The Ultimate Guide to Procuring Generative AI for Your Manufacturing Company
Artificial Intelligence is transforming every aspect of your supply chain operations and procurement processes; it’s automating workflows that would normally take hours and churning out insights to help with strategic decision making. It’s taking away the margins of manual errors, giving visibility into your data that you have not had before, and it’s strengthening relationships between departments and your suppliers. Yet, there’s one massive hiccup that could stop you from enjoying the benefits of automation: Poor Data Quality.
The term “Garbage In, Garbage Out” applies here now more than ever. If you’re feeding incorrect data to your ERP systems, MRP systems and other data warehouses, you’re going to get incorrect output even with intelligent AI agents like Agent PO to help automate procurement workflows.
The Impact of Poor Data Quality
Procurement teams rely on data from diverse sources—ERPs, emails, CRMs, and spreadsheets. When this data is incomplete, inconsistent, or siloed, the outcomes are predictable:
- Eroded Customer Relationships – incorrect information gives you wrong visibility over the status of customer orders. This could result in miscommunication or delayed communication with your customers.
- Loss of confidence in ERP systems – because employees enter broken data, the system churns out wrong information. Relying on this output would give misleading results which causes teams to rely on manual processes and spreadsheets
- Bad Decision Making – Because of bad data, decision makers aren’t equipped with enough information or incorrect information that hinders their ability. This leads to errors in forecasting, procurement, and production planning, increasing costs and delays.
- Strained Supplier Relationships – Missing data or incorrect data could lead to miscommunication between the procurement team and suppliers regarding components or compliance documents. This could lead to delayed lead times and missed delivery deadlines
Why Poor Data Affects your Tech Stack
Despite having the best technology stack or intelligent automation to help with managing your post procurement processes, poor data won’t allow you to enjoy the benefits of the technology.
Picture this: you’ve integrated an intelligent automation agent to your ERP system for certain processes in your manufacturing operations. However, because your ERP is being fed with incorrect information, your AI agent is providing you with inaccurate insights.
Why is Data hygiene a prerequisite for your technology stack?
- Decision Intelligence Relies on Accuracy: AI-driven recommendations—like identifying cost-saving opportunities or flagging supply chain risks—depend on accurate, up-to-date data. Incorrect data leads to flawed insights.
- Automation Requires Reliable Triggers: Automating procurement workflows or inventory alerts relies on trustworthy data. For example, triggering an order replenishment alert based on incorrect inventory data could lead to overstocking or stockouts.
- Low trust in AI and Automation systems: Teams are less likely to adopt AI tools if the underlying data lacks credibility. This creates a pattern where poor data prevents adoption, and low adoption exacerbates data quality issues.
What are the steps to Clean Messy Data?
As organisations, it can be a time consuming tasks to clear the messy data and feed it to your system, however there are a few ways you can do this:
- Training Workshops: You can provide your staff with workshops or training sessions on how to feed data cleanly into your data warehousing systems.
- Data Standardization: Organisations can implement guidelines and rules on how staff can input data into correctly into the systems
- Perform Data Audits – Through data audits and regular check ups, your organisation can ensure the data entered into your systems are correct and remain useable
How Agent PO can help with cleaning your data
The good news is that AI isn’t just a victim of poor data quality—it’s also a powerful tool for addressing it. Agent PO, an intelligent AI agent that is designed to help manufacturers with their procurement workflows, can also assist in keeping your data clean. Here’s some ways how:
1. Flagging Data Inconsistencies
Agent PO can continuously analyze data across systems to identify discrepancies and flag errors. For instance:
- Highlighting mismatched inventory records across ERP and warehouse systems.
- Notifying procurement teams when critical fields (e.g., supplier delivery dates) are missing or inconsistent.
By surfacing these issues in real-time, Agent PO assists teams to prioritize and address the most critical data quality problems.
2. Enabling Gradual Data Cleanup
Because Agent PO is integrated into multiple systems, it can focus on multiple sources to collect data firsthand. For example, Agent PO will sit in your inbox and read through your emails, picking up on PO revisions or new POs that your employees may otherwise have missed or forgotten to enter into the ERP systems.
3. Providing Immediate Value
Even with messy data, AI can deliver value by leveraging reliable inputs such as its integration to other systems;
- Identifying top materials for cost-saving opportunities based on supplier performance and using external market data to understand potential pricing fluctuations
- Automating follow-ups for shipping documents, ensuring compliance even if other ERP data remains incomplete.
4. Driving Data Confidence
Procurement teams can query Agent PO using natural language. This allows them to communicate with the internal data seamlessly. Through its intelligent automation, Agent PO can drive confidence through employees to adopt it and trust the data that is produced by it. As the system is easy to use, organisations do not have to spend countless hours on training either.
Turning Messy Data Into Actionable Insights
We have worked with and spoken to many procurement professionals and heads of supply chain to understand that messy data across ERP and other systems are the biggest challenges they face. Prior to integrating Agent PO, their team spent hours manually reconciling supplier communications and updating records which would lead to human errors in inputting the data.
However after integration with Agent PO, they were able to benefit from:
- Automated follow-ups with suppliers, cutting down daily manual tasks from three hours to five minutes.
- Agent PO flagged inconsistencies in supplier delivery timelines, helping the team focus on critical corrections first.
- Over time, the data cleanup process became collaborative, with Agent PO identifying gaps and the team addressing them systematically.
- Allowed manufacturers to build stronger client relationships as they were able to communicate exactly when their orders would be completed and delivered.
Conclusion
Clean data is crucial for supply chain operations, especially because they are becoming complex over time. While poor data quality can hinder the full potential of technology in supply chain operations, it doesn’t have to be a roadblock. By leveraging AI agents and intelligent automation to clean and integrate data incrementally, manufacturers can unlock value immediately while building a foundation for long-term success.
The Ultimate Guide to Procuring Generative AI for Your Manufacturing Company
Author:
Kevin Musprett
Head of Growth at Kavida.ai
Kevin drives GTM at Kavida, an AI agent used by manufacturing purchasing teams so they never miss a critical order from suppliers. Kevin dedicates himself to building and scaling kavida.ai to become the PO management platform of choice for an addressable market of over 15 million users.
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