Situation
All inbound work arrived as unstructured email and text. One person was the queue, no priority, no SLA, no audit trail, and no way to scale without adding headcount.
All case studies
B2B ServicesAgent as a ServiceAs-A-Service engagement
A High-Growth SMB Replaced Its Manual Intake Queue With a Governed Agent
Anonymized, High-Growth SMB (B2B services) · named when customer permission clears
Quality
0→1
Governed intake agent in production
Time
Same week
Response SLA established (was: none)
Cost
0
Added headcount to scale intake
The arc
Situation, work, outcome.
Work
Foundation before automation: an AI usage policy, data classification, and a small set of reusable skills came first. Only then did a single-purpose intake agent go live on top of the validated foundation, with a visible task log the team could audit.
Outcome
Intake became structured and measurable. Requests are captured, prioritized, and routed by the agent; the team reviews the reasoning trace, not the raw inbox. Capacity that was lost to triage came back, without a new hire.
The operating-model arc
What discovery surfaced, what we built, what the QBR recalibrated.
Every engagement runs the same three-phase shape, foundation before automation, measured every cycle.
Phase 1, Discover
Weeks 1–4- Interviewed the function; mapped the intake value stream end-to-end.
- Scored the gap on the Opportunity Index, high frequency, high impact, low effort.
- Classified the data the agent would touch; published the one-page AI usage policy.
Phase 2, Build
Weeks 5–10- Stood up the shared knowledge base + reusable skills (the foundation).
- Deployed the Lean intake agent with a visible task log.
- Wired it into the existing CRM, no rip-and-replace.
Phase 3, Improve
Ongoing- Monthly report on agent throughput, reliability, and value-impact captured.
- First QBR recalibrated the next-highest-ranked fix from the register.
“We didn't buy a tool. We got an operating model, and one partner who handled the process, the data, the integration, and the agent.”
Operations lead, anonymized client
Case study, FAQ
Questions about this engagement.
Published as FAQPage schema for AI Overview + People Also Ask citation.
Why build the foundation before deploying the agent?
Because AI accelerates whatever already exists, deploy an agent on a broken, ungoverned process and you scale the mess. The foundation (policy, data classification, reusable skills, shared knowledge base) is what makes the agent safe, auditable, and able to compound.
What makes this different from buying an AI tool?
A tool is an island, its own data, its own prompts, no governance, no audit trail. This was one vendor delivering operations + technology + data + software together: the process redesign, the data foundation, the integration, and the governed agent on top, measured against business outcomes.
Want yours on the list?
Start with a measurement.
The value-impact OKRs we set together at kickoff become the case study when the engagement closes. One vendor, one roadmap, measured every quarter.