Experience Still Beats AI
Automation Isn’t the Hard Part. Deciding What to Automate Is.
AI can generate text, code, summaries, and explanations of widely known problems.
That works when the problem is common, documented, and already solved many times before.
But a large part of real automation, scraping, and monitoring work does not look like that.
Many use cases are niche.
Many are new combinations of signals and constraints.
Some are so specific that there is nothing written about them anywhere.
In those cases, there is nothing for an AI model to reference.
What’s needed then is not generation, but judgment.
You have to decide how to frame the problem, what data actually matters, what can be used as a proxy, and what trade-offs are acceptable. You often need a new way of looking at the problem, not a better explanation of an existing one.
That is where experience still beats AI.
Automation problems are decision problems
Automation is often presented as mechanical work.
Define the task. Run the workflow. Collect the data.
In practice, most of the effort happens before anything runs.
What should be scraped and what should be ignored?
What should be monitored continuously versus checked occasionally?
Which changes matter, and which ones are noise?
These are not technical questions. They are decision questions.
And they are where most time is lost.
Where AI helps, and where it stops
AI is useful once direction exists.
What it cannot reliably do is decide:
which data is worth collecting long term
which signal is stable enough to monitor
when a workaround creates more risk than value
when a different approach would produce a better outcome
Those decisions depend on context, constraints, and experience with similar failures in the past.
The real cost is trial and error
Most automation failures don’t look like failures.
They look like systems that almost work.
You collect data that looks correct but turns out incomplete.
You monitor changes that are technically accurate but operationally useless.
You spend days tuning something that should have been designed differently from the start.
The cost is not credits or compute.
It’s time and attention.
Scraping and monitoring are creative disciplines
Good automation is not about copying what is visible.
It’s about deciding:
what to observe instead of what to extract
which indirect signal is more reliable than the obvious one
how to design systems that survive change
This is why two people using the same tools can end up with very different results.
One builds something fragile.
The other builds something that lasts.
The difference is not the tool.
It’s how the problem was framed.
Outcome-first thinking matters more than tools
A common mistake is forcing a solution through a specific tool.
That usually leads to fragile setups and wasted time.
A better approach is outcome-first:
What are you actually trying to achieve?
What decision will this data support?
What is realistically achievable given technical and legal limits?
Sometimes the right answer involves Hexomatic or Hexowatch.
Sometimes it involves combining tools or approaches.
Sometimes the honest answer is that something is not achievable in a reliable way.
Being direct about that saves time.
Why we’re formalizing this into support plans
As more customers use Hexomatic and Hexowatch for serious automation and monitoring work, one pattern became clear.
Many problems don’t need more features.
They need better decisions earlier.
To help customers move faster and avoid unnecessary trial and error, we’re formalizing this experience into optional support plans focused on implementation and problem solving.
These plans are not required to use the tools. They exist for cases where reliability, clarity, and outcomes matter.
Advanced Support
Designed for customers who want expert guidance and a faster path to a working setup.
Includes:
Email-based support
Help designing scraping workflows and monitoring strategies
Assistance setting up and optimizing workflows and monitors
Guidance when standard approaches are not sufficient
Quarterly video strategy review calls
Two custom scraping or monitoring templates included with annual plans
Advanced Support is meant to reduce rebuilding and help converge on the right approach sooner.
👉 Review plan details and subscribe:
Dedicated Support
Designed for teams where automation and monitoring are a core part of daily operations.
Includes:
A dedicated account manager
Monthly video review calls
Hands-on help with complex workflows and advanced monitoring logic
Custom approaches for difficult or non-standard cases
One custom scraping or monitoring template per month
Twelve templates included with annual plans
Dedicated Support is for cases where automation is infrastructure, not experimentation.
👉 Review plan details and subscribe:
Boundaries matter
Some data sources impose strong technical or legal restrictions.
Some signals are unreliable by design.
In those cases, we will be direct.
We can assess feasibility, suggest alternative approaches, or recommend a different strategy, but we will not promise what cannot be delivered reliably.
AI accelerates execution.
Automation scales output.
But deciding what to automate, what to monitor, and how to interpret the data is still a human problem.
That hasn’t changed.