AI Fundamentals: 6 Strategies For Building Success

By Judy Cubiss, SAP

Artificial intelligence (AI) may be a hot topic in industrial manufacturing, but companies often underestimate the impact of one critical element for its success: data quality. This trend creates considerable growth barriers in the form of unreliable insights, operational inefficiencies, and missed opportunities.

“Industrial manufacturers would be wise to fix data processes and derive insights using AI—and fast, as many executives have already identified business needs where these algorithms will benefit them the most,” warns Oxford Economics’ recent study on midsize manufacturers.

Here’s the reality of AI: It generates valuable insights only if the data feeding it is reliable and relevant. Investing in data management not only optimizes AI’s value but also improves chances of near-term and long-term growth.

Growth is the priority—and data is the fuel

Midsize manufacturers are focused on growing revenue, whether expanding market share, innovating products, attracting new customers, or entering new markets. Yet, growth remains modest, according to Oxford Economics, with only 12% of respondents reporting growth over 5%.

There’s an opportunity for midsize manufacturers to boost workforce performance, re-strategize their response to macroeconomic barriers, and innovate more effectively. And right now, AI and automation are viable solutions to these challenges.

Yet, many manufacturers are still caught between wanting to benefit from AI and lacking the proper foundation to support it. Oxford Economics found that approximately four out of five midsize manufacturers recognize the importance of integrating data for business model creation, process automation, or data accuracy and security. But 46% haven’t adopted data management and analytics software.

As a result, businesses struggle to implement key growth strategies, such as automating low-value tasks, replanning priorities based on new information, scaling processes and systems to match, and entering new markets.

Best practices for managing data in an AI world

“Unless data management practices improve, many industrial manufacturers will be left behind

in the race to AI excellence,” advises Oxford Economics. To build a solid foundation for AI, manufacturers must prioritize the following fundamental strategies:

1. Data collection and integration

Manufacturers can capture real-time data from machinery, operations, and customer interactions by implementing internet of things (IoT) sensors and other automated methods. The approach helps ensure data consistency and accuracy while reducing the risk of human error.

Integrating data from multiple sources—including ERP and CRM software—into centralized data lakes or warehouses and regularly cleaning it to remove inaccuracies, duplicates, and inconsistencies is also essential. Doing so enables a holistic view of operations, making it easier to apply AI-driven insights across the business.

2. Data governance

Defining clear ownership of data and establishing policies around access, sharing, and usage is critical for maintaining data quality and ensuring compliance with privacy regulations. By developing comprehensive data governance policies, manufacturers can prevent issues that arise from inconsistent data usage, such as inaccurate reporting or operational inefficiencies.

3. Data quality management

Standardizing data formats and securing data consistency helps ensure actionable AI insights can be leveraged across all departments and systems. With validation processes that consistently check data accuracy and integrity, manufacturers can minimize the risk of conflicting data and increase the effectiveness of AI-driven analytics.

4. Scalable infrastructure in the cloud

Growing data volumes brings the need for scalable infrastructure. Cloud solutions offer a flexible platform to manage large volumes of data without overwhelming internal IT resources. In addition, leading cloud platforms offer easy access to cutting-edge AI and machine learning algorithms. Edge computing, which processes data closer to where it’s generated, can also reduce latency and optimize bandwidth usage to support real-time AI applications such as predictive maintenance.

5. Data security

Strong cybersecurity protocols are necessary to protect sensitive data. Role-based access controls should also be in place to help ensure only authorized personnel can access specific data, safeguarding the business internally and externally.

6. Continuous improvement

Like AI models, data management practices must evolve. Manufacturers should establish feedback loops to continuously assess and improve their data management strategies. Defining KPIs and monitoring performance help identify areas for enhancement and high-impact AI use cases and align them with business goals.

Data management: The foundation for future success

Oxford Economics’ findings are clear: Midsize manufacturers prioritizing data management are well positioned to gain the full potential of AI. By investing in clean, organized data and scalable infrastructure, they can turn insights into action, outpace competitors, and shape a sustainable future for their business.