Executive summary
Manufacturers are implementing AI solutions, but disconnected low-quality data is reducing the value of those solutions. Years of uncoordinated technology investments have left many companies without a data foundation to support reliable insights and actions, which is creating a competitive risk.
AI can enhance supply chain visibility, risk management, decision-making and more, but only when it works from data that is accurate, timely and aligned across the business. That’s why data readiness is not just an IT project but a business-wide imperative that requires clear definitions, shared governance and ongoing investment. Leaders who treat data as currency for success can use it to build resilience, respond to volatility and fuel the full potential of AI.
Tangled up in data
Manufacturers are moving to implement more advanced AI solutions. But many of these solutions get caught in a tangle of disconnected data.
Over decades, manufacturers have often acquired equipment, technologies and whole companies without a plan for connecting data. Now, those data disconnects are dragging down the speed and the value that AI can deliver. That’s become a competitive disadvantage.
To stay competitive, manufacturers need capabilities that help them predict, plan and manage in their volatile market. “Volatility is not seasonal now, it’s structural,” Mahajan said. “We talk about tariffs today but, as a supply chain practitioner, volatility has been building for more than a decade. Ports clogging, geopolitical conflicts, pandemics, tariffs, supply shocks, labor gaps, demand swings, energy price swings and cyber risks will continue to disrupt. That is the primary reason you need to take resiliency more seriously — because volatility is structural.”
“When companies don’t value data, they don’t have insights to run effectively,” said Grant Thornton Business Consulting Services Partner Viral Chawda. “They don’t have control. Their effort is spent just searching through data to figure out what’s right. With 10 definitions of margin, people argue which number is correct, instead of how to improve it.”
AI solutions can process more data faster than ever before, but feeding them poor-quality data only accelerates confusion.
Manufacturing leaders need AI solutions that provide clear and accurate answers, based on comprehensive analysis. For instance, solutions need to help supply chain planners understand the complexity of their expanding risks. “It’s now not enough to just know who your suppliers are,” Chawda said. “You need to know the second, third, and fourth tiers, especially for government contracts or others with regulatory requirements.”
“Even beyond regulation, there is risk management that needs to analyze your third or fourth‑tier supplier for critical components,” Chawda said. “It’s not just pricing and margin — it’s also whether components are out of patent, out of life expectancy, counterfeit or coming from a country with trade restrictions or export considerations.”
Companies can’t answer those questions without data. “It is extremely important that you have the right data, that’s correct and complete, with the right data quality,” Chawda said.
When companies find that their data is not sufficient to fuel insights from AI solutions, they often assign the issue to their IT teams.
IT is not the answer
“Many companies just hand their data issues to IT and say, ‘Here, become our AI strategy,’” said Grant Thornton Manufacturing Industry Head Kelly Schindler. “That’s a huge mistake.”
Chawda agreed, “Business leaders say it’s IT’s problem, and they invest in plant capacity instead. But without insights, you can’t see where you’re going. Companies need clear definitions of key data indicators and a catalog of how they capture and compute them, with agreement across units and plants.”
Those decisions require insight from the business. While IT can help complete the data alignment, the decisions that guide the alignment and prioritize the data for decisions must come from business leaders.
“The first step is identifying the subset of critical data elements that matter,” said Grant Thornton Advisory Services Partner Sumeet Mahajan. “If you’re measuring yield, what are the 20 critical elements? Then, align on trusted definitions and put them in a marketplace where people responsible for upkeep can manage them. Power users enabling predictive models or even basic dashboards can then self-serve from that trusted marketplace. That is the floor of data readiness, even before AI solutions.”
However, AI solutions need more.
True AI readiness
“You need clean, aligned data for basic reporting,” Mahajan said. “The same data feeds predictive and prescriptive models. But clean data doesn’t mean it’s AI‑ready. AI needs granularity, speed and timeliness. Clean and certified doesn’t mean AI‑capable.”
Getting data ready for AI can be complex. “There is no one-size-fits-all within the broader manufacturing world,” Mahajan said. “So, how do we induce speed to get the data ready?”
Mahajan said manufacturers should focus on three layers:
- Common reference architecture: A common reference architecture provides a shared blueprint for how data should move, connect and be governed across systems. It creates a consistent foundation that supports scale and helps teams work from the same expectations for quality, timeliness and structure.
- Plant-specific or process‑specific semantic layer: This layer tailors shared data concepts to the unique context of each plant or process, ensuring that terms, metrics and relationships carry the same meaning for everyone. It enables teams to interpret data accurately, compare it reliably and apply it confidently to local decisions.
- Additional accelerators: Accelerators are tools and methods that speed up the work of preparing data by reducing manual effort and streamlining integration steps. They help manufacturers adopt scalable data practices faster so insights can flow more quickly into operations. They can even help manufacturers move toward data readiness by streamlining the metadata tagging that is key for data management.
Mahajan explained, “If you have all of those data qualities — timeliness, granularity, speed, security, scalability and more — you can be better prepared with an early warning system to sense the shocks you might face on the supply side, demand side or operations side. You can be in a better position to react. For ‘known-unknown events,’ like tariffs, the right data can help you prepare. For ‘unknown-unknown events,’ like a natural calamity, you can build resilience by having the right level of data granularity to know the break points in your network well in advance.”
Stakeholders have seen how data can give manufacturers resilience, and they are demanding that companies take action. A growing number of boards and investors are pushing for the adoption of AI in manufacturing to stay competitive and are asking for faster data readiness.
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“I was in a board meeting recently, and the chair’s number one question was why the company isn’t investing in technology and data,” Schindler said. “He doesn’t understand why they keep acquiring companies with 20‑year‑old ERP systems and no plan for integration.”
“There is tremendous pressure from boards on AI,” Chawda said. “You can’t do AI without the right data. So, use that to drive momentum. Leverage AI to help identify the subset of critical data to fix. Clean that. Show cleaner insights in a subsection of the business. Leadership will support it.”
As leaders see the importance of AI initiatives, they must understand that data readiness is the prerequisite for effective technology. “One does not guarantee the other,” Chawda said. “But without one, don’t talk about two — garbage in, garbage out. Manufacturers have underinvested in step one. If they want resilience and AI, they need to invest in data.”
Leaders also need to understand that data readiness is not a one-time effort. “This is just the start of the journey,” Mahajan said. “There is no end. That’s the most overlooked part — data readiness is not a one‑time exercise. And, it is not an IT exercise — it is a business exercise.”
“Data is currency,” Schindler said. “If you can’t get your data into the currency you need, and use it, you're going to go bankrupt while your competition extracts that value. It’s critical to resilience — at some point, it's going to be critical to survival.”
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