In our last post, we argued that the era of complex, configurable AI platforms is ending. The reason is simple: users want results, not tools. They want the nail hammered in, not a shinier, more complicated hammer. This observation led us down a path of asking a more fundamental question, one that starts from first principles: what is a "workflow" anyway?
The common answer is that it's a series of tasks, a sequence of steps you build. We think that’s the wrong way to look at it.
A workflow isn't a process you build; it's a problem you solve. And once a problem is solved, it should stay solved.
The Job of the Tool
If a workflow is a solved problem, then the job of a workflow tool should be to help you solve it and remember the solution. But that’s not what’s happening. Today's tools often get in the way, forcing users to become system architects when all they want is an answer.
We saw this clearly when we studied how people use existing workflow products. We found two distinct camps of frustration.
For non-technical users, the barrier to entry is immense. They come with a simple business need, like "I want to track my competitor's prices," but are immediately confronted with a wall of abstract concepts. They're forced to learn a specialized vocabulary of "nodes," "triggers," and "workflows" just to begin. The user interface, often a complex canvas of drag-and-drop options, feels less like a solution and more like a test. When an error occurs, the technical messages are cryptic, leaving them unsure of how to fix it. The promise of "low-code" feels like a bait-and-switch; it’s still too complex.
For technical users, the initial hurdles are lower, but they soon hit a different kind of wall: the performance ceiling. They find that running complex tasks or processing large amounts of data makes the system slow to a crawl. The pre-built nodes and connectors, while convenient, are often rigid. If a specific function they need isn't offered, or they need to connect to a service without a ready-made plugin, they’re stuck. Debugging is a nightmare; when a workflow fails, pinpointing the exact source of the problem is a frustrating exercise in trial and error.
Both users, despite their different skill levels, are spending their time fighting the tool instead of solving their problem. The tool has failed its primary job.
Why Don't We Just Tell the Computer What We Want?
This is the question that cuts through the complexity. If the goal is to get from a business need to a business outcome, why do we tolerate so many intermediate steps? Why can’t we just state our intent and have the system figure it out?
This isn’t a futuristic dream; it’s a logical expectation for modern software. The burden of translation, from human intent to machine execution, should be on the machine, not the human.
This simple idea became the foundation for how we designed Maybe AI.
An Answer in Three Parts: Understand, Act, Remember
We decided to build a platform based on this principle. Instead of giving you a workbench and a box of parts, we're building an expert who listens to you. The process is based on three simple, powerful concepts.
- Understand: It starts with moving from function recognition to business understanding. A user shouldn’t have to know they "need a crawler". They should be able to state their business goal in natural language: "Find AI startups that recently received Series A funding and analyze their investment value". Our system is designed to understand that intent and the business logic behind it.
- **Act: **Once your intent is understood, the system autonomously handles the entire "acquire → analyze → act" business cycle. Our intelligent planning engine determines the best strategy, selects the right tools (like the Maybe Crawler for data acquisition), and executes the plan. It works on its own to get the job done.
- **Remember: **This is the most important part. When the system successfully completes a task, it doesn't just deliver the result and forget about it. Our Workflow Solidification Mechanism automatically saves that successful execution path as a reusable, intelligent solution. When you asked to monitor competitor prices, the system doesn’t just do it once. It prompts you: "This monitoring was very successful! Would you like to save it as your dedicated 'Competitor Price Monitoring Solution'?".
This is the key. You are no longer just using a tool; you are owning a solution.
The Compounding Interest of a Solved Problem
When a successful workflow is solidified into a personal "solution," something magical happens. The next time you need it, the execution time is dramatically reduced. More importantly, the solution evolves. With each use, it learns your preferences and gets smarter.
Soon, you don't have just one solved problem; you have a library of them. These solutions can then work together to handle even more complex requests. Your "market trend solution" can feed data to your "marketing content solution," creating a network of intelligent assistants that understand you and your business.
This is the compounding interest of a solved problem. The value of the system grows with every interaction.
The measure of success is no longer, "I learned to use a complex tool." It is, "I own a set of intelligent solutions that understand my business". That is the future we are building.
About Maybe AI
Maybe AI is a business data workflow automation platform that lets prosumers describe their data needs in natural language and automatically handles the complete "acquire → analyze → act" business cycle, with intelligent solutions that learn and evolve with each use.
Website: https://maybe.ai/
X: https://x.com/hey_maybe_ai
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