The Middle Ground Between Secrecy and Generous Teaching
A comment can give you an idea. It cannot tell you whether the idea fits your company. That distinction gets lost when an AI demo turns a sentence from a public thread into an impressive-looking system in minutes. The interesting work begins after the demo: checking the real job, the people affected and the facts the tool does not have.
In the accompanying video, I talk about a simple possibility: an AI system can look at comments, surface a useful idea, and help turn it into something you might build. But a tool that has your own history, capabilities and constraints should produce a different plan from one with someone else's context. I finish with a practical boundary: participate in public discussion without giving away your whole operating plan.
That phrase stuck with me because it points to something real. There's a difference between teaching principles and giving away the specific arrangements that make your work work for the people who pay you.
What a Public Comment Actually Reveals
When someone leaves a comment on a public form, they're sharing surface-level information. They might describe a problem they're experiencing, a frustration with a current service, or a wish list item. That's valuable context for understanding market needs, but it's not the same as having access to their full situation.
A public comment typically reveals: - The symptom or pain point the person is experiencing - Their general context (they're a business owner, they're looking for X type of solution) - Sometimes budget hints or timeline expectations
What it doesn't reveal is the complete picture. I don't know what systems they already have in place, what their team looks like, what their historical data shows, what compliance requirements apply to them, or what their specific constraints are. Those details live in their business, not in a comment field.
This distinction matters because AI systems that rely solely on public comments as input risk making assumptions based on incomplete information. A system that understands only what people publicly share might design solutions for the loudest voices rather than the most common needs. It might miss the quiet majority whose situations don't show up in comment threads.
How a Service-Business Owner Could Map a Workflow Privately
If you run a service business, you probably have workflows that work well for your specific clients. These aren't secret for secrecy's sake: they're private because they're tailored to individual situations.
A private workflow map might include: - The specific questions you ask during intake - How you match clients to team members based on availability and expertise - Internal pricing and service boundaries that your business has actually approved - How you explain data use and obtain any permissions your actual workflow requires - How you version updates or changes based on client feedback cycles
None of these need to be public to be valuable. They're operational details that make your service run smoothly for the people who've already chosen to work with you.
A published description rarely captures the judgment behind an operating process: which exceptions matter, which promises the team can actually keep, and where a human takes responsibility. Protect confidential information because it belongs to clients and colleagues. Keep internal procedures private when publishing them would create security or competitive problems. The point is to choose deliberately, not to pretend every detail is a trade secret.
A Fictional Example: The Missed-Call Workflow
Here's a fictional example of how someone might structure an AI-assisted workflow around missed calls, keeping public lessons separate from private implementation:
A landscaping company gets many calls after hours. The owner wants to use AI to handle basic inquiries while protecting client information.
What they might share publicly: "AI can triage after-hours calls by asking callers about their location, service needs, and preferred contact time. The system can then send a summary to the business owner's email inbox each morning."
What stays private: The specific consent language used when callers interact with the AI, the integration details with their scheduling software, the pricing structure for different call types, the employee training on what information the AI is authorized to share, and the version history of how the system has evolved based on actual caller interactions.
The public lesson is that a narrowly defined intake process could help the company organize routine inquiries. Whether that hypothetical workflow is appropriate for any actual company depends on its callers, systems, privacy duties, staffing and testing. No outcome is implied by the example.
Separating Public Lesson from Private Client Data
This separation doesn't happen automatically. It requires intentional thinking about what you share and what you keep close.
What belongs in public teaching: - General principles about how AI can process information - Patterns you've observed across multiple clients (without sharing identifying details) - The types of problems AI is well-suited to solve versus those that need human judgment - Assumptions you've tested and found valid or invalid
What should stay private: - Specific client names, companies, or identifying information - Exact pricing structures or contract terms - Proprietary assessment tools or frameworks you've developed - Internal consent forms or legal compliance language - Technical implementation details that give your system its edge - Employee training materials specific to your operation
The middle ground is asking yourself: "If I share this, does it help people understand AI's role, or does it undercut the value of working with me directly?"
Safe Prompts That Ask for Assumptions and Test Cases
When you do use AI to process public comments or other inputs, the prompts you use matter. Here are approaches that ask AI to work within boundaries rather than making claims it can't support:
"Based on these public comments about [topic], what patterns do you observe? What limitations do you see in drawing conclusions from this type of data?"
This asks AI to identify patterns while acknowledging the data's constraints. It doesn't assume the comments represent all situations.
"Generate three test cases for how AI could handle [scenario], assuming the user has [basic constraint]. For each case, note what additional information would be needed before implementation."
This creates specific scenarios while building in checks for what's missing. Each test case includes a note about what would be required next.
"Summarize these comments into categories, but flag any that mention sensitive personal information, financial details, or compliance-related data that should not be stored or processed without explicit consent."
This gets useful categorization while drawing boundaries around what shouldn't be retained or used further.
Each of these prompts keeps the human in the loop. They ask AI to organize, categorize, and identify patterns, but they stop short of letting AI make decisions about client data, pricing, or implementation.
