This isn't an article that tries to sell you something or give you a "standard answer."
We've simply summarized our latest research on what people are using workflows and automation for, and where they get stuck. We are presenting only the common patterns we observed. Now, we invite you to add your own experiences.
Six Common Scenarios
1. Marketing & Acquisition
Includes multi-channel content distribution, lead collection and outreach, social media marketing, personalized email campaigns, marketing data analysis with ROI tracking, and ad optimization.
2. Sales & Conversion
Includes lead assignment and follow-up, quotes and contracts, automated customer communication, performance monitoring and reporting, and churn prediction.
3. Internal Operations & Support
Includes internal process and document collaboration, financial and report processing, HR procedures, project and task assignment, and knowledge base updates.
4. Customer Service
Includes unified management of multi-channel support, ticket classification and routing, feedback collection and analysis, service quality monitoring, and satisfaction tracking.
5. Product Management
Includes requirement gathering and analysis, release processes, user behavior analysis, competitor monitoring, and managing the product roadmap and feedback loop.
6. Technical Development
Includes CI/CD pipelines, code quality and system monitoring, data backup and recovery, API integration and testing, and environment management.
Cross-Scenario Pain Points
1. Scattered Data, Fragmented Processes
Content, leads, customer data, and product information are dispersed across different systems. There is often a lack of a unified view or standardized processes.
2. Repetitive Labor, Inconsistent Standards
Manually transferring data between platforms, updating spreadsheets, and deploying code is time-consuming and prone to error.
3. Difficult to Quantify Results
It's hard to create a closed loop for marketing ROI, conversion paths, and quality control. Outcomes often depend on individual experience.
4. Gaps in Collaboration and Timeliness
Cross-departmental communication is slow. Alerts and responses are often delayed, leading to missed opportunities.
Barriers for Different User Groups
For Non-Technical Users:
The learning curve is steep (abstract concepts like "nodes" and "workflows"), documentation is often too technical, debugging is difficult, configuring OAuth and APIs is complex, and pricing models can be hard to understand.
For Technical Users:
Concerns include performance and scalability with large data volumes, the scope of capabilities for individual nodes, difficulties in error-tracing and debugging, and the stability of third-party API connections.
A Few Questions for Discussion
- What is the main automation scenario you use most frequently? Why that one specifically?
- If you could only solve one pain point first, which would you choose: unifying data, standardizing processes, quantifying ROI, or improving cross-departmental collaboration?
- When non-technical team members get involved in automation, where do they struggle most? What kind of visualization, paradigm, or template would lower the barrier for them?
- As a technical user, do you care more about stability and observability, or speed and feature coverage? Why?
- Do you have a simple but stable automation that has proven effective, which you would be willing to share?
We plan to compile the best examples and questions from the comments into a practical guide on implementing automation in a future post.
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.
Data workflows, minus the work.
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