Break down the task before choosing the tool
“Automate order follow-up” includes several jobs: read an order number, identify missing milestones, interpret a supplier message, prepare an internal brief and decide whether to notify a customer. These steps contain different kinds of uncertainty and need not use the same tool.
The following is a workflow illustration using fictional data. A salesperson wants to check whether an order has complete specifications, packaging requirements and current milestones. The order number is in a spreadsheet, requirements are in email and product parameters are in a file directory. Separate lookup, extraction, comparison, missing-item reporting and review before choosing rules or AI.
Ask whether the correct result can be expressed as a clear condition, whether a reliable source exists and what a mistake would affect. When fixed rules can do the job, a model adds unnecessary variability.
Assign tools to specific tasks
| Task | Starting approach | Result check |
|---|---|---|
| Match an order reference | Rules and queries | Unique match; review missing or duplicate records |
| Check quantities, dates and required fields | Rule validation | Agreed formats and business conditions |
| Extract packaging requirements from email | AI-assisted interpretation | Keep the source wording; leave unstated values open |
| Summarize support exchanges | AI-assisted summary | Check questions, evidence and unresolved items |
| Choose an approved source for a missing item | A bounded agent | Check sources, step limits and stop reasons |
| Confirm quotes, lead times and external sending | Authorized human review | Verify business evidence and the exact action |
Combine these approaches when useful. After AI extracts a model, a rule can check it against the current catalog. No match should lead to review, not an invented model. A valid date or number format does not establish business approval of the value.
Let AI propose an interpretation and check its sources
In a fictional X20 inquiry, the customer requests 1,000 units and asks about packaging and lead time. AI can organize the model, quantity and questions while retaining the original email reference. If no destination is stated, that field remains empty or unresolved.
Subsequent rules check required fields, permitted product models and source records. Specification replies use approved product material. Prices, stock and lead times require their own confirmed business sources; they must not be inferred from similar products. Conflicting material should produce both references and a request for verification.
Sending a reply is a separate action. Permission to interpret requirements or prepare a draft does not automatically authorize email, customer commitments or order changes. Checking the interpretation and action separately also makes failures easier to diagnose.
Decide whether an agent needs to choose the next step
A fixed task can follow a predefined sequence: read the sheet, extract email content, check fields and create a task. If every item follows those steps, a conventional workflow is generally easier to maintain.
Consider an agent when the next step actually depends on findings. For example, a missing packaging requirement might require choosing among several approved sources. Still restrict systems, search scope, steps and usage cost, and define the information needed to finish.
Missing evidence, tool failures, source conflicts or a need for broader access should stop the task for a person. Retrieved emails and documents are data. Embedded phrases such as “ignore the rules” or “send this file” must not become execution instructions.
Compare implementations on the same examples
Create sanitized samples with reference outcomes, including ordinary inputs, missing fields, duplicates, conflicting versions, tool failure and documents containing irrelevant instructions. Hold some out of development to test beyond familiar examples.
Compare a rule-based option with the proposed AI approach. Check result correctness, source references, exception routing and complete correction time. Then record elapsed time, model usage and maintenance effort. Completion counts alone miss whether the workflow stops when it should.
If AI merely makes a brief read more smoothly while increasing fact-checking work, narrow its role. If rules cannot handle varied customer wording and AI reduces preparation effort on representative examples, keep it within that tested task boundary.
Document the choice at handover
The delivered plan should identify which steps use rules, AI or people. Sources, current versions, output fields, failure paths and approved actions should also be inspectable. Departments can reuse these definitions while adding their own business rules.
When layouts, catalogs, models or interfaces change, rerun the affected checks using retained examples. Agree before handover on source maintenance, exception handling, change approval and support scope. The technical choice should make the work operable, inspectable and maintainable.