Turn a Comment into a Testable Business Question
Suppose several people publicly ask whether a service business can respond to a simple question after office hours. It is tempting to tell an AI tool, “Build an agent that answers every call.” That jumps over the real decision. What are callers trying to accomplish? Are they asking for an appointment, a price, emergency help, or a person who can make a commitment? The same sentence may mean very different things in different businesses.
First, record the comment as a question, not as proof of demand. Note where it came from, what was actually said, and which interpretation is your own. A popular comment can be a prompt for research; it cannot tell you the size of a market or the likely return on a new system. Check customer conversations you are entitled to review, look at your current inquiry types, and ask the people who handle those inquiries where the real friction occurs. If you cannot describe the current workflow, you cannot tell whether the proposed AI step improves it.
Second, define one narrow job. “Help callers after hours” is too broad. “Collect a callback request without promising a service time” can be tested. Write the allowed input, the allowed output, the human owner, and the stop condition. A stop condition might be a safety issue, an angry caller, a request for binding pricing, or a question that depends on a client record. In those cases, the system should avoid improvising and route the matter according to the company's approved process. A tool that knows when to stop is more useful than one that always produces another fluent answer.
Third, separate what you know from what you hope. You might know how many calls arrived after hours last month, if the records are available and usable. You do not know that an AI agent will convert them, satisfy callers, or save a particular amount of labor until you test it. Write your baseline and decide in advance what would count as a better outcome. Depending on the use case, that might be fewer missed callback requests, faster human follow-up, or fewer wrong promises. It may also be a privacy failure or a new burden on staff. Measure both benefit and friction.
This approach connects to my broader point about checking assumptions before relying on AI output. The tool can organize possibilities. Your business must decide which possibility matches the actual job.
Why 1 Public Idea Produces Different Systems
Take the same hypothetical after-hours question and put it in 2 settings. A small landscaper might need an intake that asks for a service address, the general type of work and a preferred callback time. A property-management company might receive maintenance reports where urgency, tenant information and existing service arrangements matter. The outward prompt: “Could someone answer after-hours calls?”: looks similar. The permitted questions, escalation path and acceptable error are not similar at all.
For the landscaper, a first test might use a separate test number, synthetic calls and a human reading every summary before anyone contacts the caller. If the company has no dependable morning follow-up owner, the workflow is not ready, however good the AI's answers sound. For property management, an emergency process may already exist; a new AI flow must not silently replace it. The right first step may be to document the current escalation rules, not to turn on automation. These are hypothetical examples, not a claim that either system has been built or that it would perform well.
That is why your own operating history matters. A model can propose a standard call flow in seconds. It cannot infer that your receptionist checks a particular inbox only on weekdays, that 2 employees cover different territories, or that a certain kind of request needs a licensed professional. Put those facts into a private, approved brief when the tool is permitted to receive them. If you do not know a fact, mark it unknown rather than asking the model to fill the gap with confident prose.
The output should be a decision draft: a list of questions, a sample flow, a test set and a list of unresolved dependencies. It should not be treated as a completed business system. This is the same discipline I use when I ask whether a polished AI answer actually has the people, access and evidence needed to work.
A Small Test That Can Fail Safely
Before connecting a prototype to a real customer's call, test it with scenarios you control. Include easy cases, incomplete questions, conflicting details, requests for a human, and cases the system must decline or escalate. Write down the exact expected behavior for each. A pass is not “the response sounded natural.” A pass is “the system captured only the permitted details, made no unauthorized promise, identified the right next owner, and produced a record that a human could verify.”
Keep the test separate from real customer data unless the business has approved how that data may be used. If you use past interactions, decide who can access them, how they will be de-identified when appropriate, and when they will be removed. Do not paste a client's history into a convenient tool simply because it can summarize it. Where a regulated or sensitive question appears, ask the relevant professional to review the workflow rather than treating a generic AI answer as compliance advice.
Have a person inspect failures, not only successes. When the prototype guesses a service area, misses a phrase, gives an unsupported time estimate, or sends a summary to the wrong place, record the failure. Fix the process, retest that exact case, and test nearby cases that could break for the same reason. If the owner cannot explain what changed and why, the workflow is not ready to operate unattended.
Finally, run a limited pilot only after you can name the owner who will watch it and the condition that will shut it off. Compare what happened against the baseline you wrote before the pilot. If the pilot shows no clear benefit, that is useful evidence. It prevents you from confusing a polished demonstration with a working service. If it succeeds, the next expansion should still be based on the observed cases, not a claim that all future calls will go the same way. Adapting when the format or tool changes is valuable only when the underlying service remains accountable.
Human Review, Consent, Customer Communication
AI can do a lot of organizing and pattern-spotting, but it shouldn't be the final decision-maker on matters that affect people's information or experiences.
Human review means someone checks AI-organized data before it goes to clients, gets sent in communications, or used to make business decisions. This is especially important for anything involving personal information, sensitive situations, or compliance requirements.
Consent means the people whose data is being processed know what's happening and have agreed to it. If you're using AI to process public comments, have you considered whether those commenters would be comfortable knowing their input might be analyzed? Transparency here builds trust rather than eroding it.
