The 90-day case studies are useful, but they don’t show what compounds. Here’s the four-quarter version — with the work, the costs, and the outcomes by stage.
The composite
The picture below is composited from six of our AI Office clients who hit roughly the same shape of year-one engagement. The company is a $25M regional services firm, $4M EBITDA, 90 employees. It started at the AI Office Operator tier ($5,000/mo) and expanded the portfolio over the year. This is what its twelve months looked like.
Quarter 1: Foundation and first win
What got built:
- A 4-page AI roadmap with 9 scored candidates, sequenced into three waves
- Workflow #1 in production by week 7: AI-augmented quoting
- A documented baseline and 30-day results
Workflow #1 details:
- Pain: 5-day quote turnaround, losing bids to competitors who quoted in 2 days
- Build: AI parses inbound RFQs, pulls the 5 most similar past jobs, drafts the quote with margin guidance, and routes to a human approver
- Investment: $40K Value Sprint, 5 weeks
- Day-90 result: quote turnaround down to 22 hours; win rate up 6 points (early); an estimated $120K of annualized run-rate value
Time investment:
- Senior strategist (Frogslayer): 10 hours/month
- Build engineer (Frogslayer): heavy in weeks 3–7, lighter after
- Client side: COO at ~12 hours/month, senior estimator ~8 hours/month, sales lead ~4 hours/month
Quarter 1 spend:
- AI Office Operator: $15K (3 months)
- Workflow #1 Sprint: $40K
- Tooling: $4K
- Total: $59K
Where the client is at end of Q1:
- One AI workflow in production with documented ROI
- A clear roadmap for the next 9 months
- An internal team starting to believe AI is a real capability, not a buzzword
- The senior estimator who was the biggest blocker is now the strongest champion
Quarter 2: Second workflow, deeper integration
What got built:
- Workflow #2 in production by week 18: field-to-office reporting
- Workflow #1 refined based on a 90-day quality review
- Roadmap revisited; Wave 2 sequencing adjusted
Workflow #2 details:
- Pain: crews submitting partial photos and notes; the office spending days reconstructing them for invoicing; invoicing lagging 2–3 weeks
- Build: crews submit photos and voice notes from the field; AI structures them into the ERP and drafts invoice line items; the office reviews and posts
- Investment: $55K Value Sprint, 6 weeks
- Day-90 result: invoice lag down from 3 weeks to 4 days; ~1 FTE-equivalent of office time recovered; an estimated $90K of annualized value
Quarter 2 spend:
- AI Office Operator: $15K
- Workflow #2 Sprint: $55K
- Total: $70K
Cumulative through Q2: $129K. Cumulative annual run-rate value: ~$210K.
Where the client is at end of Q2:
- Two workflows in production
- The team talking about AI as part of how the business runs, not as a project
- One internal “AI champion” emerged organically — an ops manager who started suggesting candidates from her own observations
Quarter 3: Tier upgrade and deeper work
What changed:
- Moved from Operator to the Embedded tier at month 7 ($10,000/mo)
- Started workflow #3: AI-augmented customer service for Tier 1 inquiries
- Started a smaller workflow #4: AI-drafted weekly leadership briefings from operational data
Why the tier upgrade:
The build queue had grown to a level Operator capacity couldn’t sustain. The COO and our team had a conversation about it, and the math worked. The client wasn’t paying for “more meetings” — they were paying for engineering throughput to match their rate of workflow discovery.
Quarter 3 spend:
- AI Office Embedded (3 months at $10K): $30K
- Workflow #3 Sprint: $60K
- Workflow #4 Sprint (smaller): $18K
- Total: $108K
Cumulative through Q3: $237K. Cumulative annual run-rate value: ~$400K.
End-of-Q3 status of each workflow:
- Workflow #1 (quoting): mature; quality stable; the senior estimator retired in month 8 with most of her knowledge captured
- Workflow #2 (field-to-office): in production; one refinement underway
- Workflow #3 (customer service): in late-stage build; soft-launching in week 38
- Workflow #4 (leadership briefings): in production; the CEO is the primary user
Quarter 4: Compounding and the start of a multi-quarter program
What got built:
- Workflow #3 in production by week 41; handling 60% of Tier 1 inquiries by end of quarter
- A multi-quarter program kicked off in week 44: a deeper integration with the dispatch system that will span 4–5 Sprints over 6 months ($165K total)
- An internal “AI Operations Lead” hired in week 46 — promoted from operations, $105K salary
Quarter 4 spend:
- AI Office Embedded: $30K
- Multi-quarter program kickoff (first $40K of $165K): $40K
- Total: $70K
Cumulative through Q4: $307K. Cumulative annual run-rate value: ~$680K.
The “annual run-rate” number is value the workflows would produce in a full year if extrapolated from current performance. Year 1 captured value is lower — roughly $300–400K — because the workflows didn’t all run for a full year.
The year-one summary
- Year 1 cash spend with Frogslayer: $307K
- Year 1 captured value: ~$350K
- Year 1 annualized run-rate value as of month 12: ~$680K
- Year 2 projected captured value: ~$1.0M (assuming workflows continue and the Q4 program lands)
- ROI ratio year 1: ~1.1X captured / ~2.2X run-rate
- Projected ROI ratio year 2 cumulative: ~3X
These numbers are illustrative — this is a composite, not a single client’s books — but they reflect the honest shape of the work. Year 1 of a serious AI Office engagement isn’t a “5X ROI” story — it’s a foundation-building story where ROI catches up and overshoots in years 2 and 3. The AI Office retainer targets paying for itself (≥3X over time) rather than guaranteeing it; here, the engagement cleared that bar on the run-rate measure within 12 months and on captured value the following year.
What the team has at end of year 1:
- 4 workflows in production with documented ROI
- An AI roadmap of the next 8 candidates, scored and sequenced
- An internal AI Operations Lead (new hire)
- A senior estimator’s retirement that didn’t cost the business her knowledge
- A leadership team that operates with AI as a baseline assumption
- A multi-quarter program in flight that will reshape operations across year 2
What this looks like for businesses smaller and larger
Smaller — a $10M services business, Sherpa tier ($2,500/mo):
- 2 workflows in production by end of year 1
- ~$80K cash spend
- ~$200–300K of annual run-rate value
- Similar ratio, smaller absolute numbers
Larger — a $60M services business, Embedded tier from the start:
- 6–8 workflows in production by end of year 1
- ~$500K–$700K cash spend
- ~$1.5–2.5M of annual run-rate value
- Similar ratio, larger absolute numbers
The ratios are roughly constant across the size range. What varies is the absolute number of workflows and the absolute dollar value.
What we’d want you to take from this
- Year 1 is foundation-building. If you’re looking for a 5X ROI in year 1, you’ll be disappointed. The 5X comes in years 2–3 as workflows accumulate.
- The cadence matters more than the cleverness. What worked here wasn’t one brilliant build. It was four solid builds, on a quarterly rhythm, with measurement discipline.
- The team that emerges matters more than the workflows. By the end of year 1, the client has a hired AI Operations Lead, a senior champion, and a leadership team that thinks in AI terms. That capability is more valuable than any single workflow.
Want to see what year one would look like for your business? Book a 30-minute intro.