Scaling Up Industrial AI: From Use Case to Production-Grade Solution

Disclaimer: This article is published in partnership with Siemens. Siemens is paying for my engagement, not for promotional purpose. Opinions are my own.

This is the third part of a multi-part series of articles discussing key issues in Industrial AI and Siemens’ role and activities in this transformative technology.

Artificial Intelligence (AI) has already shown great promise in the industrial sector, but in recent times it has been transitioning from small-scale to large-scale implementation. Various industries begin to leverage Industrial AI as a transformative force, with companies like Siemens taking the lead in integrating AI-based systems. While organizations may pilot localized, narrowly focused AI-based use cases, scaling up towards production-grade solutions across facilities can be complex. It entails overcoming challenges in infrastructure, data quality, AI adoption, closed-loop AI and ecosystem support, among others. This article elaborates on these key issues and aims to provide a more holistic view of the Industrial AI scaling journey.

Evolving Use Cases into Large-Scale Deployment

When it comes to Industrial AI deployments, use cases typically target specific areas. One of the prominent AI applications in manufacturing, for instance, is predictive maintenance. It uses AI models for predicting the probability of machinery breakdown and saving downtime while enhancing operational efficiency. Nonetheless, the essential adjustments required for evolving such a use case into a solution that can be scaled to run on various machines, at multiple sites and across different product lines tend to be considerable. According to recent research, the majority of about 70% of Industrial AI projects have been stuck in pilot mode and cannot be scaled up into production.

The real challenge is scaling algorithms designed for single machines or processes to entire supply chains or a network of factories. According to the Siemens whitepaper Next-Gen Industrial AI, scaling AI requires not only robust model design but also a deep understanding of operational contexts for AI recommendations to be reliable, consistent, and actionable across a diverse range of production environments. Stitching the various use cases together into a cohesive Industrial AI strategy demands cross-functional competencies in engineering, data science, and domain-specific knowledge, underscoring the need for more collaborative, multidisciplinary approaches.

Building Scalable Infrastructure

The infrastructure that is needed to scale AI in industrial organizations requires housing high-volume data processing, data storage, and real-time analytics. Traditional IT infrastructure can fall short of the data-intensive demands that AI places on an industrial environment. Cloud resources can become expensive quickly. Therefore, edge computing acts an increasingly important component in an industrial setting by providing real-time processing of data closer to where the data is being produced, such as sensors on machinery or production lines.

Scaling Industrial AI requires a shift from centralized data storage to hybrid models leveraging both cloud and edge capabilities. This shift implies that decisions can be made much quicker locally, which is important in maintaining efficiencies on the factory floor. A glimpse into scalable infrastructure solutions that integrate AI and IoT to facilitate real-time insights is offered through Siemens' Insights Hub (formerly MindSphere), the open cloud-based IoT operating system. However, large-scale installation of such infrastructure itself is not straightforward due to high costs involved, complex integration, and regular maintenance that is required between facilities.

AI Adoption and Democratization

The wide-scale adoption and democratization of AI among employees and other stakeholders is one of the significant barriers to AI scaling in industrial settings. Adopting AI has remained a top-down approach in many organizations, involving specialized data scientists and AI experts alone, who have been provided access to AI tools and platforms. This approach creates bottlenecks and limits the potential of AI in transforming broader processes. BCG has recently shown that organizations where efforts are being made to democratize AI have quicker adoption rates and realize greater business outcomes than others. Case in point: Siemens has strongly embraced this concept of empowering non-experts through Generative AI and has embedded capabilities into their operations to enable plant managers and technicians to make AI-informed decisions without having to understand machine learning models in great detail. Democratization of AI also means embedded user interfaces and training to take full advantage of the potential insights, a culture in which Industrial AI augments rather than disrupts conventional roles.

