Building AI workflows: what to check before you start
By Mike van BeaumondAI Engineer · company brain & automationpublished on
6 min read
Quick Take
De meeste AI workflow trajecten mislukken niet door de technologie, maar door een verkeerde scope, geen plan voor uitzonderingen, of een partij die verdwijnt na livegang.
Reality check: most companies looking to build an AI workflow assume the hard part is the technology. Which tool, which model, which integration. That''s rarely where things actually go wrong. The real risk sits in scope that was never pinned down, exceptions nobody planned for, and a partner who stops responding after launch.
This article is for anyone seriously considering having an AI workflow built, who wants to know where the real risks are before signing anything.
Direct answer: Check six things before choosing a partner: a clearly scoped project, a concrete plan for exceptions, error handling you can actually see working, clarity on who owns the workflow, a fixed point of contact after launch, and a price that matches the complexity rather than a fixed package.
Why most projects don''t fail on the technology
A workflow that moves data from one system to another is relatively simple to build. What makes a project succeed or fail is what happens the moment reality deviates from the ideal scenario: a website that can''t be read, a customer who doesn''t match the expected CRM format, an API that doesn''t respond.
Quick Take: an AI workflow isn''t done when the happy path works. It''s done when you know what happens on the day something breaks, and you find that out before it goes unnoticed for three weeks.
The six things to check
Scope, agreed on paper before anything is built
Before a single line gets built, it should be clear exactly what the workflow needs to deliver, which steps it includes, and which decisions get made along the way. A partner who wants to start building immediately without agreeing on this first usually delivers something that''s close, but not quite what you meant.
A concrete plan for exceptions
What happens if a website can''t be scraped? If a customer isn''t found in the CRM? If the AI returns an answer that doesn''t match the expected format? Ask specifically. A partner who says "we''ll figure that out as we go" hasn''t thought about it yet.
Error handling you can actually see working
A workflow that fails silently is more dangerous than no workflow at all. Ask whether you get an alert the moment something breaks, and ask to see that, not just hear it described.
Ownership, clear from day one
Who owns the workflow once it''s running? Does it run in your own environment, with your own accounts, or in the builder''s environment, leaving you dependent on them? This determines whether you''re locked into one partner or not.
A fixed point of contact after launch
Many projects feel successful on launch day and stall six weeks later, when a small change is needed and nobody picks up. Ask what happens after launch, and with whom.
Pricing that matches complexity, not a fixed package
A workflow that only moves data costs meaningfully less than one that has to judge, decide and write. Be cautious of a fixed price before the scope is clear. A partner who runs a short intake first and only then names a price usually gives a more realistic picture than one who quotes a flat fee upfront.
What this doesn''t mean
Fairness matters here. Not every small automation task needs the full weight of these six points. For a simple, linear connection (if this happens, do that), a lighter approach is often enough, and this list is overkill for what you actually need. These six points matter most once judgement, context or a decision enters the workflow, not for a plain trigger-action connection.
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Frequently asked questions
That depends on scope: how many processes, how many systems, and how much judgement the workflow needs to apply. Indicative pricing starts around a few thousand euros for a single workflow and increases as multiple connected workflows or a full dashboard get added.
For a simple connection between two tools, building it yourself with no-code platforms is often workable. Once judgement or context comes into play, or the workflow becomes business-critical, building it yourself usually takes more time than expected, especially around error handling.
Simple automation follows a fixed rule: if this happens, do that. An AI workflow can also judge, decide and write based on context, which is needed once the task no longer fits a fixed rule.
Ask about two or three specific exception scenarios relevant to your process, and ask how the workflow handles them. A partner who can answer that clearly has already thought it through. A vague answer is a signal.