AI Tools Instead of Just Online Courses: How to Productize Your Expertise

Creators, consultants and other knowledge providers can now sell their expertise not only as a course, coaching or template, but also as an interactive AI tool. Instead of merely explaining to users how a process works, part of your own method can be translated into a repeatable workflow that works with individual inputs and produces a concrete result.

A practical guide from Social Media Examiner describes this model using messaging, PR and visibility tools. The interesting point is less a particular provider than the shift from information product to executable process.

The Idea Isn't New – Authority Hacker Already Uses It Operationally

This development is not entirely new, however. Months before the Social Media Examiner article, Gael Breton and Mark Webster of Authority Hacker documented how they translate internal knowledge and recurring business processes into executable AI systems.

In the podcast episode „5 Boring AI Automations Making Us 7 Figures“ from 10 June 2026, they describe, among other things, a round-the-clock support bot running on a cheap server, automated onboarding and a database meant to identify members at risk of cancelling. An earlier post of their own also cites, for community management, the comparison that 20 answers used to take around two hours and would now take about 15 minutes with their Claude Code workflow. These are the company's own figures, not an independent productivity measurement.

The interesting thing is less the specific tool choice than the pattern behind it: the knowledge doesn't stay only in course videos or documents, but is translated into an executable workflow. Authority Hacker now markets this approach as a product itself. The current AI Accelerator page offers, among other things, skills, a shared workspace and a member MCP, through which ChatGPT or Claude can search lessons, transcripts and community threads.

Authority Hacker also takes a pragmatic approach to the technical implementation. In a public post, the company argues that for large tool suites such as Google Drive, Docs and Sheets, CLI access is often more efficient than a large MCP server, because only the commands needed for the particular task have to be loaded. This fits the general trend away from the single prompt towards agents that access existing knowledge sources and business systems.

"Productizing expertise with AI" should therefore be understood less as a completely new product category. What is new is mainly how low the technical barrier to entry has become and how many platforms now support this pattern directly.

Knowledge Only Becomes a Tool Once the Process Is Clear

A course can be built relatively openly. An AI tool, by contrast, needs a clearer structure: what does the user enter, which rules are applied to that information, and what result should come out at the end?

Social Media Examiner summarises this as input, process, output. For marketing or creator products, this could look like the following:

Input: target audience, offer, existing texts or customer data
Process: the expert's documented method, including criteria, examples and decision rules
Output: messaging document, content plan, pitch, audit or prioritised recommendations for action

The real value thus lies not in the chat window, but in the process logic behind the chat.

This resembles the development of internal AI workflows. Our guide to the Claude workflow as AI Creative Director likewise covers turning a good single prompt into a documented and repeatable process.

Three Technical Levels – with Very Different Consequences

How much infrastructure is needed depends on what you want to sell.

1. An Existing Assistant or Skill

The simplest variant is a preconfigured assistant or skill within an existing AI platform. Anthropic describes skills as folders of instructions, scripts and resources that Claude loads dynamically when a task calls for them.

This suits cases where the actual offering consists mainly of a method, examples and defined work steps. The drawback: access, distribution and product logic depend heavily on the respective platform.

The framework conditions for ChatGPT have also 2026 changed. New custom GPTs can currently no longer be created or published on personal Free, Go, Plus and Pro accounts; creation remains available mainly in managed Business, Enterprise and Edu workspaces. Existing GPTs can still be used. For a new creator product, you should therefore not automatically assume that a public custom GPT is still the simplest distribution route.

2. Your Own App in ChatGPT

With the Apps SDK, OpenAI now offers a technically considerably stronger route. Developers can use it to design the logic and interface of an app, connect an existing backend and test the application within ChatGPT. The SDK is based on MCP, the open standard for connecting AI systems with external tools and data.

This turns a single prompt into a real product: users can, over time, sign in, use premium features and work with data or functions from your own backend.

Important for the business model: OpenAI already accepts app submissions, but for direct monetisation within ChatGPT it does not yet provide finished details. Support for the Agentic Commerce Protocol is planned. Anyone building a paid product today therefore still needs their own logic for access, billing or membership, rather than relying on a ready-made ChatGPT app store revenue channel.

We explain how such custom backends and permissions work in more detail in the Srocket guide to ChatGPT apps, MCP, OAuth and your own servers.

3. Standalone Software

The third level is a completely separate application with login, user management, billing and segregated customer data.

This gives creator businesses the greatest control: access can be revoked, different plans can be offered, and sensitive data does not have to be distributed via shared prompt or skill files.

However, the business also changes. Anyone selling their own app is no longer running just an information product, but at least partly a software product. Topics such as data protection, multi-tenancy, support, cost control, error handling and ongoing maintenance thus become part of the offering.

The advice "just vibe-code it and sell it" therefore falls short. A working prototype is not the same as a robust customer product.

The Most Interesting Creator Use Case Is the Implementation Gap

AI tools are particularly interesting where customers already know in theory what they should do, but regularly get stuck on implementation.

Examples include:

creating a messaging document from an interview,
deriving a content plan from existing brand information,
translating PR goals into suitable research and pitch steps,
checking weekly marketing activities against a fixed evaluation model,
or drafting a first strategic outline from customer responses.

Here the tool does not automatically replace consulting. But it can take over the repeatable part between two consulting sessions.

This is also interesting for creator business models, because it creates a new product between static course and personal 1:1 service: standardised methodology, but individual inputs and results.

An AI Tool Doesn't Automatically Sell Expertise

The most important counterpoint: a chatbot with a few prompts is not yet a defensible product.

The easier a method is to copy, the weaker the protection provided by the technology itself. Lasting value is more likely to come from good data, clear processes, continuous updating, distribution, brand and, where appropriate, human support.

Social Media Examiner also points to this trade-off: if you deliver your methodology as a transferable skill, you potentially hand part of your own intellectual property directly to customers. A dedicated app can keep this logic better hidden behind a backend, but costs more in development and operation.

For creators, the decisive question is therefore not "Can I build a bot from this?" but: which part of my offering becomes more valuable to customers once it becomes immediately executable?

Test Internally First, Then Sell

A sensible approach is to build the workflow first for your own work. If a tool regularly saves you time internally and reliably handles different inputs, you can better judge whether it can become a customer product.

Authority Hacker illustrates this path quite well: according to their public account, the systems they describe were first used in their own business and only then packaged as skills, courses, workflows and member access. This is probably the more sensible order than the reverse – first build a robust internal workflow, and only then decide which parts actually lend themselves to a product.

At the same time, it is not enough for only the best test case to work. AI outputs remain variable. Different users enter incomplete, contradictory or unusual information. A tool that can be sold therefore needs more guardrails than a personal prompt.

In marketing and creator workflows in particular, human quality control also remains sensible. AI can speed up structure, research and first implementations; claims, brand positioning and sensitive decisions should not pass through unchecked.

What Creators and Marketers Should Take Away

AI does not automatically turn expertise into software. But it lowers the barrier to offering repeatable methods as interactive products.

The best starting point is processes that recur frequently, have clear inputs and produce a clearly useful result. For an initial test, a skill or internal assistant may be enough. As soon as access control, customer data, billing and ongoing product maintenance become important, the path leads more towards a dedicated app or your own backend.

This creates a new product category between course, consulting and SaaS – but not a completely unploughed greenfield idea. More interesting is that workflows which only a few months ago were still assembled with CLI, scripts and custom servers are increasingly appearing as finished platform features and creator products.