Manufacturers such as Wolfspeed are training smart AI agents with organizational knowledge to accelerate decisions.
Manufacturers operate at the intersection of precision, scale and constant change. Production environments are increasingly automated, products evolve faster, and global supply chains shift overnight. Yet despite this digital complexity, one problem has remained stubbornly consistent: While data exists everywhere, actionable intelligence does not.
Operational systems generate terabytes of readings, logs and traces. Engineering teams store insights across documents, chats and troubleshooting notes — which they often have to dig up when they want to reference them again. Quality organizations rely on decades of historical analyses, and leadership depends on dashboards that often raise as many questions as they answer. This results in teams spending too much time stitching together data from disparate sources and not enough time acting on what that data means.
Snowflake Intelligence is designed to help manufacturers tackle these issues head on. It is a unified platform that brings structured and unstructured data together, enriches it with enterprise semantics and layers on powerful AI agents capable of understanding and reasoning across the entire manufacturing ecosystem. It’s an approach built not simply to modernize analytics but to support faster, more informed decision-making across engineering, operations, quality and leadership. Industry-leading manufacturers such as Wolfspeed are already experiencing just how transformative Snowflake Intelligence can be.
Wolfspeed, a pioneer in silicon carbide semiconductor manufacturing, recently shared how Snowflake Intelligence is reshaping their approach to data and AI. Their operations involve long production cycles, high precision, sensitive yield variables and deep engineering complexity. Timely insight can significantly impact throughput and quality.
Before consolidating their data, Wolfspeed’s environment contained more than 200 silos. Engineers spent considerable time searching for the right information, aligning definitions or reconstructing historical context. Critical learnings often lived in meeting notes or passdowns that were not easily accessible when issues arose. As their operations scaled, these challenges compounded.
Snowflake Intelligence provided the architectural foundation to unify Wolfspeed’s structured and unstructured data. Wolfspeed now brings together manufacturing systems, operational documentation, troubleshooting logs and engineering discussions into a single, governed ecosystem. AI agents can reason across this combined knowledge set and surround every operational question with the context that makes the answer meaningful.
A key concept that Wolfspeed had to consider when building their agents was the philosophy of “Ask versus Do,” or agents that merely answer questions and provide information versus those that can take meaningful action. Unni Velayudhan, Senior Director of Data and Automation at Wolfspeed, described his view that “Do” systems can deliver significantly more value than “Ask” systems in manufacturing contexts. “When you build a ‘Do’ system, the value is 5x over an Ask system,” Velayudhan said. “‘Ask’ systems will be able to give you information, but when they take that information and perform an action, the value is much higher.”
In fact, according to Velayudhan, Snowflake Intelligence enabled Wolfspeed to build not just capable, intelligent agents but rather an entire interactive knowledge base with faster access to relevant data for everyone across the organization. “This is crucial,” Velayudhan said, “because from a manufacturing standpoint, how fast you can make a decision is very important for the success of the company, for the fab. The more delays you experience, the more issues you can expect to have.”
Wolfspeed’s emphasis on governance has been central to their success. Wolfspeed focused early on designing strong semantic views, defining verified queries and establishing role-based agent access. These investments ensure accuracy and trust, which are critical in a high-stakes semiconductor environment.
According to Wolfspeed, the early impact of Snowflake Intelligence across the business has been significant, providing:
Faster investigation and recovery during operational events
Improved knowledge retention as documentation becomes more searchable and complete
Reduced time spent hunting for past analyses — Wolfspeed reports that certain teams went from spending 30% of their time hunting for data, 50% on cleaning and enriching and 20% on analysis, to spending 20% on enrichment and 80% on analysis, decisions and actions
Increased confidence in insights used for decision-making
A foundation for more sophisticated agent-based automation
Wolfspeed’s journey continues to evolve, but their experience shows what is possible when data, knowledge and AI intelligence converge inside a unified platform.
