AI-Ready Process Assessment: Summit Ridge Supply
An illustrative sample assessment I wrote to show the method. The company, people and data are fictional. The live Summit Ridge Control Tower shows the same process as a dashboard.
Summary
Fix pricing data and the credit policy first, then let AI draft orders from emailed purchase orders with a person approving each one. Together these free about 380 hours a month of order-desk time (about 2.4 people's capacity) and bring invoicing forward two days.
- Today: 1,800 purchase orders a month are keyed by hand; 532 of the order desk's 640 monthly hours go on order-to-invoice; 4% of invoices need correcting.
- What AI does: reads purchase-order PDFs and drafts the order. Pricing checks need simple rules, not AI. Credit holds need a policy change, not AI.
- Capacity is not cash: the hours freed become savings only if Summit Ridge absorbs growth without hiring, or redeploys staff. The cash that is real now is about $160,000 of working capital released once by faster invoicing.
Baseline
Order-to-invoice takes 532 of the order desk's 640 monthly hours (four representatives), and manual keying alone is two-thirds of that. Figures come from two weeks of timed samples and the order system's records.
| Step | Volume per month | Minutes each | Hours per month |
|---|---|---|---|
| Key purchase order into order system | 1,800 orders | 12 | 360 |
| Check prices against customer contracts | 1,800 orders | 3 | 90 |
| Review credit holds | 324 orders (18%) | 6 | 32 |
| Correct wrong invoices (credit memos) | 72 invoices (4%) | 25 | 30 |
| Prepare weekly invoice batch | 1,800 invoices | 0.7 | 20 |
| Total | 532 |
Time and money: an order takes 1.2 days on average from email to confirmation, and a credit hold adds 1.5 days. Invoices go out in a Friday batch, on average 2 days after shipment. Monthly revenue is $2.4 million.
Root causes: fix before automating
Three of the four big costs come from missing data and policy, not from a lack of AI.
- Every customer sends a different purchase-order layout. About 140 active customers email PDFs in their own formats, so every order is retyped. This is the one problem AI is suited to.
- Contract prices live in spreadsheets, not the order system. Representatives look up each order's prices by hand, and most invoice errors (about 80% of credit memos) are wrong prices. Fix: load contract price lists into the order system. Rules then check every line automatically.
- The credit policy holds any order over the limit, even by $1. Most of the 324 monthly holds are released unchanged by the credit manager a day and a half later. Fix: auto-release small overruns for customers with nothing overdue more than 30 days.
- Invoicing waits for Friday. Nothing requires the batch; it is habit. Fix: invoice on shipment, every day.
To-be process
Customers change nothing. AI turns each PDF into a draft, rules check prices, and a representative approves every draft until accuracy is proven. The order-desk lead owns the process day to day; drafts the AI cannot read go back to manual entry.
AI fit verdict
AI earns its place in one step only: reading purchase orders. Everywhere else, rules, a policy change or a person do the job better and more cheaply.
| Step | Best approach | Why |
|---|---|---|
| Read purchase-order PDFs | AI drafts, a person approves | Layouts vary by customer; AI extracts fields reliably, but a wrong quantity ships wrong goods, so a representative approves every draft at first |
| Check prices | Rules | Once contract prices are in the system, a price check is a lookup, not a judgement |
| Credit holds | Policy change + rules | The problem is the threshold, not the review; small overruns auto-release, the rest go to the credit manager |
| Exceptions (new customers, unusual orders, disputes) | People | Rare, high-stakes and relationship-driven |
| Invoicing | Simple automation | Invoice on shipment from the order system; no AI needed |
Benefit case: capacity is not cash
The changes free about 384 of the 532 monthly hours. That is capacity, not savings: it becomes cash only if Summit Ridge absorbs its planned 25% volume growth without hiring a fifth representative (about $58,000 a year), or redeploys people to sales support.
| Step | Hours now | Hours after | Hours freed |
|---|---|---|---|
| Purchase-order entry (AI draft, 3-minute review) | 360 | 90 | 270 |
| Price checks (rules; people handle the ~10% flagged) | 90 | 15 | 75 |
| Credit holds (holds fall from 18% to about 7%) | 32 | 12 | 20 |
| Invoice corrections (errors fall from 4% to about 1.5%) | 30 | 11 | 19 |
| Invoicing | 20 | 20 | 0 |
| Total | 532 | 148 | 384 |
Cash that is real now: invoicing on shipment brings cash in about 2 days sooner, releasing roughly $160,000 of working capital once ($2.4 million monthly revenue ÷ 30 days × 2).
Running costs to net off: an AI document-extraction service, assumed at about $600 a month, plus the review time already counted above.
| Metric | Baseline | 90-day target | Type |
|---|---|---|---|
| Order-desk hours on order-to-invoice | 532 a month | under 200 a month | Capacity released |
| Working capital tied up in invoicing delay | 2 days of revenue | same-day invoicing | Cash, one-time |
| Invoice error rate | 4% | 1.5% or lower | Quality guardrail: must not rise |
| Email-to-confirmation time | 1.2 days | same day | Customer guardrail |
30/60/90-day plan
AI goes live only after passing a 97% accuracy gate.
- Validate baseline (2 weeks)
- Load contract prices
- Approve new credit policy
- Label 200 real orders
- AI drafts: top 10 customers
- Every draft reviewed
- Daily invoicing goes live
- Measure against baseline
- AI drafts for all customers
- Review flags + 10% sample
- Order-desk lead owns it
- Monthly results review
Gate 1 (day 30): prices loaded, policy approved. Gate 2, go-live (day 60): 97%+ field accuracy on 200 labelled orders.
The data and policy fixes come first and pay off even if the AI pilot fails its gate. If accuracy stays under 97%, representatives keep reviewing every draft and the step still saves about two-thirds of keying time.