Useful AI automation examples pair a bounded interpretation task with a verified business rule and a clear destination. AI might extract a purchase-order number from a scanned invoice, for example, while deterministic logic checks whether it matches an approved order. A mismatch should go to an accounts-payable reviewer, not become an automatic payment decision.
This guide turns seven familiar use cases into implementation designs. Each one identifies the source event, relevant inputs, AI output, validation gate, destination, and exception owner. The linked Make pages verify broad use cases and listed app combinations, but they do not necessarily expose every prompt, mapping, filter, or exception path. Treat the sequences below as designs to confirm after cloning.
The goal is not to add AI to every workflow. It is to use AI where variable language or context is the problem, then use deterministic controls wherever the business rule must be exact and reproducible.
What makes a workflow a good candidate for AI automation?
AI automation is a workflow in which an AI model interprets variable or unstructured input and returns information used by later steps. A workflow platform or business application then performs the operational action. For example, AI can classify a free-text support request while a helpdesk system uses the validated category to route it.
Use deterministic rules for stable fields, exact thresholds, and decisions that must be reproduced consistently. Consider AI for free text, multilingual messages, inconsistent phrasing, or context-dependent classification. Keep the AI task bounded: classify, extract, summarize, or draft. Do not give a model unbounded authority over refunds, approvals, legal commitments, or customer promises.
Use AI to interpret variable meaning; use deterministic logic to enforce exact business rules.
A dependable operating pattern is source event, prepared input, bounded AI task, schema validation, business decision, and destination action or human review. In an invoice workflow, extraction is not approval. The extracted values must still pass authoritative accounting checks.
A build pattern that works across all seven examples
Before building, name the system of record, source event ID, allowed AI task, required output fields, destination write, and person or queue responsible for exceptions. Require structured output whenever the workflow branches or updates a business record.
The following is an illustrative contract, not a Make template schema. Reject missing fields, unexpected labels, incorrect data types, confidence values outside the permitted range, and identifiers that do not exist in the destination.
{
"category": "billing",
"urgency": "normal",
"confidence": 0.91,
"needs_review": false,
"schema_version": "1"
}Keep original source data distinguishable from generated fields. Where audit or reprocessing matters, retain the source ID or file reference, model or provider, prompt or schema version, processing time, workflow version, and review state. Confidence can help select cases for review, but it is not proof of correctness or permission to act.
A search-then-create check alone is not safe when runs can be concurrent. Two runs may both find no record and then create duplicates. Prefer a destination-supported upsert, unique constraint, transactional insert, or durable idempotency record. Make documents duplicate-data and concurrent Data Store errors in its error guidance and provides configurable retry and error-handling options. A retry still requires a safe destination write.
Seven AI automation examples, with the control that matters most
The table is a selection guide. The AI jobs and controls are proposed implementation designs unless the linked template page explicitly confirms them. Inspect a cloned scenario before relying on exact modules or mappings.
| Workflow and trigger | AI job and input | Validation and action | Fallback owner |
|---|---|---|---|
| Support ticket triage New Zendesk ticket |
Classify issue, expertise, and urgency from ticket ID, subject, and body | Allowlisted categories and maintained route map before assignment | Support triage lead |
| Lead enrichment Form webhook |
Normalize supplied company details from submission ID, email, and domain | Preserve source fields and use CRM upsert or uniqueness control | Sales operations |
| Blog to social Approved article event |
Draft platform-specific copy from approved article material | Current approval, content version, and returned post ID | Content approver |
| Invoice extraction PDF in Google Drive |
Extract vendor, invoice, tax, total, and line-item fields | Arithmetic, duplicate, vendor, currency, and purchase-order checks | Accounts payable |
| Feedback analysis Google Sheets row |
Label sentiment and controlled topics using stable feedback ID | Write at row grain and retain ambiguous cases for review | Customer insights |
| Email classification New Gmail message |
Assign an allowed helpdesk category from message and thread data | Thread or message deduplication before Freshdesk creation | Helpdesk triage |
| Project enrichment Changed Notion item |
Summarize status and identify text-grounded risk | Compare source revision and separate generated properties | Project owner |
1. Classify and route support tickets
Process: A Zendesk ticket supplies its ID, subject, body, and any relevant account or product fields. An AI step returns a category, required expertise, urgency, confidence, and review flag. Validation checks the category and expertise against enumerated values, then a maintained table maps expertise to an approved Zendesk group or agent.
