Global events of the last three years have made it increasingly difficult for the manufacturing industry to operate competitively. When the COVID-19 pandemic hit in the second quarter of 2020, manufacturing output fell by an annual rate of 43%, and hours worked by 38% — the largest declines since World War II. But new technology, in the form of artificial intelligence, has the potential to revitalize the industry.

From product design and development to final delivery, AI is already being utilized in modern manufacturing factories to streamline production processes, contributing to the industry’s 2.5% GDP growth rate in 2023. AI techniques are being employed today to identify defects and other product issues, enable predictive equipment maintenance, and facilitate shipping and tracking, among other applications. Yet integrating AI into an existing operation is no small feat, and many manufacturers need help knowing where to start.

Following are six steps that manufacturers can take to navigate the AI journey.

Identify the top challenges you’re facing. It’s critical to be selective in defining the scope of your AI project, starting with a “wish list” of problems or challenges to resolve. This process could look like identifying the data that needs to be collected, relevant software and algorithms, and metrics for tracking project success. By explicitly determining this scope, companies can ensure alignment among stakeholders, developers and users around project objectives, and provide a base for measuring the value of the implemented AI system. Goals should be focused, measurable and aligned with larger needs across the organization.

Thoroughly assess your data. Make sure you have access to high-quality data that can be cleaned and structured for AI purposes. It’s essential that the data accurately represents the real-world manufacturing process. While it’s easy for humans to disregard implausible values, flawed data can easily skew AI models. Curating your data might seem like a tedious task, but it’s essential. Complete data visualization is also key — by visualizing the data, you can obtain immediate insights into how to proceed, without the need for additional algorithms. Once you understand the data and its quality, you can decide whether the AI project can continue, and estimate the time and effort needed to acquire the remaining necessary data. When moving forward, treat your data as a resource.

Develop a strategy. Thoroughly understand your existing infrastructure and your organization's direction. Consider how you will deploy your AI models — in the cloud, on-premises, or in an air-gapped environment — and plan how to monitor and track the AI models’ success once they’re deployed. The lifecycle of AI models includes ensuring that they continue to perform as expected, and consistently addressing any issues that might arise after deployment.

Select your platform and software. Sensor data can quickly surpass the processing capabilities of a single machine. With this in mind, it’s important to have platforms, tools and repositories to handle larger volumes of real-time data processing. Consider a multi-coding-language AI platform that facilitates the use of diverse types of data, enables the creation of insightful visualizations, allows for easy collaboration, and expedites model deployment in production. Consult with your AI building teams, which often include data scientists, data engineers, machine-learning engineers, software engineers and developers. These individuals can determine the most suitable tools for your organization's use cases.

Ensure compliance, security and governance. Security considerations are paramount, and while open-source software offers the innovation of a broader community, it can also introduce increased security risks from vulnerabilities within software packages. Look for an AI platform that can identify these vulnerabilities without disrupting your developers' workflows. Specifically, look for platforms that include user access controls and reporting on common vulnerabilities and exposures (CVEs), as well as a platform that creates an association between CVEs and software packages used by your teams in development or deployment.

Take a tailored approach. Manufacturing encompasses a large range of enterprises, spanning mass customization with computer-controlled machines, fully customized work, high-volume mass production with cost constraints, high-precision and low-volume production for specialized applications, and various combinations. The application of AI to optimize the unit cost of producing millions of product items differs significantly from using the technology to identify defects in a safety-critical subassembly. Therefore, a one-size-fits-all approach doesn’t apply here, so carefully considering your particular manufacturing type’s specific needs and goals is critical to effectively applying AI to address those needs.

As the manufacturing industry evolves, rapid advances in AI might feel daunting at first, yet they hold immense potential for preventing future disruptions. When approaching AI implementation, carefully consider the diverse ways that AI can enhance efficiency and reduce costs. By diligently following each step as you embark on or improve your AI journey, you’ll be well-equipped to make informed decisions about technology implementation, and fully reap the benefits of automation in your manufacturing operations.

James A. Bednar is director of customer services at Anaconda.