The digital baseline required to start is lower than most people assume.
Artificial intelligence has surged into nearly every industrial sector over the past three years, but the enterprises moving fastest often aren’t the ones you’d expect. And according to one industry expert, the manufacturers best positioned to extract real value from AI are those that stop thinking about “AI projects” and start thinking about data strategy.
“There’s no AI strategy without a data strategy. Let’s make sure we get all of the right context in the data pieces for your organization ready to go,” says Jeff Hollan, who was director of product at Snowflake at the time of this interview. Hollan arrived at this insight after more than a year working closely with customers deploying AI inside mission-critical operations.
The promise of agentic AI—systems that synthesize, reason, and take action on enterprise data—depends heavily on context. Public LLMs can draft emails or summarize meeting notes, but they can rarely answer the questions that matter to a production engineer or plant manager.
“Once I start doing my job, LLMs become far less valuable. For the questions I care about, they just don’t have the correct context. If you don’t have the right context, you’re not able to build those pieces,” Hollan says.
This is where manufacturers, particularly those with fragmented MES, ERP, sensor, and quality datasets, face the first friction point: model performance directly correlates with the organization’s ability to centralize and standardize its data foundation.
