Advisory · Automation advisory
AdvisoryAutomation advisory
An advisory that walks your workflows, flags where work repeats, where ownership breaks, and where AI may help — then ranks what should stay human, what can be automated safely, and what to implement later in Solutions. You leave with a written plan, not a tool pitch.
Reply within one business day with scope and an access checklist. Read-only access, no credentials pasted in email.
Ranked next steps
Fix firstName one owner for every handoff before anything gets wired
Automation encodes confusion when ownership drifts. This is the only "before" item.
AutomateAuto-route inbound by source and assign on first touch
Highest repeat volume, lowest judgment — the cleanest first automation on the list.
AI assistAI-drafted proposal + email, reviewed before send
Save roughly an hour per deal. Reviewer stays on the hook for what goes out.
Why teams ask for this advisory first
Service businesses, ops and finance leaders, and teams staring at a growing stack of tools nobody maintains. We bring a written recommendation, not a vendor.
Every workflow step gets one of four calls: keep human, automate, AI-assist, or fix first. One named first step so nobody argues where to start.
Reply within one business day with scope and access checklist. Plan lands in time to shape the next build decision, not after you have already paid for it.
What you get
One plan that walks your workflows, classifies every step, and ranks what to do next. Written for an operator to forward to ops, finance, or IT without a meeting to explain it.
What the advisory includes
We walk representative workflows end-to-end: where work starts, who touches it, where it stalls, where it repeats, and where it breaks between systems.
What is happening enough times per week that the cost is real. Not "things that feel tedious" — specific steps, specific volume, specific minutes lost per run.
Who owns each step today, where responsibility changes hands, and where "someone else is doing this" turns into nobody doing it.
Approvals, judgment calls, exceptions, client-facing decisions, anything with legal or contract weight. We name what should never leave a person.
Routing, reminders, field updates, cross-system sync, logging, status changes, follow-up triggers. Repeatable scaffolding around human work.
Automation advisory · 01 – 05
Each step ends with something you can use: a confirmed scope, a traced workflow, a classified plan, a ranked order, and a clear call on what to do next.
Send
A short written description of the workflows in scope, who owns which step today, the systems involved, and a few examples of where work stalls or repeats. One-page access checklist after scope is set.
Trace
Who starts, where it stalls, where it repeats, where it crosses teams or systems. We note where ownership drifts, where the same copy-paste keeps showing up, and where a rule quietly lives in somebody’s inbox.
Classify
Each step gets a call. We name what should stay with a person, what can be automated safely, where AI can assist with a reviewer, and what needs ownership or definitions cleaned up before anyone wires anything.
Rank
Ranked next steps in the order they should happen, with the reasoning a skeptical stakeholder can argue with. One named first step so your team knows where to start this week, not this quarter.
The four calls
Every step in your workflow gets one of four calls. This is what the plan separates, and why we would rather leave something manual than automate the wrong thing.
Anywhere a rule cannot safely capture the context — or the cost of getting it wrong is high — a person stays in the loop. Automation handles the scaffolding around the call, not the call itself.
Typical examples
We would rather leave this manual than cheapen the decision.
Routing, reminders, field updates, status changes, cross-system sync, logging, follow-up triggers. High volume, low judgment, measurable cost per run.
Typical examples
When to request this advisory
Pick this if a few of these sound familiar. Pick something else if the list on the right fits better.
✓Right first move if…
→Something else fits better if…
Proof
Each write-up starts with what the advisory clarified — ownership, repeat work, AI fit, sequencing — and then names what got built, and what moved afterward. Advisory proof first, implementation outcomes second.
Service Businesses
How MCC scaled its site, social presence, and enrollment machinery with managed Google and Meta ads before ownership changed hands.
Advisory questions
What this advisory includes, how it differs from implementation, and what comes back at the end.
The low-risk next step
Request the advisory. Get the written plan with what stays human, what gets automated, where AI helps, and what to fix first. Then decide whether a broader advisory or Solutions implementation is the right next step — with the same ranked list, not another discovery.
Drafting, summarizing, classifying, triaging, pulling notes together. Always with a named reviewer on anything that leaves the building.
Written order with sequencing: what to wire first, what to wait on, what to leave manual a while longer, and why.
What must be true before each piece is built — owner named, definition locked, system access sorted — so whoever implements does not inherit the mess.
Decide
The plan stands on its own. Run the first items internally, hand them to another vendor, or route to Solutions if implementation needs a build team. No second round of research either way.
The work is already happening. Automation just stops people from doing it.
AI is useful where it saves minutes on the first draft and a person stays accountable for what goes out. We do not recommend AI as the final voice on anything client-facing or contractual.
Typical examples
Every AI recommendation names the reviewer before it ships.
Anywhere ownership is unclear, definitions drift between teams, or the underlying data is unreliable. Wiring this now just makes the mess move faster. The plan names what has to be true before anything gets built.
Typical examples
Clean up the process first. Automation second.
Local Services
$50k+
How KAAR paired a redesigned site and drone creative with Google, Meta, and Yelp, then stood up Tekmetric and training for cleaner pipeline visibility.
Read case study
Local Services
~4x
How Glacier Grand Cleaners used paid local leads and site work to reach roughly 4x ROAS on attributed spend.
Read case study