Below are some of the key findings from the report around AI ambitions, key challenges, and strategies to successfully implement AI. For complete insights, we invite you to download the full report.\u00a0<\/p>\n\n\n\n
AI ambitions: Investment, top use cases, and expectations<\/h2>\n\n\n\nInvestment: Major players leading the way<\/h4>\n\n\n\n
The transformative power of AI in the manufacturing sector is not just a future prospect, it\u2019s an unfolding reality. 77% of surveyed firms with more than USD10 billion in annual revenue are already deploying AI use cases. Engineering and design executives are particularly proactive with 58% anticipating an increase in AI spending by more than 10% within the next two years. This surge in investment is mirrored in factory operations, where 43% of leaders anticipate a similar increase, a testament to the industry\u2019s interest in AI\u2019s potential to drive innovation and efficiency.\u00a0<\/p>\n\n\n\n
Top use cases: AI application from design to factory floor<\/h4>\n\n\n\n
Use cases are where “the rubber meets the road” for AI adoption. Pavandeep Kalra, Chief Technology Officer of AI for Microsoft Cloud for Industry, emphasizes that, \u201cThe barriers to AI use-case development are falling\u201d as generative AI foundational models are reducing the requirement for industry-specific customizations. Among the survey sample, the AI use cases most likely to have progressed through production involve product design, conversational AI (chatbots), and content creation. On the factory floor, the focus is on process optimization, quality control, and asset maintenance use cases.\u00a0<\/p>\n\n\n\n
Expectations: Unlocking speed, efficiency, and innovation\u00a0<\/h4>\n\n\n\n\n\t\n\t\t\n\t\t\tMicrosoft AI Solutuions\t\t<\/h2>\n\t\t
\n\t\t\t\t\t\t\t\n\t\t\t\t\t\tLearn more\t\t\t\t\t\t\t\u2197<\/a>\n\t\t\t\t\t<\/p>\n\t<\/div>\n<\/div>\n\n\n\nThe potential for AI to deliver impact in the realm of engineering and design is becoming increasingly apparent with more than 40% of executives aiming to use AI to enhance speed and efficiency and minimize product failures. The experience of Gunaranjan Chaudhry, Director of Data Science at Symphony Industrial AI, exemplifies the transformative effect AI can have on the design process. He notes, <\/p>\n\n\n\n
\n\u201cAI speeds up the process by homing in on the specific parameters that you need to focus on. We\u2019ve had design cycles being cut from 12 months to less than six months.\u201d <\/em><\/p>\nGunaranjan Chaudhry, Director of Data Science at Symphony Industrial AI<\/cite><\/blockquote>\n\n\n\nIn factory operations, AI is expected to unlock innovation in production and assembly process, improve safety, and help reduce carbon footprint.\u00a0<\/p>\n\n\n\n
\n\t\n\n\t\n\t\t\n\t\t\t\n\t\t\t\tTaking AI to the Next Level in Manufacturing<\/h2>
Gain insights on AI adoption in the manufacturing industry<\/p>
Download the report<\/a><\/div><\/div>\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t![\"\"](\"https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/2024\/04\/MSC17_tubeForgingPlant_009.jpg\")
\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t\t<\/div>\n<\/div>\n<\/div>\n\n\n\nKey challenges to scaling AI in the manufacturing sector<\/h2>\n\n\n\nTalent, skills, and data: The triad of AI scaling challenges\u00a0<\/h4>\n\n\n\n\n\t\n\t\t\n\t\t\tAi Learning hub\t\t<\/h2>\n\t\t
\n\t\t\t\t\t\t\t\n\t\t\t\t\t\tGet familiar with the AI learning journey\t\t\t\t\t\t\t\u2197<\/a>\n\t\t\t\t\t<\/p>\n\t<\/div>\n<\/div>\n\n\n\nOne of the main challenges in scaling AI use cases is the deficit of talent, skills, and data. Manufacturers cite this as their toughest challenge in both design and engineering and factory operations. Inadequate data quality and governance also hamper use-case development in both areas. Additionally, insufficient access to cloud-based compute power is another constraint in design and engineering. AI model governance is expected to be one of the big challenges on the factory floor, with 61% of manufacturers that have begun deploying AI use cases citing governance<\/a> as a major data challenge. This underscores the difficulties in maintaining high standards of data quality, consistency, and compliance within the complex and variable manufacturing settings.<\/p>\n\n\n\nCritical need for data unification and contextualization\u00a0<\/h4>\n\n\n\n
Scaling AI use cases can be hindered without solid data foundations, as respondents have identified inadequate data quality (57%), weak data integration (54%), and insufficient governance (47%) as significant challenges. Regrettably, only approximately one in five manufacturers have production assets with data prepared for utilization in current AI models. This issue is further exacerbated as manufacturers transition use cases into production. Notably, larger manufacturers tend to encounter a more pronounced challenge with unsuitable data.\u00a0<\/p>\n\n\n\n
Reducing fragmentation for AI to flourish\u00a0<\/h4>\n\n\n\n
