To truly make AI work wonders in manufacturing, it's not just about the tech itself. We also need to deeply understand our processes, empower our people, and connect our systems.
Artificial Intelligence (AI) holds immense promise for the manufacturing sector, offering unprecedented efficiency, groundbreaking insights, and a competitive edge. However, despite the initial potential demonstrated in pilot projects, many AI initiatives struggle to scale up and deliver sustainable ROI. The stark reality is highlighted by IDC research, which reveals that nearly 90% of AI pilots fail to transition to full production. This failure can often be attributed to a lack of integration into the daily operational fabric of enterprises, leaving AI initiatives in 'pilot purgatory'. To learn more about how AI and digital transformation can work together to overcome these challenges, explore our insights on AI and Digital Transformation: A Perfect Match.
For AI and hyperautomation to be effective, it is crucial to have an exhaustive, interconnected view of the entire operational landscape. This includes understanding the 'as-is' workflows, human decision-making processes, technology stacks, governance rules, risk and compliance mandates, and how all these elements align with overarching business strategies. Without this comprehensive understanding, deploying AI algorithms or automating complex workflows is likely to encounter significant hurdles. To gain deeper insights into the ethical considerations of AI implementation and the importance of process transparency, read our article on The Ethical Imperative: Why We Can't Automate What We Don't Understand.
To achieve process transparency, organizations can leverage the APQC’s Seven Tenets of Process Management. These tenets provide a structured approach to understanding and managing processes, ensuring alignment with organizational goals. These tenets include Strategic Alignment, Governance, Change Management, Process Models, Process Performance, Process Improvement, and Tools and Technology. According to APQC's research, 97% of organizations prioritize process management, indicating its critical role in future-proofing businesses.
Gartner's concept of the Digital Twin of an Organization (DTO) is a strategic enabler that can help overcome the hurdles of AI scalability. A DTO provides a dynamic software model and digital blueprint of an organization, based on operational data and contextual information. It visualizes and understands how an organization operates, responds to changes, and deploys resources. This integrated view is essential for transforming experimental AI into a driver of profound digital transformation. To learn more about implementing a DTO and future-proofing your business, check out our guide on The Digital Twin Advantage: Future-Proof Your Business in 7 Steps.
To ensure successful integration of AI in manufacturing, it is vital to focus on data quality and process transparency. Strategies include conducting thorough process documentation, engaging stakeholders through effective change management, and ensuring continuous feedback loops for improvement. Leveraging tools such as Mavim's Intelligent Transformation Platform can facilitate this process by providing a centralized platform for process design, mining, simulation, and automation, seamlessly integrated with Microsoft tools like Dynamics 365, Power BI, and Azure. For more information on how to optimize your IT infrastructure management using digital twins, explore our article on Optimizing IT Infrastructure Management with Digital Twins.