Why BI Is the Precursor to Effective AI Deployments

Executives often believe that AI is the silver bullet: If you invest in a model, value will follow. 

But the reality is harsher. Without a solid data foundation, AI often sputters. That’s where business intelligence (BI) steps in. By organizing, reconciling, and contextualizing data, BI lets leadership see where AI can truly move the needle and avoid wasted investment.

In the manufacturing industry, companies adopting BI first have been able to reduce stockouts, align inventory, and provide decision-makers with dashboards that guide operational priorities. Only after that clarity do they layer on predictive models or automation. Below is an excerpt that shows how BI can unlock transformation before AI even enters the frame.

As reported by Vera Nieuwland and Lilia Restrepo in “It’s All in the Data: Can Business Intelligence Help Solve AI Struggles?” on IndustryWeek:

“With the challenge of implementing AI and delivering real business value, manufacturers may find themselves uncertain about where to begin. A strong foundation in business intelligence can help companies organize, analyze and understand data so investments more strategically align with your business goals.

AI depends on clean and accessible data to function effectively. Many companies struggle with:

• Siloed data: Their information is scattered across multiple systems and departments. 
  
• Data inconsistency: When data is in different formats, includes inaccuracies or comes from outdated records, this can make AI unreliable. 
  
• Lack of understanding of their data: Knowing your data and its potential shortfalls is critical for successfully deploying AI. 
  
• Lack of clear objectives: Some companies make the mistake of deploying AI without fully understanding their objectives and the problems they want to solve. This can lead to a waste of time and resources. 

For instance, a mid-market manufacturer of industrial equipment faced challenges with siloed data across its procurement, warehouse and production teams. Due to misaligned inventory records and spreadsheets that were not kept up to date, the company was often weighed down with duplicate orders and delays. A BI platform that combined data from its supply chain, ERP and production systems into a single dashboard improved real-time visibility into inventory levels, production schedules and supplier performance metrics.

The result? The company saw a 30% reduction in stockouts, along with lead times shortened by 25%. Workforce allocation was optimized thanks to more accurate demand forecasting. Team leaders were able to shift from reactive troubleshooting to more proactive decision-making.” 

Continue reading “It’s All in the Data: Can Business Intelligence Help Solve AI Struggles?” by Vera Nieuwland & Lilia Restrepo on IndustryWeek. 

Why It Matters to You 

Many organizations pour millions into AI before realizing that poor data is the real blocker. Investing first in BI and implementing data cleaning, integration, and dashboards not only de-risks AI initiatives but enables leadership to spot which use cases are truly value-adding.

For executive teams, BI isn’t just an analytic tool — it’s a scaffolding for decision systems that gives you dashboards showing consistent, trusted metrics. In that environment, expansions, automations, and intelligent workflows can be tied to business outcomes, not bets.

Next Steps 

• CIO/Data Leadership: Conduct a data audit — map sources, data quality issues, redundancy, gaps. 
  
• Operations/IT Teams: Deploy a BI pilot that centralizes core master data (e.g., inventory, orders) to expose misalignment. 
  
• Business/Strategy: Use the BI insights to surface high-value AI use cases and validate them with ROI estimates before investing. 
  
• AI/Analytics Teams: Start new AI initiatives in read-only or advisory mode (no automation) to build trust and refine models. 
  
• Executive/Finance: Budget for data infrastructure (ETL, governance, dashboards) as foundational capex, not just model costs.