A ticket about a damaged device might produce category=product_damage and expertise=hardware_support. Those values are illustrative. The model should not invent an agent or group ID. Unknown categories, low confidence, and high-severity cases should go to the support triage lead. Use the ticket ID plus classification version as a proposed processing key.
Make lists a Zendesk and OpenAI template for ticket categorization and expertise-based allocation, with Zendesk, OpenAI, Flow Control, and Tools shown on the page. The public page does not establish the complete prompt, mapping, or routing sequence.
2. Enrich a lead and hand it to the CRM
Process: A form webhook captures the submission ID, email, company domain, source, and consent or lawful-basis information where relevant. AI normalizes supplied company information and returns fields such as company name, industry, employee range, source, confidence, and review status. The workflow validates JSON, preserves the submitted values, and writes to the intended Salesforce object.
Treat generated company facts as unverified unless they come from an approved and attributable data source. Do not overwrite authoritative CRM values without an explicit precedence rule. Use a Salesforce-supported upsert, unique key, or database-enforced constraint rather than relying only on lookup then create. Route uncertain records to sales operations. For field ownership and duplicate prevention, see CRM systems consulting.
The current Make lead-enrichment template lists Webhooks, OpenAI, JSON, Salesforce, and Slack. Its public page does not establish the enrichment source, CRM object, or duplicate behavior.
3. Draft social posts, then publish only approved content
Process: A published article starts a generation stage. AI drafts platform-specific copy from approved article material, and the workflow stores the article ID, platform, content version, draft, and approval state. A separate publishing stage receives only approved content, sends it to the supported channel, and records the returned post ID.
Use a proposed key of source_article_id + platform + content_version. Do not use a publication date alone because revisions and multiple platforms can collide. Require a current approval status and approver record. If a destination post succeeds but the status update fails, reconcile the destination using its post ID before retrying.
Make references a first-stage blog-to-social generation template and a separate second-stage publishing template. The second-stage page lists Webhooks, Flow Control, Airtable, HTTP, LinkedIn, and Facebook Pages. It does not verify an X or Twitter branch, so confirm channels in the cloned scenario.
A social post can publish successfully while the tracking record fails to update. Save the destination post ID when available, reconcile the channel, and only then resubmit. Otherwise a normal retry can create a duplicate.
4. Extract invoice data for accounts-payable review
Process: A PDF invoice added to Google Drive is parsed into standardized fields such as vendor, invoice number, currency, total, tax, due date, and line items. Deterministic checks reconcile line items and tax with the stated total, then compare vendor, currency, invoice identity, purchase-order reference, and permitted amount tolerance against authoritative records.
Use a proposed duplicate key such as vendor ID, normalized invoice number, and currency, subject to the accounting system’s rules. Missing fields, arithmetic differences, duplicate candidates, unapproved vendors, and purchase-order mismatches go to accounts payable. Extraction confidence is only evidence about whether the document was read correctly. It is not an approval decision.
The verified PDF.co invoice template uses Google Drive, PDF.co, and Google Sheets for extraction and is partner-created, with Make stating that it has not tested the template. Purchase-order comparison, approval routing, audit design, and accounting-system posting are additional implementation work, not verified features of that page.
5. Classify customer-feedback sentiment
Process: A new or selected Google Sheets row supplies a stable feedback ID, row ID, text, and submission time. AI returns an allowed sentiment such as positive, negative, neutral, or mixed, plus controlled topics and confidence. The workflow validates the labels and writes the result back to the same feedback record.
