How AI Is Transforming Manufacturing

Ryan Spurr

We only need to look toward prominent Industry 4.0 models to see how artificial intelligence (AI) impacts the future of manufacturing. It’s baked into the roadmap and is arguably a vital part of the end state. Artificial intelligence can ingest a combination of data from sensors, machines, and people and then apply it to algorithms designed to optimize operations or achieve lights out manufacturing. 

Back in reality, we have some time before leading organizations achieve this pinnacle state of manufacturing, let alone the industry as a whole. Only about 9% of manufacturing organizations are leveraging Artificial Intelligence today. In the interim, what role does artificial intelligence play in industrial and process transformation? Are there business use cases that deliver reasonable value today? Do all artificial intelligence use cases require extensive expertise?

There are two routes an organization should consider in answering these questions. The first is to assess the readiness of a manufacturing organization’s ability to design and implement AI-based solutions in-house. The second is the ability to leverage AI-based solutions and expertise as part of commercial offerings. 

Most organizations lack the skillsets, scientists, data, and infrastructure readiness to pursue unique differentiating processes or solutions. Today, most manufacturing organizations have disconnected machines, people, and processes, all of which are not particularly suited to AI or machine learning (ML). One is more likely to find paper than a technical foundation for implementing and accelerating artificial intelligence. In this respect, the manufacturing trade has a long way to go—but don’t let that dissuade your organization from experimenting and investing in artificial intelligence. Like with other longer-term initiatives, it takes time to upskill employees, change the culture, and implement some of the underlying investments necessary to tackle artificial Intelligence.

While your organization may not be positioned to become the next Skynet, most manufacturers are surprised to find how quickly commercial solutions are adopting artificial intelligence to enhance or transform traditional manufacturing processes. The more our manufacturing practice digs into this topic, the more impressed we are by meaningful usage to deliver incremental or even game-changing business outcomes.

Let’s explore some of the readily available artificial intelligence and machine learning solutions used by manufacturers today:

Workplace Safety

One of the most adopted use cases has been leveraging artificial intelligence in workplace screening and safety, driven primarily by the pandemic. It’s possible to use AI to identify employees, conduct thermal screenings, or to monitor employee interactions for contact tracing and facility sanitization. The same technologies have also led to long-term solutions associated with workplace safety events before they happen or speeding up post-incident root cause analysis (for example, think slips, trips, and falls). These solutions lead to healthier employees, a safer workplace, and continued operations.

Machine Maintenance

All manufacturers strive to keep their facility and critical production equipment operational. AI/ML contributes significantly to modernizing maintenance management, moving it from a responsive or regular maintenance posture towards a predictive or prescriptive one. Reimagine a world where there is a 75% reduction in maintenance windows, 28% reduction in annual maintenance spend, and 30% reduction in parts maintenance. 

By combining sensors and machine data with artificial intelligence, maintenance managers can quickly identify both eminent failures and provide predictions of when failure may occur. Some manufacturers have taken maintenance management further with prescriptive maintenance. This approach combines predictive failure data with other business data (workloads, shift schedules, cost, and risk factors) to optimize the entire maintenance management lifecycle. Artificial intelligence is used to determine when to maintenance, how long equipment may operate without failure, prioritizing equipment maintenance, and even recommending spare part levels. Artificial intelligence makes it possible to increase uptime, reduce maintenance labor costs, and alter spare parts costs on your balance sheet.

Building Management and Physical Security

Many manufacturing organizations struggle to balance the costs and need for live security teams against protecting company assets and employees. Most forgo traditional physical security, leaving their facilities’ and employees’ safety at risk. With advancements in cameras, building management systems, and artificial intelligence, companies can now afford to implement security solutions that identify common security scenarios, such as guest or delivery vehicle arrival, theft, or active shooter.

Machine Vision

 Another area to broadly keep an eye on is artificial intelligence combined with machine vision. While some use cases are not new, the industry has seen rapid adoption in hundreds of proven use cases ranging from production, warehouse, and logistics optimization, quality inspection, and even fleet management. In addition to functionality, these solutions have also become affordable, leading to their rapid uptake.

In warehouse and logistics, we now see manufacturers using machine vision and AI to reduce transactions and increase capacity. For example, this alters how pallets are prepped, ensures customer orders are packed accurately, and reduces employee transactions by eliminating scanning. Such solutions can reduce up to 90% in pallet scanning time while also improving logistics throughput, increasing customer order accuracy, and reducing return rates.

Another area is fleet management, where companies have rapidly adopted the concept of in-dash cameras monitoring everything outside the vehicle from signs, other vehicles, pedestrians, and driving patterns. Most of these solutions optionally include in-cab monitoring and other safety features benefiting drivers, not just employers. These solutions are affordable with benefits including route and fuel optimization, insurance premium discounts, and reduction of at-fault incidents.

With heavy commercialization and affordability, these solutions are now accessible to any manufacturing organization and often self-fund returns in weeks or months.


With an ever-increasing number of devices and limited cybersecurity resources, we are leveraging artificial intelligence to help tackle the most considerable cybersecurity challenges. Operational technology environments produce massive amounts of security logs and data, along with their respective networks, security appliances, and applications. Artificial intelligence can help sift through the noise and assist by autonomously detecting intrusions, malware, fraud, employee behaviors outside normal baselines, and ultimately elevating threat intelligence.

As you can tell, artificial intelligence is being adopted quickly within commercial solutions across the entire value chain of the manufacturing industry. These AI solutions unlock immediate opportunities to implement and deliver against top and bottom-line objectives and steer their companies towards more advanced use cases down the road.

In fact, among manufacturing leaders, 34% are investing in artificial Intelligence and 19% in machine learning-based initiatives to augment their workforce, solve critical challenges, and start their organizations on a long-term transformation. Even 16% CFOs view artificial Intelligence as playing a crucial role in business results, putting it in third place behind only cloud computing and the Internet of things (IoT). 

Combining AI/ML with other technologies such as sensors, machines, and human inputs will dramatically improve operations and likely lead to new forms of innovation and productivity in the industry. While your organization may not possess the skillsets necessary, don’t let that prevent you from investing in commercial solutions that can jumpstart your AI/ML journey.

Connection’s Manufacturing Practice is passionate about industrial transformation and works to establish a portfolio of solutions to meet your organization’s challenges. We have a range of artificial intelligence and machine learning solutions to solve manufacturing challenges today.

To learn more about Connection’s Manufacturing Practice, or to discuss the challenges and solutions highlighted in this article, contact us today.

Ryan Spurr is the Director of Manufacturing Strategy at Connection with 20+ years of experience in manufacturing, information technology, and portfolio leadership. He leads the Connection Manufacturing Practice, go-to-market strategy, client engagement, and advisory services focusing on operational technology (OT) and information technology that make manufacturers more digitally excellent.