How can I make money with AI? It is an understandable question. It also leaves out the work, the customer and the problem. A list of business ideas cannot tell you which idea fits your experience or which customer would value the result.
In my second AI with Honor LIVE on October 4, 2026, I suggested asking the assistant to ask questions until it has enough context to suggest relevant uses. I also suggested looking for a problem in the business where you work, with care about what you are allowed to do. Those are starting points for a conversation, not evidence that a particular business will earn money.
Here is how I would turn that starting point into a practical process. Describe the work. Find an observable problem. Test a small improvement with permission. Compare the result with the current method. Keep the distinction between something that can be built and something someone actually needs.
Replace Vague Income Questions With Concrete Tasks
The problem with starting from "how can I make money" is that it skips the part where you actually understand what you are working with. Useful work is a starting point, but it still needs a customer, a workable price and a way to deliver it. If you do not know what problem you are trying to solve, any suggestion from an AI tool will feel like a guess. Ask the assistant to ask you questions about your work until it has enough context to make a relevant suggestion. This can help you articulate what you do, step by step, rather than leaving the definition up to the model's assumptions.
When an AI asks clarifying questions, it is pushing you to describe the inputs, the outputs, the constraints, and the pain points. What data do you work with? Where does it come from? Where does it go? What has to happen for the task to be considered finished? These are not abstract questions. They are the boundaries of any real-world task. By answering them, you create a map that both you and the tool can follow. This is the first step in moving from a general curiosity to a specific experiment.
Slow down and describe the work clearly before handing a task to a tool. That description is where usefulness begins. Without it, you are asking the tool to read your mind. With it, you are giving it a problem space it can actually navigate.
Ask Clarifying Questions
Once you have decided to look for a problem inside your own workflow, the next step is to ask good questions. This is where many people stall because they assume the AI should just know what to do. The context available to an assistant varies with its settings and prior access. Ask it to state what it thinks it knows, then correct what is missing or wrong. Answer relevant questions with enough detail to describe the task, while keeping private information out of an unapproved tool.
What does a good clarifying question look like? It is specific to your situation, not generic. "How do you currently handle X?" "What part of this process feels slowest?" "Where do you usually lose track of information?" These questions force you to articulate the parts of your day that run on habit rather than design. They also give the AI the concrete details it needs to suggest something that might actually fit.
Be careful when looking at problems inside someone else’s business. That caution is worth carrying into this phase. You are not trying to redesign your company’s entire operation. You are looking for a small, contained piece that you can describe, test, and potentially improve. The questions you ask should illuminate the edges of that piece, not demand access to everything the company does. Keep the scope narrow, the questions focused, and the information you share appropriate to the task at hand.
Describe Current Workflow
After you have asked clarifying questions and the AI has a better sense of what you do, the next step is to describe your current workflow as it actually exists, not as you wish it existed. This is where many plans fail because they start from an idealized version of the work. Start with the reality on the ground, including the exceptions that are easy to leave out of a neat process description.
Describe the steps you take from the moment a task starts to the moment it finishes. Mention the tools you use, even if they feel basic or outdated. Note where you have to manually move information from one place to another. Note the moments when you check your work twice because you are not sure it is correct. These details are the raw material for any improvement, AI-assisted or not.
Be careful about what you share and with whom. This is not a request to broadcast your company’s internal processes to the world. Describe the work accurately and get permission before involving information or systems that belong to someone else. Describing your workflow out loud, or into a chat window, helps you see it more clearly. You might discover that a step you thought was essential is actually optional, or that two separate tasks could be combined. That clarity is the foundation for any useful experiment.
Find an Observable Problem
With a description of your current workflow in hand, you can now look for an observable problem. This is not a theoretical complaint like "I wish things were faster." It is a specific, repeatable pain point that you can point to and say, "This happens every time we do X, and it takes too long" or "This always results in an error."
Finding a problem inside your own work context means looking at the things that frustrate you, your colleagues, or your customers on a regular basis. For a hypothetical example, it could be a report that takes three hours to compile because data has to be copied from five different systems. It could be a customer service reply that gets sent back and forth three times before the issue is resolved. It could be a weekly meeting where the same questions are asked because the information people need is not in one place.
The key is that the problem must be observable. You should be able to see it happening, measure roughly how much time or effort it costs, and identify what about it feels wrong. This is not about finding something to complain about; it is about finding something that, if improved, would free up time, reduce errors, or make the work less frustrating. The permission check applies here too: you are looking for a problem you have permission to address, or at least one you can test without overstepping boundaries.
Distinguish Your Experience From Customer Evidence
Your frustration is useful evidence about your experience. It does not tell you whether a customer has the same problem, whether they care enough to change, or whether your proposed improvement would fit their work. Keep those questions separate.
