If you're evaluating AI automation, the most important question isn't "Is the tech impressive?" It's:
> Will it measurably improve the business?
The good news: many AI workflow automations have clear ROI because they reduce manual work, speed up turnaround times, and prevent costly errors. The challenge is that ROI often gets lost in vague claims like "10x productivity."
This guide gives you a business-first approach to measuring ROI — without needing complex finance models — plus examples for legal operations and SaaS teams.
Step 1: Define the workflow and its baseline cost
Pick one workflow. Don't start with "automate everything." Your goal is to establish a clear baseline: what the workflow costs today, how long it takes, and where the pain is.
What to capture (minimum)
For the workflow you choose, estimate:
- Volume: how many items per week/month
- Time per item: average minutes/hours
- Roles involved: who touches it
- Error / rework rate: how often it requires correction
- Cycle time: time from start to completion
- Escalation rate: how often it turns into a problem
You don't need perfect data. A two-week sample or even reasonable estimates are enough to start.
Baseline labor cost formula (simple)
Baseline Labor Cost per month =
(Items per month) × (Avg minutes per item ÷ 60) × (Hourly cost)
> Use loaded hourly cost if possible (wage + benefits + overhead). If you don't know it, use a conservative estimate.
Step 2: Identify where AI changes the workflow
AI automation usually doesn't remove the workflow entirely. It removes the low-value steps inside the workflow:
- reading and interpreting
- extracting fields
- classification and routing
- summarizing context
- follow-ups and reminders
- filling in systems (CRM, ticketing, document stores)
Your job is to estimate: how much time is reduced per item and what quality improvements are achieved.
Step 3: Measure ROI in three buckets
AI automation value generally falls into three buckets. You'll usually have a clear win in at least one.
Bucket A — Time savings (labor efficiency)
This is the easiest to quantify.
Monthly time savings =
(Items per month) × (Minutes saved per item ÷ 60)
Monthly labor savings =
Monthly time savings × hourly cost
Even if you don't reduce headcount, time savings translates to:
- higher capacity
- faster response times
- reduced burnout
- more time for strategic work
Bucket B — Quality gains (error reduction)
Quality improvements can be more valuable than labor savings.
Examples:
- fewer misrouted contract requests
- fewer missed deadlines
- fewer incorrect data entries
- fewer compliance mistakes
- fewer escalations and rework cycles
A simple quality model:
- define cost per error (or cost per rework cycle)
- estimate how many errors are reduced
Bucket C — Revenue and strategic impact
This is harder to measure, but often the biggest lever.
Examples:
- faster sales cycles
- improved conversion rates
- improved retention and renewals
- reduced churn risk
- improved NPS / CSAT
You don't need to rely on this bucket to justify the project — but it's often the upside that makes automation strategic.
Example ROI #1: Legal intake triage (contract requests)
Workflow: Contract requests arrive via email. Legal ops reads the request, extracts context, and routes to the right reviewer.
Baseline
- 250 requests/month
- 12 minutes average triage time
- Loaded hourly cost: $85/hour
Baseline labor cost:
250 × (12 ÷ 60) × 85 = $4,250/month
AI automation impact
AI can:
- classify request type
- extract metadata (counterparty, deal type, deadline)
- summarize request and key context
- route to the right reviewer
If AI reduces triage time from 12 minutes → 4 minutes:
- minutes saved per request: 8
- monthly time saved:
250 × 8 ÷ 60 = 33.3 hours/month - monthly labor savings:
33.3 × 85 = $2,830/month
Quality gains
If misrouting and incomplete intake info causes 10% rework (25 items/month), and each rework costs 20 minutes:
Rework cost baseline:
25 × (20 ÷ 60) × 85 = $708/month
If AI cuts rework in half:
$354/month quality savings
Total measurable benefit
Time savings + quality gains:
$2,830 + $354 = $3,184/month
Annualized:
$38,208/year
This doesn't include benefits like faster cycle time and reduced stakeholder friction, which are often substantial.
Example ROI #2: SaaS support ticket classification + summarization
Workflow: Tickets arrive. Agents triage, route, and respond. Misclassification and missing context cause delays.
Baseline
- 1,200 tickets/month
- 4 minutes triage time each
- Loaded hourly cost: $45/hour
Baseline triage labor cost:
1200 × (4 ÷ 60) × 45 = $3,600/month
AI automation impact
AI can:
- auto-tag by category
- detect urgency signals
- route to correct queue
- summarize the issue + environment details
- extract structured fields (customer, product, severity)
If triage time drops from 4 minutes → 1 minute:
- minutes saved per ticket: 3
- monthly time saved:
1200 × 3 ÷ 60 = 60 hours/month - labor savings:
60 × 45 = $2,700/month
Quality + retention gains (optional upside)
If faster routing and clearer summaries reduce churn by even a small amount, ROI grows quickly.
Example:
If this prevents one churn event per quarter worth $8k ARR:
Annual impact:
$8k × 4 = $32k/year
Even conservative improvements can make automation strategic.
Step 4: Account for implementation and ongoing costs
You should include:
- build cost (engineering + integration)
- tooling costs (hosting, APIs)
- monitoring and maintenance
- ongoing improvements
A practical approach:
- calculate ROI at 3 months and 12 months
- include conservative estimates for maintenance
> If you're unsure, plan for a baseline maintenance cost and improve accuracy later.
Step 5: Define success metrics before you build
If you want ROI to be undeniable, define success before implementation.
Strong legal metrics
- time-to-triage
- time-to-first-review
- request backlog
- rework rate
- missed deadlines
Strong SaaS metrics
- time-to-assign
- time-to-first-response
- ticket backlog
- resolution time
- CSAT/NPS shifts
Pick 2–4 metrics. Track them weekly.
A practical ROI checklist (fast)
Before building, answer:
- What is the workflow and its volume?
- What is time per item today?
- What's the rework/error rate?
- What steps can AI reduce?
- What is "done" and who approves?
- What are the success metrics?
- How will we monitor output quality?
If you can answer these, you can justify the project.
The bottom line
AI automation ROI is easiest to prove when:
- the workflow is frequent
- manual steps are consistent
- speed matters
- rework and misrouting are costly
Start with one workflow, measure before and after, and scale from success.
Want help building your ROI model?
Stratus Logic designs AI automation that's measurable, secure, and built to scale.