What is the biggest challenge preventing manufacturers from delivering on the promise of AI for enhanced efficiency and innovation?

Spoiler alert:

🚫 It's not budget.

🚫 It's not a lack of talent.

🚫 It's not resistant leadership.

It's the inadequate data foundations.

A recent survey revealed the following findings:

✅ 57% highlighted data quality as their biggest challenge when implementing AI.

✅ In chemical manufacturing, 75% report poor data quality as the major barrier to AI adoption.

✅ Almost half (47%) struggle with data governance

✅ Fewer than 23% say that most of their production data is useful for AI.To understand how manufacturers can modernize their data architectures for AI readiness, I had a podcast conversation with Jonathan Wise, Chief Technology Architect at CESMII (The Smart Manufacturing Institute).

He identified three pillars essential for AI readiness:

1. 𝐃𝐚𝐭𝐚 𝐀𝐜𝐜𝐞𝐬𝐬𝐢𝐛𝐢𝐥𝐢𝐭𝐲You can't train AI without accessible data. Jonathan breaks down why getting your data flowing across systems is the first critical step.

2. 𝐃𝐚𝐭𝐚 𝐋𝐚𝐛𝐞𝐥𝐥𝐢𝐧𝐠Having data isn't enough. Meaningful, contextualized data is key for any AI project.

3. 𝐃𝐚𝐭𝐚 𝐑𝐞𝐥𝐚𝐭𝐢𝐨𝐧𝐬𝐡𝐢𝐩𝐬It's not just about isolated data points. AI thrives on the connections between those points.

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Watch/Listen below:

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