Most AI budgets you’ll see are theoretical or vendor-flavored. Here’s the actual money math for a $20M B2B service business in our priority industries, based on what we’ve seen work across hundreds of projects.
The starting assumptions
A typical $20M B2B service business in our priority industries looks like this:
- 75–150 employees
- $2–4M EBITDA
- One owner/CEO, one COO or operations director, one CFO or finance leader
- One or two senior people whose judgment is irreplaceable (an estimator, a lead engineer, a head of compliance)
- A small marketing/sales team (3–6 people)
- Field, service, or delivery teams that scale linearly with revenue
For this profile, a realistic AI Year 1 budget breaks into three components.
Component 1: The partner ($30K–$120K/year)
Not optional. If you’re trying to build AI capability without a senior outside partner, your timeline is 18 months instead of 12, and the failure rate is 3–4x higher. That’s not a scare stat — according to RAND, more than 80% of AI projects fail, twice the rate of IT projects that don’t involve AI. The thing that moves that number is senior execution, not more software.
- AI Sherpa ($7,500 per 3-month cycle): lightest touch. A guided program that gets your leadership team fluent and scopes the first automation. No engineering — that starts with AI Office.
- Operator ($60K/year): dedicated engineering capacity on a weekly cadence. One small automation a month: a report, an alert, a data sync, an intake step, a follow-up, a simple integration. This is the most common starting tier.
- Embedded ($120K/year): twice the pace. Two or three small automations a month and a 6–12 month AI and automation roadmap. Right for PE-backed operators or multi-entity buyers.
For a $20M service business, plan on ~$60K for the retainer in year 1 unless you have a specific reason to go bigger.
Component 2: Projects that outgrow the retainer (expect zero to two in year 1)
The retainer ships small, task-level automations. Some things are bigger than that: a dashboard that pulls several systems together, a tool your whole team works in, an assistant wired deep into your systems. Those flow out of the AI Office relationship, get scoped after a Deep Dive, and are priced as projects from the ranges on the pricing page:
- A dashboard or report from data you already have: $15K–$95K, four to ten weeks.
- A small tool or app that does one job well: $20K–$75K, four to eight weeks.
- An AI assistant or automation inside your current systems: $12K–$150K, six to twelve weeks.
Many clients do none of these in year 1 and let the retainer carry the load. Budget for one if you already know what it is.
Each project is tied to one agreed business metric, baselined before the work starts and reported against after it ships.
Component 3: Tools, infrastructure, internal time ($15K–$40K)
The smaller bucket, but still real:
- Frontier model API costs: ~$200–$2,000/month at production scale for a typical mid-market workflow set. As workflows scale this can grow, but it stays modest.
- AI tool licenses your team uses individually (Claude for Work, ChatGPT Enterprise, Copilot, and the like): $50–$500/user/month depending on usage. For a 100-person company with 30 active AI users, ~$50K/year is in range.
- Infrastructure (hosting, monitoring, deployment): typically $5K–$20K/year for early-stage deployments. Larger as you scale.
- Internal team time: 10–20% of one director’s time plus 5–10% of several operational owners. This is real cost — call it $30K–$60K of loaded labor allocated to AI work in year 1.
Putting it together
A defensible Year 1 budget for a $20M service business:
| Component | Year 1 spend |
|---|---|
| AI Office retainer (Operator tier) | $60,000 |
| Projects outside the retainer (zero to two) | $0–$150,000 |
| Tools + licenses + infrastructure | $20,000–$60,000 |
| Internal team time (loaded labor) | $30,000–$60,000 |
| Total Year 1 | $110,000–$330,000 |
Cash out the door, excluding internal labor allocation: $80,000–$270,000.
The ROI math that justifies it
For this profile of business, realistic year-1 outcomes from a well-run AI program:
- Quoting acceleration: an 8–15 point win-rate improvement on $5M of inbound bid volume = $400K–$750K in additional bookings.
- Field/office time recovered: one FTE-equivalent saved at ~$80K loaded cost.
- Senior estimator/expert leverage: 5–10 hours/week recovered for the senior person. At a ~$150K loaded rate, that’s $40K–$80K of expert capacity recovered.
- Customer service automation: one FTE-equivalent of routine call handling at ~$60K loaded.
- Indirect: faster decisions, better visibility, faster M&A integration capability, retention improvement.
A well-run program is designed to pay for itself inside year one — measured against the KPI agreed for each automation or project, not asserted.
When the budget doesn’t work
Two situations where this math doesn’t apply:
You’re significantly under $20M revenue. The retainer pricing is the same, but the relative scale of the investment is bigger. Smaller businesses should start lighter — the free and low-cost on-ramps, or AI Sherpa ($7,500 per 3-month cycle) — to prove the partnership before scaling up.
You’re significantly over $20M revenue. The retainer scales — you’d typically be at Operator or Embedded. The build volume scales too: year 1 might be 4–6 larger builds, not 2–3. The total budget can run $400K–$800K, still a fraction of what a national consultancy or internal team would cost.
Want this run against your numbers?
If you want a budget walked through your specific business — your size, your industry, your current AI maturity — that’s a 30-minute conversation. Book a 30-minute intro, or take the AI Readiness Assessment first.