For a hypothetical example, an office manager might dislike copying information into a weekly report. A business owner might care more about whether the report reveals an overdue decision. A tool that produces a prettier report faster may leave the important problem untouched. Ask the person using the result what they need to decide, and ask the person preparing it what makes that difficult.
Look for the current workaround. What does someone do when the problem occurs? Who corrects the mistake? What happens if the task waits? These questions can reveal whether the issue is inconvenient, consequential, or already handled adequately. A polite expression of interest is different from agreeing to test a change in a real workflow.
A successful personal test is useful, but keep the claim as narrow as the evidence. Record the task, the conditions, the checks and the result. Do not turn one favorable attempt into a promise about every customer. Feedback can guide the next test while still leaving demand and willingness to pay unresolved.
Define the Smallest Useful Outcome
The final step in this preliminary framework is to define the smallest useful outcome you are willing to test. This is where you decide what "success" looks like for your experiment, and it should be modest. I recommend a small prototype that can be reversed. That means choosing a version of the idea that you can try without making permanent changes, and limiting its scope so that if it does not work, you can stop without changing the live workflow, while accounting for any time or money already spent.
What is the smallest thing you could do with AI assistance that would still feel like an improvement? For a hypothetical example, it might be drafting the first version of a routine email so you can edit it rather than writing from scratch. Maybe it is summarizing a long document so you can decide whether to read it in full. Maybe it is organizing a set of notes into categories so you can find what you need faster. The outcome should be useful enough that you notice the difference, but small enough that you can stop if it creates more work than it saves.
Measure effort and errors, compare the AI-assisted attempt to the manual alternative, and check whether tools you already own can handle the problem. These are practical checks, not guarantees. They help you answer the question: is this worth continuing? If the AI draft requires as much editing as writing from scratch, or if a tool you already own does the job adequately, the experiment has taught you something useful even if you do not continue.
Employer/Client Permission and Minimal Information
Before any code runs or draft writes itself, the first gate is authorization. If you are an employee, follow the organization’s policies for the data and systems you use. Confirm what uses are permitted before running an experiment with company information or systems. Start by scheduling a brief conversation with your manager or the appropriate department head. Frame it around productivity, not replacement. Say something like: "I'm exploring whether AI can help me work through a recurring problem I have. I'd like to run a small test first and keep any results contained." Get that permission in writing if possible, or at least an email trail. If you are a business owner, the same principle applies to clients. Explain what you intend to do, what data you will use, and how you will protect confidentiality.
The scope of information you share with AI must be minimal. Do not paste customer lists, financial statements or proprietary processes into an unapproved tool. For an initial experiment, use invented information. Removing obvious identifying details from real records may still leave confidential or recognizable material. Replace names with roles or generic labels. Remove exact dates, amounts, or locations that could be traced back to a person or transaction. The goal is to give the assistant enough context to be helpful without exposing anything sensitive. If the useful test requires protected information, stop and have the responsible owner decide what environment and access are appropriate. Running a tool locally does not, by itself, settle that question.
Test a Small Reversible Draft with Invented Test Data
Once permission is secured and information is sanitized, write a draft that can be reversed if it goes wrong. Choose a task that is bounded in time and scope. For a hypothetical customer-service test, write three fictional complaints and ask the assistant to summarize their common issue in a paragraph. Use invented test data that looks real but is not. Create fake customer names, fictional case numbers, and made-up product details. This lets you see how the AI handles structure, tone, and logic without risking real information.
Set a time limit appropriate to the task before you begin. If the output is unusable, record why before deciding whether another attempt is justified. If the output is useful, keep it as a reference point for future work. The key is that this first attempt is disposable. It is a learning step, not a deliverable. Treat it like a sketch rather than a final product.
Check Dependencies and Manual Alternatives
Check the dependencies before starting the test. Before investing time, check what you already have. Do you have approved access to the AI tool you intend to use? Is your organization’s network configured to reach it? Are there firewalls or approval processes that could block access mid-task? Test the connection early.
Next, identify manual alternatives. What would you do if the AI failed to produce anything useful? Perhaps you have existing templates, spreadsheets, or colleagues who could help. Write down those fallback options so you know exactly where to turn if the AI underperforms. This step prevents wasted hours waiting for a response that never comes. It also keeps you in control of the workflow rather than at the mercy of a black box.
If you are a business owner, consider whether your existing software tools can handle part of this work before adding AI. Many companies already pay for CRM systems, document managers, or communication platforms that include basic automation. Adding AI on top of broken dependencies only compounds the problem. Start with what works, then layer in AI where it adds clear value.
