As someone who’s been in the AI automation game for over a year now, I’ve seen the landscape evolve rapidly. When I first started, AI automations were a new frontier. We were pioneers, figuring things out as we went along. Now, they’re everywhere – but many people are still stuck using outdated methods.
I’ve learned a lot in the past 12 months. My team and I have sold AI automation templates to over 600 customers and built the Big Moves AI Automation Marketplace. We’ve gone from complex setups that took hours to streamlined systems that can be up and running in minutes. Yet, I see many people still struggling with the same issues we faced a year ago.
It’s time for a change. In this article, I’m going to share nine advanced AI automation tips that will revolutionize the way you build and use these systems. Whether you’re looking to create more efficient workflows for your business or planning to sell templates on our marketplace, these insights will be invaluable.
1. Focus on Systems, Not Just Automations
The first and most crucial tip is to shift your focus from individual automations to comprehensive systems. Many online communities and tutorials offer blueprints for single automations. While these can look impressive, they often fall short in practical application.
Here’s the truth: businesses scale systems, not isolated workflows. Without a systemic approach, you risk creating more problems than you solve. Manual tasks might even end up being faster than poorly implemented automations.
In my experience, thinking in terms of full systems yields far better results. For instance, our content automation system doesn’t just handle one aspect of content creation. It encompasses a six-step process that covers everything from initial planning to final distribution. This holistic approach allows us to leverage AI more effectively, running through multiple prompts in sequence to generate comprehensive outputs.
2. Streamline Your Blueprint Imports
If you’re using platforms like make.com to build your automations, you’ve likely encountered the tedious process of remapping modules when importing blueprints. This used to be a major time sink, taking hours to set up a single template.
We’ve found a better way. By using API calls instead of pre-built modules, we can now push in the necessary IDs from our base directly. This eliminates the need for manual remapping, dramatically reducing setup time.
This approach is particularly valuable if you’re an AI automation agency owner. You can now install your base template into client accounts much more efficiently. While it requires a bit of JSON knowledge, the time savings are well worth the learning curve.
3. Build from a Solid Base
Having a robust foundation to build your automations from is crucial. I use Airtable, but platforms like SmartSuite or even Notion can work well too. The key is to have a central hub where you can control various aspects of your automations.
One of the most valuable elements we’ve incorporated is an AI model table. This allows us to easily update and reference different AI models across our automations. Instead of manually updating each automation when a new model comes out, we can make changes in one place that propagate throughout our system.
This approach also allows us to leverage the strengths of different AI models for specific tasks. For instance, we use Claude for copywriting but prefer OpenAI models for project planning due to their superior reasoning capabilities.
4. Centralize Your Brand Assets
Consistency is key in branding, and this extends to your AI outputs. We’ve created a brand assets tab in our base that includes crucial information like brand voice guidelines, excluded words, and customer avatar details.
By using Airtable’s Linked Records feature, we can pull this information into any workflow that needs it. This ensures that every AI-generated piece of content aligns with our brand identity, without the need to manually input this information each time.
5. Embrace Multi-Model Platforms
Many people still rely on individual AI modules for platforms like ChatGPT or Claude. However, there are now platforms like OpenRouter that allow access to multiple AI models through a single interface.
While there’s a small additional cost (about 7% in OpenRouter’s case), the flexibility and time savings more than make up for it. You can easily switch between models based on the task at hand, without needing to set up and manage multiple AI service accounts.
6. Create a Comprehensive Prompt Library
A well-organized prompt library can significantly streamline your workflows. We’ve built ours to include not just the prompts themselves, but also output variations and examples of good outputs.
This approach creates a feedback loop that helps the AI understand what we’re looking for, resulting in more on-brand and higher quality outputs over time. It’s a key factor in producing AI-generated content that doesn’t look like typical, generic AI output.
7. Consolidate Your Workflows
We’ve managed to dramatically reduce the complexity of our systems while increasing their capabilities. Our latest version of Content Automation OS uses just 5 tables and 5 make.com scenarios, down from dozens in our previous version.
This consolidation was inspired by Elon Musk’s approach to manufacturing: remove as much as possible until the system breaks, then add back only what’s necessary. By pushing our systems to their limits, we’ve created more efficient, easier-to-manage workflows.
8. Leverage Filtering in Your Interfaces
When working with large databases of prompts or other information, effective filtering is crucial. We use Airtable’s filtering capabilities to show only relevant information in each part of our workflow.
For example, in our podcast workflow, we filter our prompt library to show only podcast-related prompts. This keeps interfaces clean and user-friendly, which is especially important when training team members or customers to use these systems.
9. Stick with Your Platforms
While it can be tempting to constantly switch between platforms looking for better deals or features, this often costs more in time than it saves in money. Unless there’s a clear, significant benefit to switching, it’s usually better to stick with the platforms you know and have already set up.
Remember, you pay either in money or in time. For a growing business, time is often the more valuable resource. Spending hours migrating systems to save a few dollars a month is rarely a good trade-off.
Instead, focus your energy on marketing and sales – that’s what will truly grow your business. As one of my mentors once told me, “If you’re not making the amount of money you want to make, you should be spending 80% of your time on marketing and sales.” This advice transformed my business from barely six figures to seven figures.
Conclusion
AI automation is a powerful tool, but like any tool, its effectiveness depends on how you use it. By focusing on systems rather than isolated automations, streamlining your processes, and leveraging the full capabilities of your chosen platforms, you can create AI workflows that truly transform your business.
Remember, the goal isn’t just to automate for the sake of automation. It’s to create systems that enhance your productivity, improve your output quality, and ultimately drive your business forward. With these advanced tips, you’re well on your way to achieving that goal.
Frequently Asked Questions
Q: How long does it typically take to set up an AI automation system?
The setup time can vary greatly depending on the complexity of the system. With our latest optimizations, we’ve managed to reduce setup times from several hours to under 10 minutes for some templates. However, more complex systems may still require more time to set up properly.
Q: Is it necessary to have coding skills to implement these advanced AI automation tips?
While some basic understanding of concepts like JSON can be helpful, most of these tips don’t require extensive coding skills. Many automation platforms provide user-friendly interfaces that allow you to implement these strategies without deep technical knowledge.
Q: How often should I update my AI models in my automation workflows?
It’s a good practice to review and update your AI models every few months or whenever a significant new model is released. However, by using a centralized AI model table as suggested in the article, you can make these updates quickly and easily across all your workflows.
Q: Can these AI automation techniques be applied to any business or industry?
While the specific applications may vary, the principles behind these techniques can be applied to virtually any business or industry that deals with information processing, content creation, or workflow management. The key is to adapt these strategies to your specific needs and processes.
