First, consider which routine business processes currently take the most time.
How much time will be saved by automating the task? And how often do you need to do this task? The comic XKCD created an excellent visualisation of when it does (or does not) make sense to automate a task.
For example, consider the “automated invoice processing” use-case in our how to guide. GrillMaster Europe processes hundreds of supplier invoices each month across multiple countries. These invoices arrive in various formats—PDF, scanned documents, emails—and are largely handled manually. If each invoice takes 5 minutes to process, and the team needs to process between 20 and 50 invoices per day, this is a perfect use case for AI.
However, if you have a process that usually takes you half a day once per year, this is a less relevant use case (looking purely from a time savings perspective).
Next, consider which business processes currently have the most mistakes.
How error-prone are specific tasks? And how costly are these errors to fix? For example, consider the “automatic anomaly detection in finance” use case in our how to guide. Within GrillMaster Europe’s finance processes, large volumes of transactions are processed daily—including journal entries, claims, payments, and intercompany settlements. In many organisations, errors, irregularities, or potential fraud are only discovered later—often during audits or month-end closings. This leads to rework, lost time, and compliance or data quality risks.
By deploying AI-driven anomaly detection, GrillMaster Europe can structurally tackle these issues. This solution is suitable for any modern finance platform with access to journal data and master data—supporting API integration or data streaming. In this scenario, suspicious transactions are displayed in real time via dashboards or alerts to controllers or auditors.
‘AI takes over the manual entry work and makes the process less error-prone. This gives employees more time to optimise product configurations and provide proactive customer advice. That is where the real quality gain lies.’
Finally, consider which business processes are the most frustrating?
Are there tasks you can automate for employee satisfaction? Or simply because you hate doing them? For example, consider the “multimodal analysis of service requests” use case in our how to guide. The service team has complained that the initial assessment of service requests is one of the most annoying aspects of their job—they prefer the steps in which they are actually able to help customers!
GrillMaster Europe receives daily service requests from consumers and resellers about defective or damaged products. These requests come via email, contact forms, or self-service portals—often including a short description and photos of the issue. By using a multimodal AI solution—which processes both images and text—GrillMaster Europe can partially automate and speed up the evaluation of service requests.
There are multiple ways to define the right AI use cases for your business, and identifying the right use case is the first step to seeing real benefit from AI.