The complexity of manufacturing data comes not just from its volume but also from its diversity. A single plant might pull from MES and SCADA systems, PLCs, Internet of Things (IoT) sensors, maintenance logs, ERP systems, test equipment, shift passdowns, standard operating procedures (SOPs), engineering notebooks and, increasingly, the conversational knowledge exchanged across Slack and Teams. As mentioned earlier, Wolfspeed had a variety of data spread out across hundreds of silos.
Each of these sources offers only a partial view of reality — the real challenge is understanding the full picture.
Snowflake Intelligence unifies these sources into a governed, secure ecosystem where data is not only centralized but connected through shared meanings and definitions. Structured data from manufacturing systems sits alongside unstructured content such as documents, meeting transcripts and operator notes. This gives teams both the “what happened” and the “why it matters” and creates a powerful foundation: a single place where manufacturing truth lives, accessible to the people across the entire organization who need it most.
For many manufacturers, the most valuable operational knowledge is not captured in formal systems. It lives in human conversations, decisions and learnings accumulated over decades, in the form of everything from meeting transcripts and shift handover notes to maintenance reports, troubleshooting threads in Slack or Teams and so much more.
These assets shape how engineers debug issues, how operators recover equipment and how teams make daily decisions. But because they exist across multiple repositories and often lack structure, they remain hard to search, reference or apply in real time.
By ingesting and governing this content, Snowflake Intelligence transforms it into a searchable, intelligent knowledge layer. Teams can surface prior analyses instantly, see how similar problems were resolved in the past and understand the rationale behind decisions without digging through folders or pinging colleagues.
Over time, this improves knowledge retention, strengthens documentation and builds a workplace where insight compounds rather than disperses.
One of the most transformative shifts Snowflake Intelligence introduces is the ability for manufacturing teams to engage with their data conversationally. Enterprise-ready AI agents understand context, semantics and the specifics of manufacturing processes.
Instead of switching between dashboards, databases or shared drives, engineers can simply ask questions such as:
“Where did yield dip this week, and what changed compared to last month?”
“Summarize troubleshooting actions taken after the last equipment failure.”
“Show the top contributors to test escapes across the last three lots.”
These inquiries require an understanding of how the organization defines tools, lots, steps, lines, products and specifications and where the right data lives, how it’s filtered and how different teams interpret it. Snowflake Intelligence agents use semantic views and verified queries to ensure answers align with the organization’s official definitions and expectations, not generic interpretations.
This helps teams move seamlessly from curiosity to clarity.
Once freed from the friction of data access, engineers and operators can spend more time solving problems, exploring hypotheses and implementing improvements.
Information alone does not accelerate a plant — action does.
Snowflake Intelligence enables agentic workflows in which agents not only retrieve insights but also help teams determine what to do next, making it possible to build a “Do” system over an “Ask” one, as Velayudhan described. Manufacturers are beginning to adopt these capabilities to support myriad efforts across their businesses, such as:
Guided troubleshooting after tool failures
Recommendations on likely contributors to quality drift
Consolidated historical context for investigations
Automated synopses of recurring issues
Notifications when key metrics deviate from normal patterns
These capabilities reduce the gap between understanding and acting. New engineers gain confidence faster. Operators have support grounded in institutional expertise. Leaders get clearer visibility into root causes and trends. And because all of this is layered on governed data and enterprise semantics, teams can trust the results.
As manufacturers begin adopting Snowflake Intelligence, they generally scale successfully when they:
Start with the questions that matter most to their operations
Build semantic models that reflect how their teams actually think about data, not just around dashboards and schema
Integrate unstructured and structured data early so insights carry full context
Roll out agents to small groups first, iterating based on real use cases
Pair every AI capability with strong governance, role boundaries, data controls and verified logic
This balanced approach ensures AI enhances, rather than disrupts, existing workflows. Over time, it enables teams to operate with a level of connected intelligence that feels like an extension of the organization itself.
Wolfspeed’s experience demonstrates how unified enterprise knowledge and AI-driven agents can reshape decision-making across manufacturing. To explore their architecture, governance model and practical agent use cases, watch the on-demand webinar:
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