Closed-Loop AI Systems for Continuous Improvement

AI will be of highest value in the industrial context when being embedded in closed-loop systems to learn through permanent feedback and automatically optimize its behavior for best outcomes without costly human intervention. In many such scenarios - process optimization or quality control - closed-loop AI enables models to make continuous refinements of their recommendations themselves, based on real-time data and for greater accuracy. However, closed-loop AI at scale requires high computational resources and state-of-the-art data infrastructure. One great example has been Siemens' AI-based solutions that deploy closed-loop AI in finding defects in real time and making necessary adjustments in the parameters of production. However, closed-loop AI systems need proper mechanisms for addressing data privacy and cybersecurity concerns as well as ensuring compliance of data with regulatory requirements in industrial environments.

Applied Technologies and Methodologies

Effective scaling strategies are strongly dependent on the selection of appropriate AI technologies and methodologies. Typically, techniques such as federated learning, reinforcement learning, and transfer learning, among others, may be used to improve model performance without data privacy concerns. Federated learning, for instance, where training of AI models is carried out across decentralized data sources without centralizing data, turns out particularly useful in industrial settings where data security is key. According to IBM, only advanced security-aware AI techniques will scale and be compliant with data regulations. Siemens has embedded such methodologies into its AI toolkit to meet the specific requirements of its industrial customers. It can enable companies to deploy AI models that learn across distributed environments while remaining compliant with privacy regulations. Choosing and implementing the methodologies, however, demands to carefully weigh both the industry-specific requirements and the regulatory constraints - usually an underestimated aspect of most early-stage AI projects.

Supporting Ecosystems and Collaborative Models

Scaling Industrial AI in the industrial environment requires a supporting ecosystem. Such ecosystem partnering involves technology providers, research institutions, and regulatory bodies. An effective AI ecosystem will support innovation, enable knowledge exchange, and drive industry standards that will be essential for scaling AI across the sector. The collaboration between Siemens and NVIDIA serves as a great example: The partnership utilizes the powerful GPUs of NVIDIA coupled with the Siemens' domain expertise to speed up development of AI-powered applications targeted at industrial needs. Research reveals that industry collaboration can accelerate AI adoption significantly. Supporting ecosystems often feature open-source platforms and innovation hubs that facilitate co-creation and enable experimentation at low cost. Building those ecosystems takes up time and resources, but the result presents an indispensable prerequisite when organizations aspire to scale their AI initiatives.

Addressing Industry-Specific Challenges

Unlike in the tech sector, legacy systems, high standards of regulation, and safety requirements result in massive constraints for industrial companies in the process of upscaling AI. For instance, AI-driven machinery often operates in stringent environments, as can be seen in the pharmaceuticals and aerospace sectors. Failure of AI models in those sectors could lead to serious financial or safety repercussions, which makes companies wary about scaling up. AI adoption within regulated industries is all about careful testing and validation for safety and reliability. By addressing such issues, companies like Siemens have been working on developing AI validation frameworks that permit thorough testing and compliance with regulations prior to full deployment of AI applications. Those frameworks will guarantee not only that the regulatory standards are met, but also that models prove resilient and reliable under different operational conditions.

Future Directions for Scaling Industrial AI

The future of scaling AI in industrial settings is likely to shift toward more adaptive, autonomous systems. Advances in AI technologies - such as Generative AI and self-learning systems - offer new opportunities to tap gains in productivity and innovation. Whether this potential is unlocked depends first and foremost on continued investment in infrastructure, talent, and collaboration across the industry. A recent study by IDC projects that Industrial AI spending will increase annually by more than 25% in the coming five years, driven by demands for resilience and efficiency within the production environment. Therefore, every industrial company is supposed to develop a roadmap for AI scaling, supported by a long-term vision prioritizing adaptability and continuous improvement for competitive advantage.

Takeaway

Scaling Industrial AI comes with a specific set of challenges that goes far beyond the purely technical challenges: alignment in AI capabilities, infrastructure, workforce readiness, and supportive ecosystems. Siemens takes the lead through the deployment of AI solutions that are geared towards enhancing operational efficiency, resilience, compliance, and scalability across different industrial settings. Solving these challenges allows industrial organizations to unlock the true potential of AI, transform their operations, and point the way toward a new era of smart manufacturing.

Conclusion: The road to production-grade and large-scale Industrial AI is long and multi-dimensional, but companies that start with the major hurdles in mind will eventually clear them to thrive in an AI-driven future.

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