In this article
The essential idea
Choose one recurring decision, measure the work around it, then test an assistant against the current process. The useful result is a workflow the team can operate, including the cases it must hand back.
A first use case: preparing a support reply
Imagine a support team receiving requests about delivery, damaged products and billing. The tempting brief is “automate support”. A workable first brief is narrower: identify delivery-status requests, retrieve the order and prepare a reply for an adviser. Refunds, disputes and changes of address stay outside the pilot.
Follow several requests from arrival to closure. Where does the adviser search? Which information is missing? Who resolves exceptions? A classification that takes seconds is not necessarily the expensive part; switching between three systems may consume most of the effort. Automate the bottleneck you observe, rather than the part that looks most impressive in a demonstration.
Write the contract before choosing the model
Describe the input, the permitted sources and the expected output. For this example: a customer message and authenticated customer identifier enter; a draft with order status and a traceable source leaves. The model must not invent an order number, select another customer’s order or promise compensation.
Split interpretation from execution. The model can interpret the message; application code verifies identity, reads the order and checks the response format. Missing identifiers lead to a clarification request. Conflicting information goes to an adviser. These paths belong in the first design because they determine whether the pilot can handle normal, imperfect requests.
A pilot with a clear decision at the end
Start from one recurring task and a result the team can assess.
Observe
Find repetitive work and its current exceptions.
Frame
Estimate effort, cost and review needs.
Test
Build one narrow workflow with representative inputs.
Decide
Expand, adjust or stop based on the pilot evidence.
Calculate time recovered, including review
Use an explicit worked example, not a promised return. Suppose 600 requests per month currently take four minutes each: 40 hours. If checking a prepared reply takes one minute, that is 10 hours. Add eight hours for exceptions, maintenance and reviewing failed drafts: the new workload is 18 hours, leaving 22 hours of potential capacity.
Those numbers are illustrative assumptions, not MASLOV Solutions results. Replace them with observed durations and include software costs, implementation and the time needed to learn the tool. Capacity recovered is not automatically a cash saving: decide whether it will absorb growth, shorten waiting times or allow advisers to work on more difficult cases.
Illustrative monthly workload
Assumptions: 600 requests. Before: 600 × 4 min = 40 h. Pilot: 600 × 1 min review + 8 h exceptions and maintenance = 18 h. Difference: 22 h of potential capacity, not a measured saving.
hours / month
Run in shadow mode before sending anything
During an initial shadow phase, the assistant prepares replies while the team continues its existing process. Compare both outputs without sending the assistant’s version to customers. Include incomplete requests, multiple orders, old messages, unavailable carriers and text that tries to instruct the assistant to ignore its rules.
Have an adviser label the outcome: usable as written, useful after editing, wrong or outside scope. Record why a draft failed. A missing order connector requires different work from an ambiguous customer request. Human review reduces risk only when the reviewer has enough context and time; adding an approval button does not guarantee quality.
A pilot with an explicit handover
Proposed draft-only pilot. An adviser remains responsible for sending the customer reply.
Message → customer identity → order lookup
Request in scope and order information available?
Yes
- Prepare draft with order evidence
- Adviser checks, edits and sends
No
- Keep the request in the manual queue
- Show missing information or failure reason
Decide what earns a production release
Agree release criteria before seeing the pilot results. The team might require that every draft identifies its order, that unavailable sources produce no delivery promise, and that unauthorised orders never appear. Set acceptable editing effort and response time from the service’s actual needs; there is no universal score that makes an assistant production-ready.
Release to a bounded queue with a named owner, a way to disable draft generation and a manual fallback. If review takes longer than writing from scratch, revise the workflow or stop. A failed pilot can still be useful when it identifies that inconsistent source data, rather than model quality, is the real obstacle.
FAQ / DECISIONS
Frequently asked questions
How long does a first AI automation project take?+
There is no universal duration. Bound the workflow, evaluation set, decision owner and production criteria before estimating a pilot.
Do I need a lot of data to get started?+
Data needs depend on the error types and decision. Use a representative evaluation set; do not infer production quality from a few convenient examples.