Why Human-Centered Design Matters in Automation
Automation that removes a person from a loop tends to be slower and worse than automation that removes the typing, because the person was doing two jobs: the work and the noticing. You can automate the first fairly easily. The second one you have to design in, or it disappears.
The second job nobody writes down
Job descriptions list the first job. The second is invisible until it is missing: someone in the flow notices that Tuesdays are strange, that one customer keeps coming back, that the numbers moved slightly out of proportion. Automation removes the person who was standing in the stream, and with them the noticing, because noticing was never the thing they were asked to do.
This is why my manifesto line is that the best integration is the one you do not see. Not because invisibility is elegant, but because an integration that makes itself obvious to the person still standing in the flow is the only way to keep the noticing inside the system once the machine takes the typing.
Design for the person who stays
There is a version of automation where the human becomes a rubber stamp — presented with a conclusion and a button. That version degrades quietly, because a rubber stamp does not accumulate understanding, so six months later nobody can explain the system, and the person who could has left. The other version keeps the human deciding things that require judgement, and shows them what the machine looked at while it prepared the decision.
The difference shows up in the escalation rules. Ask an automation project who is allowed to overrule it and what they can see when they do. If the answer is that the operator can see the transcript but not the sources, you have built a rubber stamp and called it a control.
Filbert Henrico is a Technical Customer Success Lead at FINETIKS in Tangerang since June 2025, with seven-plus years of customer success and technical sales across Southeast Asian fintech, including Xendit, Peakflo, and Brick. His focus includes API integrations, agentic AI, workflow automation, conversational AI, UAT, and launch readiness. Bachelor of Marketing, Victoria University, Bandar Sunway, Malaysia, 2016–2019.
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These pages describe how I approach the work, not a fixed methodology. If you are scoping something similar and want a second opinion on the trade-offs, send me the problem in the messy state it is actually in.
Keep reading
All essaysWhich AI use case should a team build first?
Build the one where a human already does the task every day, the correct answer is easy to recognise, and a wrong answer is cheap to undo. Those three conditions are what let you finish the project: the workflow gives you data, the recognisable answer gives you a test, and the cheap undo gives you permission to ship.
How do you automate customer support without breaking escalation?
Map every escalation rule a human currently applies, and automate only the work that sits above the rules. Escalation is not a feature you add to an automated flow — it is the boundary of the flow, so the boundary has to exist and be written down before anything is automated. Teams that automate first and define escalation later end up with a system that handles everything confidently, including the things it must not touch.
What does production readiness mean for a voice AI agent?
A voice agent is production-ready when it can end a call safely without ever needing to be clever. That means four things are tested against real audio, not transcripts: it recognises the caller's actual speech, it handles being interrupted, it has somewhere to hand off to a human, and every action it takes against a real system has a limit it cannot exceed.