[{"@context":"https:\/\/schema.org\/","@type":"BlogPosting","@id":"https:\/\/www.the-future-of-commerce.com\/2025\/02\/03\/ai-in-manufacturing-lessons\/#BlogPosting","mainEntityOfPage":"https:\/\/www.the-future-of-commerce.com\/2025\/02\/03\/ai-in-manufacturing-lessons\/","headline":"AI in manufacturing: Formula for AI success is revealed via industry lessons learned","name":"AI in manufacturing: Formula for AI success is revealed via industry lessons learned","description":"As industrial manufacturers scale artificial intelligence in their operations, they're applying lessons learned that other industries can use to guide their own AI transformation.","datePublished":"2025-02-03","dateModified":"2025-02-03","author":{"@type":"Person","@id":"https:\/\/www.the-future-of-commerce.com\/contributor\/judy-cubiss\/#Person","name":"Judy Cubiss","url":"https:\/\/www.the-future-of-commerce.com\/contributor\/judy-cubiss\/","identifier":868,"image":{"@type":"ImageObject","@id":"https:\/\/secure.gravatar.com\/avatar\/572cbc68c66c360236d1837f055bceb9067da710af55051b7c6ebae40cd519ce?s=96&d=mm&r=g","url":"https:\/\/secure.gravatar.com\/avatar\/572cbc68c66c360236d1837f055bceb9067da710af55051b7c6ebae40cd519ce?s=96&d=mm&r=g","height":96,"width":96}},"publisher":{"@type":"Organization","name":"The Future of Commerce","logo":{"@type":"ImageObject","@id":"https:\/\/www.the-future-of-commerce.com\/wp-content\/uploads\/2023\/01\/logo-foc-schema-app-1.png","url":"https:\/\/www.the-future-of-commerce.com\/wp-content\/uploads\/2023\/01\/logo-foc-schema-app-1.png","width":172,"height":60}},"image":{"@type":"ImageObject","@id":"https:\/\/www.the-future-of-commerce.com\/wp-content\/uploads\/2025\/02\/AI-manufacturing-success-FTR.jpg","url":"https:\/\/www.the-future-of-commerce.com\/wp-content\/uploads\/2025\/02\/AI-manufacturing-success-FTR.jpg","height":375,"width":1200},"url":"https:\/\/www.the-future-of-commerce.com\/2025\/02\/03\/ai-in-manufacturing-lessons\/","about":["Industrial Manufacturing",{"@type":"Thing","@id":"https:\/\/www.the-future-of-commerce.com\/commerce\/industries\/","name":"Industries","sameAs":["https:\/\/en.wikipedia.org\/wiki\/Industry","http:\/\/www.wikidata.org\/entity\/Q2976602"]},"Industries",{"@type":"Thing","@id":"https:\/\/www.the-future-of-commerce.com\/commerce\/intelligent-enterprise\/","name":"Intelligent Enterprise","sameAs":["https:\/\/en.wikipedia.org\/wiki\/Intelligent_enterprise","http:\/\/www.wikidata.org\/entity\/Q6044119"]}],"wordCount":926,"keywords":["AI (Artificial Intelligence)","B2B Industries","Industrial Internet of Things","Industrial Manufacturing"],"articleBody":"Industrial manufacturing isn\u2019t just figuring out how to scale artificial intelligence\u2014it\u2019s got a serious head start.This is the sector that\u2019s already been through a digital transformation with Industry 4.0, connecting devices, integrating IoT, and turning data into actionable insights. They\u2019ve spent years navigating the messy realities of digitization, and now they\u2019re applying those lessons to AI in manufacturing.But research in a new SAP industry report shows that while manufacturers’ hard-earned expertise in data, integration, and scaling can help other sectors avoid common pitfalls and accelerate their own AI transformation, only 16% of IM businesses as have integrated AI so far, versus 25% across all industries.That\u2019s a fascinating finding. Does it show a lack of urgency? If so, why? The barriers to AI adoption in industrial manufacturing are lower than in most industries\u2014but perhaps it comes down to transformation fatigue as much as anything else that\u2019s causing them to go carefully. And that\u2019s precisely why their journey offers such a valuable blueprint.AI in manufacturing: A familiar challenge with new stakesFor industrial manufacturers, AI feels like d\u00e9j\u00e0 vu. Scaling AI brings back many of the same hurdles they faced during the Industry 4.0 revolution: fragmented data, legacy systems, and workforce scepticism. Yet, having tackled these challenges before, they know where to focus their energy.Take data, for instance. AI is only as good as the data it\u2019s fed, and manufacturers have learned that messy, inconsistent inputs lead to bad outcomes. While transforming to Industry 4.0, they invested heavily in cleaning, standardizing, and integrating data streams from IoT sensors and production lines. That groundwork is now paying off, giving them a clear edge in scaling AI.For industries just starting out, this lesson is crucial: before you scale, you have to clean house.Then there\u2019s the question of systems. Industrial manufacturing\u2019s reliance on mission-critical legacy infrastructure means that replacing old systems isn\u2019t always an option.Instead, they\u2019ve become experts in building bridges between old and new technologies. It\u2019s not the flashiest approach, but it works\u2014and it\u2019s a reminder to other sectors that integration is often more practical (and less risky) than starting from scratch.      