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Artificial Intelligence How to Make AI Initiatives in Production Successful

From Tobias Knieper, Lead Marketing Manager, DACH at Fivetran | Translated by AI 4 min Reading Time

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Many managers today are ambitious to implement AI across the board in production. But without a robust data infrastructure, (pilot) projects will fail. The understanding of treating data as a fundamental infrastructure and not as a side issue is crucial for a fundamental transformation today.

Agent-based AI requires a database that also provides the necessary context—a challenge especially in manufacturing due to the physical and distributed nature of industrial data. Agent-based AI requires a database that also provides the necessary context—a challenge especially in manufacturing due to the physical and distributed nature of industrial data.(Image: Fivetran)
Agent-based AI requires a database that also provides the necessary context—a challenge especially in manufacturing due to the physical and distributed nature of industrial data. Agent-based AI requires a database that also provides the necessary context—a challenge especially in manufacturing due to the physical and distributed nature of industrial data.
(Image: Fivetran)

In industry, proof-of-concepts for AI initiatives are often very promising, but then fail to deliver the hoped-for return on investment. The cause is usually not the AI models, but the data basis: fragmented, poorly managed and low-context data environments, which are common in manufacturing.

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