To effectively scale AI, data fragmentation must be minimized. Most manufacturers will need to update their data architecture, infrastructure, and processes to gain full benefits from AI<\/strong>, as well as other technological and business objectives. Prioritizing a modernization strategy that enhances interoperability between engineering and design and factory data systems, as well as between operational technology (OT) and information technology (IT), is crucial.\u00a0<\/p>\n\n\n\nStrategies to successfully scale AI\u00a0<\/h2>\n\n\n\n\n\t\n\t\t\n\t\t\tThe AI Strategy Roadmap: Navigating the stages of value creation\t\t<\/h2>\n\t\t
\n\t\t\t\t\t\t\t\n\t\t\t\t\t\tRead the blog\t\t\t\t\t\t\t\u203a<\/a>\n\t\t\t\t\t<\/p>\n\t<\/div>\n<\/div>\n\n\n\nAs manufacturers worldwide progress in their digital transformation journeys, data serves as the crucial enabler. To harness the full potential of AI, it is imperative to establish and maintain data quality, integration, and governance throughout the entire value chain, extending from design and production to distribution. Bridging the divide between OT and IT systems, which frequently function in isolation and adhere to disparate protocol and stands, is essential. By cultivating a more cohesive and consistent data landscape, manufacturers can expedite their AI integration and optimize their use case scalability. As manufacturers continue to tap into the transformative power of AI, here are some essential considerations.<\/p>\n\n\n\n
Start with the business case<\/h4>\n\n\n\n
Companies should begin by identifying the business problem or challenge that technology, including AI, could address. Only then should technology solutions be considered. \u201cAsking \u2018what can we do with AI?\u2019 can generate lots of great ideas,\u201d says Philippe Rambach, Chief AI Officer of Schneider Electric, \u201cbut most will have limited impact if they don\u2019t start with the actual business need.\u201d\u00a0\u00a0<\/p>\n\n\n\n
Embrace structural flexibility<\/h4>\n\n\n\n
In this evolving landscape, use-case development extends beyond the realm of AI experts. It has become a multidisciplinary endeavor as internal expertise is harnessed alongside data science and engineering teams, in collaboration with business product owners and IT professionals. Such a unified approach increases the chances of successful AI use-case development and deployment.\u00a0<\/p>\n\n\n\n
Prepare the data foundation<\/h4>\n\n\n\n
AI thrives in a mature data environment. To achieve this, manufacturers can strategically adopt modular updates and employ data virtualization techniques to incrementally enhance their systems and align their data architecture with AI capabilities without immediate, extensive change. It\u2019s important to assess your organization’s data collection, storage, and processing capabilities, addressing any identified weaknesses before deploying AI use cases. This may involve consolidating data repositories where feasible. While AI models necessitate high-quality data, perfection is not a prerequisite for moving use cases into production.\u00a0<\/p>\n\n\n\n
Leverage AI for skill development<\/h4>\n\n\n\n
It’s natural for manufacturers to be concerned about the lack of AI skills and talent, but they should recognize that AI can aid in cultivating these skills within their workforce. Generative AI, for instance, simplifies the process for engineers and non-IT staff to engage with models. AI can also enhance the problem-solving abilities of production personnel.\u00a0<\/p>\n\n\n\n
Take AI to the next level\u00a0<\/h2>\n\n\n\n
AI holds the potential to fundamentally transform the manufacturing sector, bringing about unparalleled opportunities for innovation, productivity, and environmental sustainability. To gain a deeper understanding of these insights and explore practical strategies for scaling AI use cases, download the full \u201cMIT Technology Review Insights: Taking AI to the Next Level in Manufacturing<\/strong><\/a>\u201d report today.\u00a0<\/p>\n\n\n\n
\n\t\t\tMicrosoft AI Solutuions\t\t<\/h2>\n\t\t
\n\t\t\t\t\t\t\t The potential for AI to deliver impact in the realm of engineering and design is becoming increasingly apparent with more than 40% of executives aiming to use AI to enhance speed and efficiency and minimize product failures. The experience of Gunaranjan Chaudhry, Director of Data Science at Symphony Industrial AI, exemplifies the transformative effect AI can have on the design process. He notes, <\/p>\n\n\n\n \u201cAI speeds up the process by homing in on the specific parameters that you need to focus on. We\u2019ve had design cycles being cut from 12 months to less than six months.\u201d <\/em><\/p>\nGunaranjan Chaudhry, Director of Data Science at Symphony Industrial AI<\/cite><\/blockquote>\n\n\n\n In factory operations, AI is expected to unlock innovation in production and assembly process, improve safety, and help reduce carbon footprint.\u00a0<\/p>\n\n\n\n Gain insights on AI adoption in the manufacturing industry<\/p> \n\t\t\t\t\t\t\t One of the main challenges in scaling AI use cases is the deficit of talent, skills, and data. Manufacturers cite this as their toughest challenge in both design and engineering and factory operations. Inadequate data quality and governance also hamper use-case development in both areas. Additionally, insufficient access to cloud-based compute power is another constraint in design and engineering. AI model governance is expected to be one of the big challenges on the factory floor, with 61% of manufacturers that have begun deploying AI use cases citing governance<\/a> as a major data challenge. This underscores the difficulties in maintaining high standards of data quality, consistency, and compliance within the complex and variable manufacturing settings.