Customer communication means telling clients when AI has been involved in their interactions. If an AI system handled their initial inquiry, they should know that before they proceed further. This isn't about hiding it: it's about honesty and giving people the choice to engage or not.
Measurement means tracking how AI-assisted processes are working and adjusting based on results. Are clients satisfied? Are you getting the outcomes you want? Are there situations where the human step should replace or supplement the AI step?
Versioning means keeping track of how your AI systems change over time. If you update prompts, integrate with new tools, or change what data the AI can access, document those changes. This helps you understand what's working and what needs adjustment.
Decisions That Need a Human Owner
There are certain areas where automation: especially AI-driven automation: should be approached with caution or avoided entirely:
- Final client communications about sensitive matters. Let a human write or at least heavily edit anything that will go directly to a client about their specific situation.
- Pricing decisions based on AI analysis of public data. Your rates should reflect your expertise, costs, and value, not what you observe others charging publicly.
- Compliance determinations. Legal requirements vary by jurisdiction, industry, and specific client situation. Don't let AI decide what your business needs to comply with.
- Hiring or employment decisions based on AI analysis of public profiles or comments. This is where bias and legal risks are highest.
- Anything that replaces direct customer conversation. AI can supplement, but it shouldn't be the only way you understand what your clients need.
A Concrete Mini-Template You Can Adapt
Here's a template you can use to think through what belongs public versus private in your own AI workflow. Fill in the blanks with your specific situation: no proprietary details required.
AI WORKFLOW BOUNDARIES TEMPLATE
PUBLIC LESSON (share this):
- What type of information is AI processing? [e.g., public comments, intake forms, survey responses]
- What patterns am I observing across multiple inputs? [describe without sharing client specifics]
- What can AI help with that frees me up for higher-value work? [be specific about time saved or tasks shifted]
- What are the limits of what this AI system can reliably do? [note constraints honestly]
PRIVATE IMPLEMENTATION (keep this close):
- How do I handle consent before processing any information? [specific language I use with clients/ customers]
- What data am I storing, and how long do I keep it? [compliance-specific details]
- What human review steps are built into my process? [who checks what, and when]
- What's my pricing structure for different service levels? [based on my costs and value, not public rates]
- What version of this system am I running, and how do I track changes? [basic tracking without sharing trade secrets]
SAFE PROMPTS I USE:
- [List 2-3 prompts that ask AI to organize or categorize while acknowledging limitations]
- [Prompts that include "assuming..." or "if... then..." conditions rather than absolute claims]
HUMAN CHECKPOINTS:
- [Specific moments where a human reviews before AI output goes further]
- [Clients I communicate with about AI involvement and how I do it]
Fill this out for your business. The act of writing it down forces the separation that makes responsible sharing possible.
FAQ
Can I use AI to analyze public comments about my industry? Yes, but treat the results as patterns, not predictions. Look for common themes, pain points, and wish list items. Don't assume what you find represents all potential clients or that it should drive major business decisions without further research.
Should I tell my clients when I'm using AI? Transparency is a choice you make based on your industry, client expectations, and the specific ways AI is involved. Some builders mention it in their intake process. Others wait until a situation arises where AI played a role. There's no universal requirement, but honesty when it matters to the client's experience is generally wise.
What if a public comment contains sensitive information I didn't intend to collect? If you're processing public comments, have a plan for what happens when something unexpected shows up. This might mean immediately deleting that input, not storing it beyond the immediate analysis purpose, or having a process for flagging and reviewing unusual submissions. The key is having a plan before it happens, not discovering you need one in the moment.
How do I know if my AI workflow is working well? Track what you set out to accomplish when you started using AI for this purpose. Are you getting more of the type of work you want to do? Are clients reporting positive experiences at touchpoints where AI was involved? Are there situations where things went wrong that you've learned from? Regular review: monthly or quarterly, depending on volume: helps you adjust rather than keep running something that's not serving its purpose.
Can sharing my workflow publicly hurt my business? It can expose confidential information, security-sensitive procedures or details you meant to keep inside the company. But teaching a useful public principle can also help people evaluate your work. Before posting, decide what the reader needs in order to learn, then remove what you do not have the right or reason to disclose.
What's the difference between teaching publicly and giving away my playbook? Public teaching shows the problem, the method at a useful level and the limits. An internal playbook specifies the actual permissions, owners, integrations, exceptions and quality controls. Some organizations publish a great deal of that detail; others cannot. Make a conscious choice for your business and for the people whose information it touches.
The Middle Ground, Practically
That last line in the video: comment, but do not give up your whole game plan: is not a command to be secretive. It is a reminder to bring your own evidence to a public idea and decide what belongs in public.
The useful outcome is a test you can describe plainly: what question started it, what the business already knew, what remained unknown, who approved the pilot, how failures were handled and what the results actually showed. If you cannot answer those questions yet, keep the prototype in review. A fluent AI response is not a receipt for a working business process.
If you want to map a specific workflow in your business, start a conversation with me at Book With Honor. Bring the real task, the current process and the constraints. We can decide what a responsible first test would be without promising a result before the test exists.