When a company asks me about adopting AI, I start with a task and the person who will use its result. “Help sales” is still too broad. “Suggest a category for each incoming request and leave ambiguous ones in a review queue” gives us something specific to discuss: data, boundaries and integration.
The cases below are scenarios to assess, not client results or promises of profitability. A frequent task can still be a poor candidate if checking the output takes as much effort as doing the work, or if nobody can verify that it is correct.
Four business AI use cases and what to require
Use this table to define the application. Collect the proposed evidence using examples that represent your company's process, including the cases that currently cause uncertainty or rework.
| Application | Bounded output | Evidence before expanding | When to defer it |
|---|---|---|---|
| Classify requests | A suggested category; ambiguous requests go to review. | Errors by category, correction time and consequences of a wrong assignment. | The team has not agreed on classification criteria. |
| Draft support replies | A draft based on approved information; a person decides whether to send it. | Factual accuracy, required corrections and total time to resolve the enquiry. | Support policies change without an updated source. |
| Search internal documents | An answer with a source, restricted to the user's access. | Supporting references, freshness and behaviour when information is missing. | Nobody owns the sources or access rules are undefined. |
| Extract document fields | A proposed record with missing fields marked and a reference to the original. | Accuracy by field, format errors and validation effort in the destination system. | The next team's required data schema is undefined. |
For a closer look at a particular application, see the guides to lead classification, AI for customer service and RAG with sources and permissions. Each covers decisions that this comparison only introduces.
Check whether the task needs AI
If the data already exists in a field and the rule is stable, I first check whether a validation, an integration or an existing product feature can handle it. Copying a CRM status into another system does not require language interpretation. Understanding a request written in different ways might justify testing a model.
I also compare features in the team's current software with a custom integration. Access controls, data exports, review options and maintenance ownership matter. Building something new is not automatically the best choice.
The brief I request for an AI use case
Before recommending a pilot, I prepare a brief with the process owner. Unknowns remain explicitly unresolved; they are not replaced with optimistic estimates.
- Task and user: who receives the output and what decision they make with it.
- Input and source: available examples, permission to use them, formats and the person responsible for keeping them current.
- Output and destination: expected fields or draft, where review happens and how the result reaches the existing system.
- Errors and limits: what can be corrected, what must stop the workflow and which actions require approval.
- Comparison: how the same task is done today and which alternative without AI will be assessed.
- Ownership: who reviews exceptions, accepts the pilot and operates the workflow afterwards.
As an external reference, Microsoft assesses business value, user needs and technical feasibility. The brief above is my practical proposal for turning those questions into a reviewable scope. It is neither a certification nor a guarantee of returns.
A hypothetical example: classify before replying
Imagine a team handling order enquiries. It is considering two applications: suggesting a category to the team or sending an answer to the customer. Both read the same message, but the second also needs the order status, current policies and rules about which commitments it can make.
If categories are already agreed and someone can review them, I would propose testing classification on authorised historical messages without updating the CRM. The sample would include ambiguous cases and consequential errors. If the team disagrees on labels, I would resolve that disagreement first. A longer prompt would not be my starting point.
The choice to test classification depends on those conditions. It does not prove time savings or establish that replies can later be sent automatically.
Separate usefulness, savings and engineering evidence
A draft can be correct and still take too much effort to review. The comparison needs to include data preparation, correction, exceptions, integration and operation. Use the AI effort and savings log to measure that work without counting the same benefit twice. Available hours do not automatically mean lower spending.
I also distinguish what a portfolio proves. Adslyfy's sanitised screenshots and execution history provide evidence of monitoring decisions. They do not establish profitability, accuracy or savings for the AI scenarios in this guide.
What to agree before commissioning the pilot
I would ask for a delivery covering the bounded workflow, affected systems, an evaluation sample, the person validating the result and a record of unresolved questions. I would separate implementation costs from model usage, tools, review and maintenance. The scope needs to explain what happens with incomplete data and how the team returns to the manual process.
If a prototype already exists, the next step is to review its evidence before production. Selecting a use case and accepting an operational system are separate decisions.
Turn an AI idea into a specific task
Through my automation and applied AI service, I work from the business process through implementation and agreed operation. If you are assessing an application, tell me which task you want to improve, who would use the result and which examples you can share. We can then assess whether to test AI, use a rule or prepare the data first.