It’s All in the Data: Can Business Intelligence Help Solve AI Struggles?

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• Data inconsistency: When data is in different formats, includes inaccuracies or comes from outdated records, this can make AI unreliable.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.

Starting with a Strong Data Foundation

Before implementing AI, it is good to assess your existing data landscape. Start by identifying what data you already have—whether it’s related to production, supply chain management or customer demand.

Next, it is important to determine where this data is stored: in spreadsheets, ERP systems or legacy databases? Scattered and inconsistent data is a common problem for companies. Different departments working from separate data sources can lead to inefficiencies and unreliable information.

A business intelligence system can help centralize and clean data, aggregating information from multiple sources into a single system and mitigating redundancies. Visual dashboards can help with monitoring key trends and making informed decisions.

BI can also help identify and prioritize AI use cases. Instead of integrating AI without clear objectives, you can uncover where data-driven insights will be most impactful—whether it's in cost reduction, optimizing operations or improving the customer experience.

Once a manufacturer has a structured data environment, BI can help uncover where AI can drive the most impact, enabling strategic, high-value implementations. Real-time dashboards can highlight key trends and inefficiencies, identifying possible areas for AI-driven automation and optimization. Historical data analysis can reveal performance patterns, providing a strong foundation for AI models and predictive insights. Anomaly detection can identify data quality issues, making sure AI systems operate with clean, reliable information.

Implementing AI: A Phased Approach

Rather than a large AI investment up front, a strategic, phased approach begins with exploration and gradually scales successful initiatives.

Step 1: Start wth data-driven AI pilots

Using BI insights, identify one or two high-impact AI opportunities and start small. This allows companies to test AI in a controlled environment and refine their approach before scaling. For example, an AI-powered chatbot can be implemented in customer service to handle routine inquiries, ideally freeing up human agents to devote their time to more complex issues. This can be more effective than rolling out AI across all customer touchpoints at once because companies can measure effectiveness before broader deployment.

Step 2: Establish your platform and technology strategy

For companies looking to integrate AI across the organization, there are three primary options that depend on a company’s existing technology infrastructure, scalability needs, available resources and budget:

Best-of-breed solutions: Selecting specialized AI tools for specific functions, such as automated quality control or AI-driven demand forecasting.

Single-platform solutions: Effective for less complexity and maintenance, this approach uses an all-in-one system, like an ERP with AI capabilities. 

Custom-built AI solutions: An enterprise-wide AI strategy or platform with more tailored or proprietary AI solutions suited to specific business needs. This approach is ideal for companies with sufficient in-house expertise. 

Step 3: Assess, learn, scale and train

Once an AI initiative proves successful, perform an assessment to deeply understand the risks as well as what is needed for the organization to use and adopt it successfully. BI tools can support this process by tracking KPIs, measuring AI output quality and identifying where improvements or additional training are needed. Scaling should be aligned with IT, operations and management teams so there is clear understanding on goals, operating procedures, expectations and resource allocations.

AI prompting and interpretation training is a necessary but often overlooked step to effectively communicate and work with AI tools. Many AI models require well-structured inputs to generate meaningful outputs. BI dashboards offer visibility into how AI is being used across teams, helping organizations tailor training efforts and improve their scaling-up process.

AI holds incredible potential for manufacturing companies in terms of efficiency, cost savings and innovation. Business intelligence can help manufacturers understand their data, identify AI opportunities, and implement AI successfully.