Manufacturing trends 2025: Transforming industry with AI, digitalization, and sustainability                Manufacturers will kick digital transformation into high gear in 2025 as they adopt advanced technologies like AI to build resiliency and drive growth.      How AI is changing the game in manufacturingIf Industry 4.0 was about connecting the dots, AI is about predicting what\u2019s next. With AI, the manufacturing industry is\u00a0 already seeing game-changing results across its operations.AI isn\u2019t just making processes faster\u2014it\u2019s making them smarter:Predictive maintenance: Analyzing sensor data to forecast equipment failures, preventing costly downtime.Quality assurance: Using AI-powered vision systems to catch defects on production lines in real time.Energy management: Optimizing energy use by predicting demand, improving both sustainability and cost efficiency.These applications aren\u2019t just solving operational headaches\u2014they\u2019re delivering tangible value. And while they\u2019re rooted in manufacturing, the logic behind them is universal. Every industry has bottlenecks that could benefit from smarter, data-driven solutions.3 lessons for other industriesThe challenges manufacturers face with AI are the same ones everyone else is grappling with. Data silos, disconnected systems, and workforce readiness are barriers across the board.But industrial manufacturing’s experience offers a playbook for navigating these hurdles:Data quality can\u2019t be an afterthought. Manufacturers know that garbage in means garbage out, and they\u2019ve invested in cleaning up their data streams before scaling AIPrioritize integration over disruption. This ensures that new technology complements existing systems rather than replacing them outright.Start small. Focusing on targeted AI applications like logistics optimization or inventory management create momentum and builds trust across the organization.      Data analytics in manufacturing: How mill products companies get to the truth                The truth is hard to find, but data is helping mill products manufacturers like Steinbeis uncover truths that build trust with partners and customers while bolstering the bottom line.      The road ahead: AI\u2019s evolutionIndustrial manufacturing’s journey with AI highlights three steps of adoption, each building on the last:Automation: The first step, where repetitive tasks are streamlined to improve efficiency.Process transformation: AI starts rethinking how operations work, making them smarter and more adaptable.Autonomy: The ultimate goal, where AI systems manage workflows independently, reacting to real-time changes without human intervention.Most industries are currently somewhere between automation and transformation. But as seen in the manufacturing industry, autonomy isn\u2019t as far off as it once seemed\u2014it\u2019s the natural progression for businesses willing to invest in scaling AI.The bigger pictureHere\u2019s the bottom line: AI isn\u2019t just another tool\u2014it\u2019s a strategic shift. Manufacturing’s journey through Industry 4.0 has shown that transformation isn\u2019t a single project; it\u2019s a mindset.Scaling AI requires patience, persistence, and a willingness to iterate. But the rewards\u2014greater efficiency, smarter decision-making, and enhanced customer value\u2014are worth the effort.The lessons learned by manufacturers are clear: focus on the fundamentals, embrace integration, and aim for continuous improvement. AI has the power to redefine industries\u2014but only if it\u2019s implemented thoughtfully.So, are you ready to take AI from pilot mode to production? The tools are here, the roadmap is clear, and the time to act is now.  38% of manufacturing execs want to increase market share.36% want to increase revenue.34% want to increase margins.Get the manufacturing research, stats, and pain-point solutions\u00a0HERE."},{"@context":"https:\/\/schema.org\/","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"2025","item":"https:\/\/www.the-future-of-commerce.com\/2025\/#breadcrumbitem"},{"@type":"ListItem","position":2,"name":"02","item":"https:\/\/www.the-future-of-commerce.com\/2025\/\/02\/#breadcrumbitem"},{"@type":"ListItem","position":3,"name":"03","item":"https:\/\/www.the-future-of-commerce.com\/2025\/\/02\/\/03\/#breadcrumbitem"},{"@type":"ListItem","position":4,"name":"AI in manufacturing: Formula for AI success is revealed via industry lessons learned","item":"https:\/\/www.the-future-of-commerce.com\/2025\/02\/03\/ai-in-manufacturing-lessons\/#breadcrumbitem"}]}]