Start with a process, not an AI subscription
Choose one bounded workflow: order exceptions, incoming request triage, invoice matching or the preparation of a recurring report. Record its monthly volume, time per item, error types and the person responsible when it fails. A week of observed work is a more useful baseline than a guess about how busy the team feels.
Separate routine cases from exceptions. If half the work requires missing information or negotiation, the automation opportunity is smaller than the total workload. The first investment may be improving the process or data rather than adding software.
Calculate the benefit after human review
Monthly capacity value = (baseline hours × share of work removed − new review and exception hours) × fully loaded hourly cost. Subtract recurring software, hosting and support costs to estimate the monthly net benefit. Keep implementation and migration costs separate.
Hours released are capacity, not automatically cash saved. They become financial savings only when spending actually falls, or additional profitable work can be delivered without adding the same labor. Do not count the same released hours as both payroll savings and new revenue.
A worked example: handling operational requests
The figures below are hypothetical planning inputs, not Raytech prices or client results. Suppose a team handles 2,800 requests per month at three minutes each: 140 hours. An initial design removes 75% of that handling time but adds 24 hours of review and exception work.
That releases 81 hours per month. At an assumed fully loaded cost of $32 per hour, capacity is worth $2,592. After $350 in recurring costs, the modeled net benefit is $2,242 per month. With a $12,000 implementation cost, simple payback is about 5.4 months after reaching that operating level; ramp-up time delays it.
| Input or result | Illustrative assumption |
|---|---|
| Monthly baseline | 2,800 × 3 minutes ÷ 60 = 140 hours |
| Handling time removed | 140 × 75% = 105 hours |
| Remaining new review/exception work | 24 hours |
| Net capacity released | 105 − 24 = 81 hours |
| Monthly benefit after recurring costs | 81 × $32 − $350 = $2,242 |
| Simple implementation payback | $12,000 ÷ $2,242 ≈ 5.4 months |
Test the downside before approving the budget
If the system removes only 50% of handling time with the same review burden, the monthly benefit falls to $1,122 and payback extends to about 10.7 months. If review rises as volume grows, or the workflow changes frequently, the business case becomes weaker again.
Model a conservative, expected and optimistic scenario. Include setup delays, data cleanup, staff training, vendor fees and a support owner. If the conservative scenario cannot justify the investment, narrow the scope or run a smaller pilot before committing.
Make the pilot prove operational quality
Define acceptance criteria before implementation: which cases may run automatically, which require review, what counts as a correct result and how failures are surfaced. Compare output against human-reviewed examples that include difficult cases, not only clean demonstrations.
For AI-assisted decisions, assess the consequences of an incorrect output and keep appropriate human review. NIST’s AI Risk Management Framework organizes this work around governing, mapping, measuring and managing risk. The level of oversight should follow the consequences of failure.
- Sample real workflow volume and handling time.
- Count correct outcomes and exception work separately.
- Test duplicate inputs, unavailable dependencies and recovery.
- Assign ownership of alerts, updates and vendor costs.
- Define the conditions for stopping or expanding the pilot.
What a useful automation proposal should contain
Ask for a process map, integration assumptions, the scope of human review, an itemized implementation estimate and a recurring-cost model. Ownership of accounts, data and documentation should be clear before work begins. A reliable proposal explains the operational tradeoffs as well as the happy path.
Bring one workflow and a measured baseline to Raytech. We can use those inputs to scope a pilot, identify integration constraints and define what must be demonstrated before a larger rollout. The decision is whether the system improves your operation enough to justify its full cost.