your own numbers.
The top of the ranking ships: the reporting assembly, the variant matrix, the handoff contract. Each one arrives as a specification drawn from observed steps, not from a workshop guess.
The automations are consumers of something more valuable: a private, structured record of how your company actually works. Chat that answers from it. Onboarding that teaches from it. Whatever you build next, on data only you own.
Where AI actually works in marketing — and where it stalls.
Everyone selling AI to marketing teams has a case study with a number in it. We are not going to quote you someone else’s, because we cannot verify it. Here are the patterns instead, including the failures.
The clearest win in the market, and the least glamorous. Teams that industrialise variant production — sizes, locales, cuts, hooks — get more shots on goal per cycle. Teams that ask a model to invent the campaign concept quietly stop after the second brand review.
Where it works: sequences whose content is derived from what already converts. Where it stalls: sequences generated from nothing, which read as generated and are unsubscribed from as generated. The asset is your team’s real language — most companies have never captured it.
The highest-frequency, lowest-judgement work in the function — and the one most teams believe they already solved with a brittle sheet. It looks solved. It is a senior person’s recurring half-day.
Briefs, outlines, refreshes and the long tail of updates. Pays back where there is an existing library to start from, and produces landfill where there is not. The differentiator is retrieval, not generation.
Everyone has a CRM. Almost nobody has a CRM that reflects what was actually said in the messenger. The gap between the two is where pipeline quietly dies — and it is a data problem long before it is a model problem.
One asset into six markets and four channels. Boring, mechanical, high-volume — which is exactly why it is one of the few places where the payback shows up in the same quarter you started.
Pilots do not die because the models are bad. They die because nobody could say which of forty candidate processes to do first, or what "correct" looked like when it was done. That ranking is the thing Silow produces; the automations are what you build on top of it.
Nobody automates the waiting. It is not a tool category, so no vendor sells it — yet two thirds of the elapsed time in the go-to-market team we recorded sat in a queue between two people. It is the largest number on this page and the cheapest one to move.
The part other vendors leave out.
Two of the hypotheses we most wanted to be true were killed by our own data. You should hear that from us here rather than discover it in month three.
The recording sees activity, never outcome. Whether the campaign worked, whether the deal closed — that is not in the data. Join one column of your outcomes to our trace and it becomes provable. Until then, anyone quoting you an ROI number is guessing.
The pitch was "N people doing the same thing, here is the saving." The real answer across nine people over five weeks was 44 minutes — 0.22% of working time. The median duplicate was fifteen seconds. There is no product there, so we do not sell one.
Three quarters of what our naive metric called "meeting time" had no room attached — one person’s 666 "meeting minutes" were a parked video-call landing page. That is a capture problem, not a metric problem, and we say so instead of shipping the chart.
Numbers are computed before the model is allowed to write a sentence about them, so no figure gets invented. But a true number can still be attached to the wrong claim. We have caught exactly that twice — both times with eyes, not with the gate.
Weeks, not quarters.
Silow runs on the team’s existing machines and tools. No migration, no new system to adopt, no process change while we look. You choose the deployment mode — including fully air-gapped.
Scenes become tasks, tasks become work directions. Numbers are computed before a model writes a sentence about them, and every quote is checked back against the raw scene it came from.
Every candidate scored by impact, effort, risk and payback against your real hours — so the first thing you build is the one that pays back fastest, not the one that demos best.
Each opportunity arrives as a specification an engineer can build from: the procedure, the evidence, the scenes it came from. Not "this looks automatable" — the actual steps, observed.
What you do with the hours is yours.
Silow maps work, not workers. No productivity ranking, no performance evaluation, no automated decision about anyone — not as a policy written afterwards, but because the unit of analysis is the process. Two weeks of recording, and the first ranked roadmap lands with the hours attached.