The future is now: Implementing AI in manufacturing

AI in manufacturing: worker in a safety helmet and jacket working on a laptop next to a robotic machine

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In today's hypercompetitive landscape, manufacturers face unprecedented pressure to innovate while optimising operations and driving new revenue streams. AI can help solve that conundrum.

New manufacturing research from ServiceNow reveals that manufacturers allocated 12.7% of their IT budgets to AI in the last fiscal year and that 82% plan to increase their AI investments in the next fiscal year.

Table of contents

What is AI in manufacturing?

AI in manufacturing combines machine learning, industrial internet of things (IIoT), predictive analytics, and workflow automation into a unified digital ecosystem to transform how products are designed, produced, and delivered. It bridges physical operations and digital intelligence, creating seamless connections between machines, people, and processes.

Manufacturing AI can transform raw production data into actionable intelligence that can help predict maintenance needs, optimise production processes, and make data-driven decisions with speed and accuracy. In so doing, it helps enable real-time decisions that were previously impossible at scale.

Agentic AI—autonomous systems that can reason, plan, and execute complex tasks with minimal human supervision—offers great promise in the industry. Intelligent AI agents can independently solve complex manufacturing challenges, orchestrate workflows across disparate systems, and continuously learn from their environment.

More than 20% of manufacturers have already deployed agentic AI solutions, according to our research, and another 45% plan to adopt agentic AI within the next 12 months.

Manufacturing AI can transform raw production data into actionable intelligence that can help predict maintenance needs, optimise production processes, and make data-driven decisions with speed and accuracy.

How can AI help in manufacturing?

AI empowers manufacturers to break through long-standing barriers of efficiency, quality, and innovation, transforming manufacturing from reactive to proactive.

Accelerated design and engineering

AI-empowered product development helps manufacturers streamline the creation process, reducing time to market for new products.

Enhanced predictive maintenance

AI algorithms analyse equipment data to predict failures before they occur, transforming maintenance from reactive to predictive. This shift can dramatically reduce downtime while extending machine lifespans.

Intelligent quality control

Computer vision systems inspect products with extraordinary accuracy, detecting microscopic defects invisible to the human eye. These systems learn continuously and become more effective with each product examined, expediting the ability to identify, contain, and resolve quality issues even after a product leaves the factory.

Optimised supply chain

AI models analyse thousands of variables simultaneously—from weather patterns to geopolitical events—to predict supply chain disruptions before they affect production. This foresight helps improve supplier collaboration and reduce supplier risk.

Streamlined production

Advanced AI hones production parameters in real time, balancing quality, speed, and resource utilisation with precision that’s impossible through manual adjustments alone.

Boosted cost savings

By optimising production processes to reduce energy consumption and waste and by predicting equipment failures to minimise unplanned downtime, AI can help drive costs out of the business.

Improved operational technology management

By providing visibility into and context around manufacturing operational technology environments, AI can help reduce cyber risk and downtime.

Digitised sales and service

AI-powered customer relationship management (CRM) for manufacturing can speed up order to cash, dealer support, and customer service, boosting revenue and improving customer experiences while reducing the cost to serve.

Helmeted man looking at a laptop in his hands in front of manufacturing machinery

Where is AI used in manufacturing?

The transformative impact of AI spans the entire manufacturing value chain. In fact, 57% of manufacturers in our study implemented more than 100 use cases in the past year, including:

Customer sales and service

Production and quality control

Supply chain management

Will manufacturing jobs be replaced by AI?

AI will not replace manufacturing jobs, but it will transform the industry. The future of manufacturing is about augmenting human workers’ capabilities and redefining their roles.

AI excels at handling repetitive, dangerous, and precision-critical tasks. Using AI in these areas can free human workers to focus on innovation, complex problem-solving, and customer relationships. This creates more engaging, higher-value roles while simultaneously improving productivity and safety.

The most successful manufacturers approach AI as a collaboration between human expertise and machine intelligence. They focus on reskilling their workforce for this new paradigm, recognising that their people remain their greatest asset in an AI-powered world.

Pacesetters—organisations in our study that scored highest in AI maturity—are more likely than other businesses to invest in comprehensive AI training programs. In fact, 77% of Pacesetters have implemented training programs to actively develop needed AI skills, compared to 56% of other manufacturers.

What are the benefits of AI in manufacturing?

The benefits of AI in manufacturing are substantial and measurable. They include:

Increased operational efficiency: Manufacturing Pacesetters achieve 1.75 times greater efficiency through AI-powered process optimisation and intelligent automation.

Enhanced product quality: AI-powered quality control systems detect defects with extraordinary accuracy, reducing waste and warranty claims while enhancing customer satisfaction.

Accelerated innovation: By analysing vast amounts of production and market data, AI helps manufacturers identify new product opportunities and optimise designs before physical prototyping begins. Pacesetters report 1.58 times faster innovation.

Supply chain resilience: AI forecasting models help manufacturers anticipate disruptions and adapt quickly, maintaining production continuity despite external challenges.

Sustainability improvements: AI optimises resource utilisation and energy consumption, helping manufacturers reduce their environmental footprint while improving profitability.

Margin enhancement: Perhaps most compelling, manufacturers realised an average 7.6% increase in gross margins from their AI investments over the past year, with Pacesetters achieving a 10.3% boost.

By unifying AI, data, and workflows on a single platform, you can transform your operations today while building the foundation for tomorrow's innovations.

How to implement AI in manufacturing

Implementing AI in manufacturing requires a strategic approach focused on value creation rather than technology for its own sake.

  1. Start with clear business objectives: Begin with specific business challenges where AI can deliver measurable value. Avoid the temptation to implement AI without clear performance metrics. Pacesetters are 1.72 times more likely than other organisations to operate with a clear, shared AI vision for business transformation.
  2. Embrace a platform approach: 70% of Pacesetters prefer a comprehensive platform with built-in AI capabilities over point solutions. An enterprise-grade platform provides the integration capabilities necessary for AI to access and analyse data across your entire operation.
  3. Prioritise data governance: AI is only as good as the data that powers it. Our research shows that 53% of Pacesetters have implemented formal data governance programs, compared to 43% of other manufacturers. This focus on data quality translates directly to AI performance.
  4. Build cross-functional AI teams: Successful AI implementation requires both technical expertise and deep domain knowledge. Create teams that blend data scientists with manufacturing experts to ensure AI solutions address real operational challenges.
  5. Develop your AI talent pipeline: The AI skills gap is real and growing. Leading manufacturers are investing in both hiring specialised talent and upskilling their existing workforce to thrive in an AI-powered environment.
  6. Begin small with a focus on agility: The AI revolution waits for no one. Start with small, focused pilots to learn and adapt rapidly, and then scale successful strategies across the business.

The manufacturing AI imperative

AI in manufacturing isn't a distant future—it's today's competitive reality. The path forward requires both technology implementation and a fundamental reimagining of how your manufacturing enterprise operates in an AI-powered world.

By unifying AI, data, and workflows on a single platform, you can transform your operations today while building the foundation for tomorrow's innovations. The future belongs to manufacturers who view AI not as a cost centre, but as their greatest opportunity for transformation.

Find out how ServiceNow can help you put AI to work in manufacturing.