Manufacturers are reaching an inflection point in their digital transformation journey, defined by high expectations and ambiguity about what lies ahead. According to Valtech’s Voice of Digital Leaders 2024 Report, one out of five organizations report not using AI in their operations, which is a clear opportunity missed. On top of improving productivity, when used diligently, AI-driven product recommendations and content can also indirectly boost sales by enhancing customer engagement, satisfaction and brand relevance.

The true potential of AI goes far beyond immediate improvements; it promises to revolutionize business models by enabling personalized experiences and more streamlined processes.

Step 1: Laying a Strong Data Foundation

For advanced AI applications like predictive maintenance and process optimization, a strong data foundation is essential. However, manufacturers often struggle with critical data being trapped in silos. Data from various business units may be disconnected or stored in incompatible systems, making it outdated, incomplete, and inconsistently formatted. This impedes AI applications from running successfully.

Other challenges include:

Data sharing. Predictive maintenance requires extensive data sharing, sometimes beyond company boundaries. Manufacturers should evaluate the benefits of sharing data with B2B partners, balancing these against the risks of exposing sensitive information.

Customers’ equipment data availability. Customers lack incentives to share factory data and often ask questions like “how is this necessary?" Another issue is reluctance to upload due to security policies, interoperability issues between different production ecosystems, and cost concerns for installing necessary sensors.

To address these issues, manufacturers should dedicate resources to launching data projects that facilitate better data integration and sharing company wide. These projects will improve the quality and accessibility of data, enabling advanced AI applications. Evaluating cases can help convince customers to share data by displaying the benefits gained in doing so.

Step 2: Building Confidence in AI Adoption

A culture of risk-aversion and the need to protect trade secrets hinders AI adoption in the manufacturing industry. On top of this, manufacturers see AI as risky because it requires gathering and disseminating large amounts of data which can be susceptible to cyberattacks.

Companies need to begin mitigating fear with:

Low-risk experiments. Start with low-risk AI applications, such as in customer service, to build experience and confidence.

Sandbox environments. Experimenting with production data in controlled environments can help manufacturers learn how to use machine learning models for tasks like predictive maintenance.

To combat AI fear, begin with simple AI projects to gain practical experience. These initial successes can build knowledge and confidence, paving the way for more complex applications. Creating a culture that embraces experimentation and learning will facilitate broader AI adoption.

Step 3: Driving Organizational Transformation

Established manufacturing companies often operate within mature ecosystems that lack positive disruptive influences, leading to a scarcity of new best practices for innovation. There is also an overall lack of strategic priority for embracing change, which is endangering the success of AI adoption. Together, these two factors are inhibiting forward motion when it comes to technology.

Ways to begin driving organizational change include:

Holistic initiatives. Consolidate digitalization and AI projects into centrally managed programs with clear governance structures.

AI roadmaps. Develop comprehensive AI roadmaps, strategies for recruiting and retaining specialists, and IT setups that enable KPI measurement and data exchange.

Leadership engagement. The C-suite must recognize the potential of a data-driven approach to drive innovative business models. Viewing data as a strategic asset, akin to manufactured products, is critical.

The bottom line is that if companies don't prioritize AI adoption, they will fall behind. By making AI  an important priority and centralizing efforts, manufacturers can achieve significant progress. And by focusing on these foundational steps — laying a strong data foundation, building assurance in AI adoption, and driving organizational transformation — manufacturers can embark on their AI journey with confidence.

Herbert Pesch is managing director Valtech B2B at Valtech.