Keep the row-level identity intact. Do not use a daily aggregate key for individual feedback because multiple submissions, model runs, or revisions may be overwritten. Treat sarcasm, mixed sentiment, multilingual text, empty feedback, and materially ambiguous wording as review cases. Calculate rates only after the row-level records are stored.
The Google Sheets and OpenAI sentiment template verifies the broad analysis use case. Confirm the trigger, columns, prompt, and write mapping after cloning.
6. Classify email before creating a helpdesk ticket
Process: A Gmail message provides message ID, thread ID, sender, subject, body, and attachment references. AI assigns an approved category and optional sentiment or routing label. The workflow validates structured output, checks for an existing Freshdesk ticket linked to the message or thread, and creates a ticket only when the event is not already represented.
Use message identity when each message needs separate handling, and thread identity when the workflow is thread-level. Do not infer urgency from sentiment alone. Legal, security, abusive, sensitive, or high-severity messages should go to a human queue. Invalid JSON, unknown categories, and missing message identifiers should stop before ticket creation.
The current Gmail, OpenAI, JSON, Flow Control, and Freshdesk template describes email classification and ticket creation. Confirm destination groups, field mappings, and deduplication behavior in the cloned workflow.
7. Enrich project records in Notion
Process: A selected or changed Notion item supplies its item ID, source revision, status text, and relevant properties. AI returns a summary, a risk grounded in the supplied text, and a suggested next step. Validation checks that the source revision is newer than the last processed revision, then writes generated fields separately from source properties.
Do not let the model invent dates, owners, or commitments. Store the source revision, model or provider, schema version, and generated timestamp. Use the Notion item ID plus source revision as a proposed processing key, and ensure that writing generated fields does not retrigger the same workflow indefinitely. Incomplete output should go to a review property rather than replace source data.
The Notion and OpenAI template verifies the broad database-enrichment pattern. Check its trigger, properties, output fields, and loop behavior after cloning.
Test the workflow before letting it write to production
Start in draft, review, or shadow mode where practical. Compare AI results with human decisions before enabling consequential actions. Test normal cases and edge cases, including missing fields, ambiguous or multilingual text, malformed output, duplicate events, destination failures, rate limits, and partial success.
Measure against a baseline rather than assuming benefits. Useful measures include handling time, time to first action, rework rate, exception rate, duplicate rate, and cost per processed event. Keep raw observations separate from reported summaries: an individual ticket, invoice, email, model run, and citation are different records from a daily or weekly aggregate. Assign an owner for taxonomy changes, prompt and schema versions, exception review, and periodic quality checks.
- Representative edge cases and malformed AI output have been tested.
- A stable source-event key and destination-side uniqueness or upsert control are in place.
- Schema validation, deterministic business rules, and a human fallback are defined.
- A named person or queue owns exceptions and review decisions.
- Baseline quality, duplicate, exception, and cost measures are recorded at the correct grain.
Make’s current pricing page lists a Free plan with 1,000 monthly credits and a 15-minute minimum interval between runs. Credit consumption depends on execution behavior, including bundles, searches, repetitions, and modules. Plan limits do not establish that every connected application or AI provider is free or available for a particular workflow. Check permissions, quotas, API access, retention settings, and provider terms before sending customer, employee, or financial data to a model. For help designing or auditing a scenario, see Make automation services.
Choose the first workflow by operational friction
Start with a repetitive process that has a visible handoff cost, accessible source data, a manageable category set, and a low-risk action or clear approval gate. Name one process owner and one system of record. Estimate event volume, exception rate, downstream impact, and the cost of a wrong decision before prioritizing.
A feedback-classification pilot that writes a reviewable label to a stable row ID may be a sensible first test. Defer automated refunds or invoice approvals until the deterministic rules, ownership, and recovery path are explicit. If categories or approvals are unclear, resolve that ambiguity before automating it.