Measure Effort/Errors/Costs Without Inventing Results
When you run your first test, record what actually happened. How long did it take to set up the prompt? How many rounds of back-and-forth did the assistant need before the output made sense? Note any errors, hallucinations, or missing steps. Track the time you spent reviewing and editing the output. These are real metrics, not estimates.
Also note the cost, if any. Some AI tools are free within certain usage limits; others charge per token or per month. Record what you paid, if anything, for this specific test. Do not invent projected savings or productivity gains at this stage. Those numbers come later, after you have data from multiple runs. For now, capture the raw input: time spent, errors found, and actual cost incurred.
If you are working within a company, share these metrics with your manager. They provide a factual basis for deciding whether to scale the effort. For a hypothetical test, preparation might take three hours and review another two. That result would tell you about the cost of this approach. It would not, by itself, prove the underlying problem is unimportant or that another approach could not work.
Test Demand Separately from Building
A common mistake is assuming that because you can build an AI-assisted solution, there is demand for it. Do not confuse technical feasibility with market need. After your first draft works, step back and ask: who would use this, and why? If you are an employee, would your colleagues benefit from this approach? Would it save them time or reduce errors? Test the idea informally. Ask a trusted coworker if they have faced a similar problem and whether they would try your method.
If you are a business owner, look at your existing customer base. Do they complain about the problem you are solving? Are they already paying for partial solutions? Seek permission for an appropriate customer conversation, and ask about the current work rather than pressing for praise of the proposed tool. The goal is to validate that people care about the outcome, not just that the AI can produce it. Building something nobody wants is a waste of time, regardless of how well the technology works.
Set a Stop/Revise/Continue Decision
After measuring effort, errors, and preliminary demand signals, make a clear decision point. Define three paths: stop, revise, or continue.
Stop if the problem proves too vague, the AI cannot handle it with sanitized data, or there is no interest from others. This is not failure; it is information. You have learned what does not work, which is valuable.
Revise if the draft showed promise but needs better prompts, more restricted data, or a narrower scope. Adjust one variable at a time. Maybe the prompt was too broad; tighten it. Maybe the data was too open; strip more identifiers. Run another small test before committing more time.
Continue when the evidence meets the criteria you set for the next bounded test. A person saying they would use it is a useful lead to investigate, but it is not proof of adoption or willingness to pay. This decision should be documented, even briefly. Write down why you chose the path you did, what you learned, and what the next small step looks like. This record makes the decision traceable and gives the next test a clear purpose.
Questions Before the Next Test
Does a personal project remove the permission question? A project can still involve agreements, confidential information or resources that belong to someone else. Check the applicable obligations and policies. This article is a workflow guide, not a ruling about employment or intellectual-property rights.
Does removing names make the information safe to share? Not necessarily. A description can reveal a person or business through context. Invented test data is a useful starting point. Real data requires an appropriate decision about authorization, access and the receiving tool.
What if the AI gives me something wrong? Compare the output with the source material and the task requirements. Identify whether the mistake came from an unclear request, missing information, a faulty inference or an unsupported claim. Do not assume a fluent rewrite has fixed the underlying issue. If you cannot check an important part, keep that limitation visible and do not use it as a basis for action.
How do I know whether to build a product? A working draft and an interested colleague are early signals. A product also needs a defined user, a useful result, support, permission to use the required material and a workable way to deliver it. Test those assumptions separately. An internal tool can be worthwhile without becoming a product, and a promising product idea can still be too costly to deliver.
Keep the Next Step Small Enough to Check
Suppose a hypothetical repair business receives several kinds of service request. Its owner wants an AI assistant to sort them before a human reviews them. A reasonable first test could use invented requests and predefined categories. The owner can then check whether the assistant places an ambiguous request in a review queue rather than inventing a confident answer. No customer message needs to be sent, and no appointment needs to be booked to learn from that test.
The next step depends on what happens. If the categories themselves are unclear, clarify the process before changing the model. If the assistant misses an important detail, add a check and retest. If the result adds more review work than it removes, consider an ordinary form or rule instead. Keep the customer-facing workflow unchanged until the responsible people have reviewed the evidence and agreed to a specific next step.
A polished AI checklist can still miss an important dependency. Use the checklist as something to examine, not as proof that the work is ready. The guide to AI access and human control explains why reading, drafting and acting need separate attention. If the real issue is helping a customer understand what you offer, the business discovery guide addresses that part of the process.
The useful outcome is a better-grounded decision about the work. If you are a business owner who wants to discuss a specific workflow, book a conversation with me. Bring the problem, the current process and what you need the result to do.