Automation Nation
The rise of one-person powerhouses, companies beyond solopreneurs and personal brands
It’s happening all around us.
From cronjobs to Clickup events, edge workers, Zapier, Make and n8n workflows. Our world is automatedly evolving. We’re witnessing the emergence of “micro-enterprises” – one-person businesses and startups leveraging automation to punch far above their weight. These agile operators are harnessing a suite of tools and platforms to handle 70-90% of their daily operations.
An astute automator could essentially leverage:
– Make.com to schedule posts, automate social growth engagement, and AI-assisted content curation in as little as under 20-steps workflow
– Clickup.com and Gohighlevel.com for administrative smart document management and optimize AI-driven prospect identification and automated outreach campaigns for lead generation
– Manychat.com for programmatic, automated content distribution and social outreach DM campaigns and lead generation/ email collection
– Zapier.com to connect seamless e-commerce integrations and automate order processing for order fulfilment
And these are just some tools and use-cases from the many available options out there. Of course, there can’t be results without any effort. But the secret to success lies in what I might like to call “Interdependent Semi-Automation” (ISA). This approach recognizes that while many tasks can be fully automated, the most impactful elements still require human insight, creativity, and decision-making.
Technical Workflows vs. Data Conversant Workflows
Understanding the difference between purely technical workflows and more dynamic, input-focused processes paves the way for greater, precise and optimal automation.
Technical/Programmatic Workflows
Technical or Programmatic Workflows refer to structures that are repetitive by nature, and fits a large-scale I/O model. One such example would be programmatic SEO, where brands like Zapier have garnered over 6.3M site visits with high-performance keyword targeting. All it takes is a CMS Blogsite, APIs and a simple spreadsheet, and you’re on your way to creating thousands of keyword-targeted landing pages to up your SEO game.
Another example would be Python scripts that scrapes specific Subreddits, and pieces together a script backed with Minecraft gameplays to churn vertical videos under 5-minute duration, specifically for Youtube Shorts, IG Reels or as Tiktok videos.
Such workflows focus on playing the numbers game, where quantity is put to the test to cast the widest net possible, in order to increase clicks, views and ultimately, conversions.
Focus: Efficiency, consistency, scalability
Examples: Automated testing, deployment pipelines, data processing
Best for: Repeatable, well-defined processes
Fully Automated Content Generation
Like the Python script example given above, such automated content provide little to no value, and would only serve to help viewers identify your content as automated and inorganic. It may be good for short-term view counts, but is not very beneficial in the long run, especially when it comes to the platform’s algorithm.
While the pros like rapid production, scalable and being low-cost may seem very favourable, the cons of being too generic and severely lacking authenticity far outweighs what your audience may overlook.
Data Conversant Workflows
On the other end of the automation workflow spectrum, we have the Data Conversant Workflow, that acts as a conversational, intuitive assistant that takes a user’s input and other relevant data, and enhances the output quality incrementally.
Focus: Creativity, adaptability, brand voice
Examples: Semi-automated blog post creation, AI-assisted video editing with human direction
Best for: Content or workflows that requires a personal touch, storytelling, brand-building
While Artificial Intelligence systems are breaking past the point of 'self-learning', there is still a large portion of generative AI that requires the essential human touch of sentiments that no amount of NLP or ML can fully replicate.
Human-In-The-(Feedback)-Loop (HITL)
We are definitely ways away from fully agentic AI automation, especially for tasks and projects that require human input. The results derived from our automations are only as good as the prompts, and there is absolutely no 'single-shot' prompt OR Large Language Model that is capable of getting things done perfectly.
AI automation experts are fast discovering that the generated output from all our GEN AI tools are just not good enough to pull the wool over any adequate reader. Sure, programming IDEs can put together a slipshod vibe-coded AI-wrapper that looks and feels decent, until vulnerabilities are uncovered.
But for day-to-day content generation, captions, even videos, there has to be that higher level of pre-generation refinement, with ample feedback and finetuning.
As data intensive and technical as it sounds, Data Conversant workflows refer to a more ‘interpersonal’ structure of back-and-forth exchange of user input and context referencing between generative intelligent language models, all in a bid to create the best and most relevant data output.
The Semi-Automated Edge: Beyond Cookie-Cutter Generation
The distinction between fully automated and semi-automated approaches is becoming increasingly obvious. Our viewers can now distinguish ChatGPT-generated content very easily, and it takes more thought-work to provide actual value and insights to keep your audience hooked and scrolling.
Interdependent Semi-Automated Generation
ISAG leverages automation for the foundational elements of data processing and generation – scope aggregation, context-optimizing and concept-refinement – while reserving key creative decisions and human input for a higher competitive edge. This approach results in high quality control that’s both efficient to produce and remains genuinely engaging.
An example could be an N8N workflow that uses AI to generate numerous variants of a Fireworks display, for a social media page that features videos of fireworks on a daily basis. While the baseline concept remains strict to a unique and identifiable niche, what makes this process stand out is the part of the semi-automation workflow that takes the user’s input of the type of firework, vibrancy, colours, spark variance, speed and duration to generate an entirely different video content every single time.
The N8N workflow would then send the final video render directly to Slack or Discord, and prompt the user for an accompanying caption that fits the nature and timeliness of the post, before being sent via API to the respective social media platform for scheduling or immediate posting.
The entire process of this workflow? Less than 3 actual minutes.
The entire duration spent by the user? 30 seconds for fireworks descriptive setting, 1 minute to type out the actual caption.
The edge given by implementing such Data Conversant workflows allows for customizable content via highly propitious variables; a daily schedule prompt that, if not replied to, will re-prompt the user once more a few hours later, and if still ignored, be brought over to the next day, while not messing up any step of the workflow.
This adaptability goes beyond erroneous edge cases, and does not create additional backlogs of tasks that would otherwise require more time from the user resolve, eg. having to manually delete posts that was generated and posted fully automated that goes beyond a platform’s guideline, containing offensive material or content etc.
This example is just one of the many benefits of Data Conversant Workflows, which places a high emphasis on quality control, refined output and an added ‘human touch’.
– Pros: Unique voice, authentic experiences, higher engagement
– Cons: More time-intensive, requires ongoing human input
As mass adoption of AI and automation tools becomes the norm, productivity levels will out-scale the need for multiple overheads and allow most of us to handle day-to-day tasks hyper-efficiently. More one-man startups and solopreneurs will drive us to heavily focus on precision content curation and marketability matching, aka personal branding.
Shifting from being just satisfied with mass content generation to craving authenticity, we can see a change of pace by brand content creators that transition from just being a part of the ‘Creator Economy’ to the ‘Human Touch Economy’, supporting the value of distinctly human skills like empathy, creativity, and complex problem-solving.
