Manufacturers are reaching an inflection point in their digital transformation journey, defined by high expectations and ambiguity about what lies ahead. According to Valtech Voice of Digital Leaders 2024 Reportone in five organizations say they do not use AI in their operations, which is clearly a missed opportunity. In addition to improving productivity, when used diligently, AI-powered product recommendations and content can also indirectly boost sales by improving 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: Establish a Strong Database
For advanced AI applications such as predictive maintenance and process optimization, a solid database is essential. However, manufacturers are often faced with critical data trapped in silos. Data from various business units may be disconnected or stored in incompatible systems, making it outdated, incomplete, and inconsistently formatted. This prevents AI applications from running correctly.
Other challenges include:
Data sharing. Predictive maintenance requires extensive data sharing, sometimes across company boundaries. Manufacturers should weigh the benefits of sharing data with B2B partners, weighing them against the risks of exposing sensitive information.
Availability of customer equipment data. Customers are not encouraged to share factory data and often ask questions such as “why is this necessary?” » Another problem is the reluctance to upload due to security policies, interoperability issues between different production ecosystems and cost issues related to installing the necessary sensors.
To address these issues, manufacturers should dedicate resources to launching data projects that facilitate better data integration and sharing across the enterprise. These projects will improve the quality and accessibility of data, enabling advanced AI applications. Case evaluation can help convince customers to share data by displaying the benefits that come with it.
Step 2: Building Confidence in AI Adoption
A risk-averse culture and the need to protect trade secrets are hindering the adoption of AI in the manufacturing industry. Additionally, manufacturers view AI as risky because it requires collecting and disseminating large amounts of data that can be susceptible to cyberattacks.
Businesses must begin to alleviate their fears by:
Low-risk experiments. Start with low-risk AI applications, such as in customer service, to build experience and trust.
Sandbox environments. Experimenting with production data in controlled environments can help manufacturers learn how to use machine learning models for tasks such as predictive maintenance.
To combat fear of AI, start with simple AI projects to gain hands-on experience. These early successes can build knowledge and confidence, paving the way for more complex applications. Creating a culture that embraces experimentation and learning will facilitate broader adoption of AI.
Step 3: Drive organizational transformation
Established manufacturing companies often operate within mature ecosystems lacking positive disruptive influences, leading to a scarcity of new innovation best practices. There is also a general lack of strategic priority to embrace change, which puts the success of AI adoption at risk. Together, these two factors hinder the evolution of technology.
Ways to start 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, specialist recruitment and retention strategies, and IT configurations enabling KPI measurement and data exchange.
Leadership commitment. Leaders must recognize the potential of a data-driven approach to driving innovative business models. It is essential to consider data as a strategic asset, just like manufactured products.
Ultimately, if businesses don’t prioritize AI adoption, they will fall behind. By making AI a high priority and centralizing efforts, manufacturers can make significant progress. And by focusing on these foundational steps – establishing a strong database, building assurance of AI adoption, and driving organizational transformation – manufacturers can embark on their AI journey with confidence.
Herbert Pesch is general manager Valtech B2B at Valtech.