Which processes are suitable for AI or automation?
Maybe you see opportunities for AI everywhere. Or so many that you don't know where to start. We help determine which processes are interesting, which technology fits them and which ideas you're better off leaving alone.
Does this sound familiar?
This question often comes up when there is clear inefficiency, but it's not yet clear which technical solution fits it best.
Lots of repetitive knowledge work
Employees keep reading, checking, summarising or classifying similar information.
Information is copied over by hand
Data moves via people from email, documents or spreadsheets into other systems.
There are many AI ideas at once
Different teams see opportunities, but nobody knows which are the most interesting technically and commercially.
You don't want to automate for the sake of it
The solution has to demonstrably reduce time, errors or friction.
Where do you want to be?
You don't want to end up with a list of twenty AI ideas. You want to know where a technical investment actually makes sense.
The best opportunities first
You know which processes combine enough value, volume and technical feasibility to explore further.
AI where it's needed
We distinguish between AI, regular automation, existing software and simple integrations.
A concrete next step
You know which question to validate or build first, or which not to pursue any further.
Why does this happen?
Not every annoying process is automatically a good AI problem. The value lies in the combination of process, data, technical feasibility and impact.
See how we approach thisAI is taken as the starting point too quickly
A fixed rule, integration or existing tool can sometimes solve the same problem more simply and reliably.
The real bottleneck is still unclear
What looks like manual work at first sight may be caused by a poor process or a missing integration.
Feasibility differs per task
Some tasks tolerate uncertainty, others require virtually error-free behaviour.
AI Reality Check
In an AI Reality Check we examine up to three ideas or one concrete process. We assess value, technical feasibility, data, integrations, risks and build versus buy. If one part then needs to be proven in practice, an AI Prototype Sprint can be the logical next step.
View this offer€1,950 excl. VAT
Fixed price · one process or up to three AI ideas
- Result
- A reasoned technical direction and build / buy / don't build advice
- Duration
- About two weeks
- Next step
- Build, validate with a prototype first, or deliberately stop
Why 9to5 for this?
We start with the problem you're trying to solve, and only then ask which AI model fits. Sometimes the best technical conclusion is that part of it doesn't need AI at all.
View casesSoftware engineering before AI hype
We also compare AI with automation, integrations and existing software.
Technical feasibility counts from the start
Data, systems and operational constraints are part of the assessment.
We can build the follow-up too
The advice doesn't have to be translated again by another technical party afterwards.
What else fits your situation?
AI Prototype Sprint · €6,950 excl. VAT
From one concrete AI idea to a working end-to-end prototype that lets you actually test the key technical assumptions.
View offerDo we need AI for our idea?
You have a problem or product idea and suspect AI could be a solution, but first want to know whether that is technically and practically the wisest route.
View use caseShould we build ourselves, use existing software or connect systems?
The problem is clear, but there are several technical routes. You want to know which combination of buying, building and integrating is wisest.
View use case