Maximilian, a Senior Product Manager at Körber Digital, provided the audience with a quick overview of the buzz around AI. “Everybody is into AI… We’re totally high on the hype,” he said.
Maximilian covered areas such as:
- The rise in Google search inquiries around AI.
- The capital investment in AI companies.
- The massive expansion in AI tools in recent years.
Know your place. Know your goal. Build Trust.
- Know your place: This is about understanding how your AI service fits into the UX, application, intelligence, and infrastructure marketplaces.
- Know your goal: Here the key is to be clear about your objectives.
- Build trust: Finally, businesses must work out how to develop relationships with users based on confidence.
After Lorenz had outlined his framework for AI best practice, Dirk asked a few questions, including:
“What are the skills a company should have to leverage the opportunities [around AI]?” to which Lorenz answered, “It’s not purely technological. It comes down to the willingness to change and the acceptance that there’s a big wave rolling to us all.”
AI use case in the pharmaceutical industry
He outlined how his firm trains AI to fulfill the specific needs of the pharmaceutical industry. In a sector with stringent regulatory standards, this includes developing models with a high enough level of accuracy to operate alongside laboratory equipment.
He said, “There are multiple regulations which we have to follow so this is a bit… let’s say the killer of the value but on the other side it also brings in the value because… this is really the big hurdle for everyone to enter this industry.”
Regarding the specific AI tools used by his company, Leder mentioned the following domains:
- Image-based classification.
- Sequence-based classification.
- Object detection.
Value measurement with data products
He shifted the conversation to value measurement. How can you calculate business value in the field of AI? And why is it important?
Central to Mazat’s presentation was the idea of continuous performance assessment.
“When it comes to value measurement I’d argue that it’s super important to consider it from the very beginning,” he said.
He also also established the following ideas:
- Value measurement is a core piece of a Datapreneur’s Toolbox.
- Value measurement needs to be considered along the whole data product development cycle.
- Having a clear view on business value facilitates prioritization decisions.
- Value measurement is specific to the data product type.
- In light of the complexity of many data initiatives, it’s important to ask whether each of your measurements are capable of delivering a useful insight.
The panel discussion is a time to share ideas and experiences and look ahead to the future of the AI scene.
In the discussion, Dirk quizzed Andreas Mazat about how to justify data and AI investment to potential clients. Andreas Mazat advised companies to “be cautious and not try to build everything.”
He said it was vital to understand a firm’s data assets and prioritize action in terms of where you can derive the most value.
During the final few minutes of the event, Dirk asked what themes would dominate discussions in the AI scene a year from now. In response to this question all three panelists mentioned AI regulation. This is unsurprising as the EU is due to vote on an AI Act this year.
Let’s get the future we want for AI
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