<\/p>\n\n\n\n Scaling AI use cases can be hindered without solid data foundations, as respondents have identified inadequate data quality (57%), weak data integration (54%), and insufficient governance (47%) as significant challenges. Regrettably, only approximately one in five manufacturers have production assets with data prepared for utilization in current AI models. This issue is further exacerbated as manufacturers transition use cases into production. Notably, larger manufacturers tend to encounter a more pronounced challenge with unsuitable data.\u00a0<\/p>\n\n\n\n To effectively scale AI, data fragmentation must be minimized. Most manufacturers will need to update their data architecture, infrastructure, and processes to gain full benefits from AI<\/strong>, as well as other technological and business objectives. Prioritizing a modernization strategy that enhances interoperability between engineering and design and factory data systems, as well as between operational technology (OT) and information technology (IT), is crucial.\u00a0<\/p>\n\n\n\n \n\t\t\t\t\t\t\t As manufacturers worldwide progress in their digital transformation journeys, data serves as the crucial enabler. To harness the full potential of AI, it is imperative to establish and maintain data quality, integration, and governance throughout the entire value chain, extending from design and production to distribution. Bridging the divide between OT and IT systems, which frequently function in isolation and adhere to disparate protocol and stands, is essential. By cultivating a more cohesive and consistent data landscape, manufacturers can expedite their AI integration and optimize their use case scalability. As manufacturers continue to tap into the transformative power of AI, here are some essential considerations.<\/p>\n\n\n\n Companies should begin by identifying the business problem or challenge that technology, including AI, could address. Only then should technology solutions be considered. \u201cAsking \u2018what can we do with AI?\u2019 can generate lots of great ideas,\u201d says Philippe Rambach, Chief AI Officer of Schneider Electric, \u201cbut most will have limited impact if they don\u2019t start with the actual business need.\u201d\u00a0\u00a0<\/p>\n\n\n\n In this evolving landscape, use-case development extends beyond the realm of AI experts. It has become a multidisciplinary endeavor as internal expertise is harnessed alongside data science and engineering teams, in collaboration with business product owners and IT professionals. Such a unified approach increases the chances of successful AI use-case development and deployment.\u00a0<\/p>\n\n\n\n AI thrives in a mature data environment. To achieve this, manufacturers can strategically adopt modular updates and employ data virtualization techniques to incrementally enhance their systems and align their data architecture with AI capabilities without immediate, extensive change. It\u2019s important to assess your organization’s data collection, storage, and processing capabilities, addressing any identified weaknesses before deploying AI use cases. This may involve consolidating data repositories where feasible. While AI models necessitate high-quality data, perfection is not a prerequisite for moving use cases into production.\u00a0<\/p>\n\n\n\n It’s natural for manufacturers to be concerned about the lack of AI skills and talent, but they should recognize that AI can aid in cultivating these skills within their workforce. Generative AI, for instance, simplifies the process for engineers and non-IT staff to engage with models. AI can also enhance the problem-solving abilities of production personnel.\u00a0<\/p>\n\n\n\n AI holds the potential to fundamentally transform the manufacturing sector, bringing about unparalleled opportunities for innovation, productivity, and environmental sustainability. To gain a deeper understanding of these insights and explore practical strategies for scaling AI use cases, download the full \u201cMIT Technology Review Insights: Taking AI to the Next Level in Manufacturing<\/strong><\/a>\u201d report today.\u00a0<\/p>\n\n\n\n\n
Taking AI to the Next Level in Manufacturing<\/h2>
\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t\t<\/div>\n<\/div>\n<\/div>\n\n\n\nKey challenges to scaling AI in the manufacturing sector<\/h2>\n\n\n\n
Talent, skills, and data: The triad of AI scaling challenges\u00a0<\/h4>\n\n\n\n
\n\t\t\tAi Learning hub\t\t<\/h2>\n\t\t
Critical need for data unification and contextualization\u00a0<\/h4>\n\n\n\n
Reducing fragmentation for AI to flourish\u00a0<\/h4>\n\n\n\n
Strategies to successfully scale AI\u00a0<\/h2>\n\n\n\n
\n\t\t\tThe AI Strategy Roadmap: Navigating the stages of value creation\t\t<\/h2>\n\t\t
Start with the business case<\/h4>\n\n\n\n
Embrace structural flexibility<\/h4>\n\n\n\n
Prepare the data foundation<\/h4>\n\n\n\n
Leverage AI for skill development<\/h4>\n\n\n\n
Take AI to the next level\u00a0<\/h2>\n\n\n\n