Gerhard G. StocKinger · Published on November 2, 2025
AI Automation Put to the Test: What the Remote Lab Index Reveals
A strong demo does not necessarily mean a stable business process.
The idea of fully autonomous AI work is appealing. However, recent practical tests involving real freelance tasks paint a more sobering picture: Even high-performing agents can only consistently complete a small portion of complex tasks correctly.
Why real work is harder than a benchmark prompt
Business tasks consist of dependencies, incomplete information, tool changes, and quality decisions. An agent may be highly proficient at individual steps and still fail in the overall process.
Getting Started the Right Way
- Break the process down into clearly verifiable subtasks.
- Measure baseline values for time, quality, and error rate.
- Automate low-risk steps first.
- Provide for human oversight at critical junctions.
- Grant more autonomy only after consistent results have been achieved.
That's not an argument against agents. It's an argument for professional process design. Companies shouldn't ask whether AI will replace a job, but rather which steps it can reliably improve today.
Conclusion: Successful automation stems from measurement, limits, and learning loops—not from maximum autonomy on day one.
This post is based on my LinkedIn post from November 2, 2025, and has been expanded for the blog.