What U.S. Manufacturers Need to Know About AI Regulatory Compliance |

With the growth and widespread application of artificial intelligence (AI), there is a clear need for regulatory compliance to ensure efficient operation and protect all stakeholders. Lori Witzel, Research Director for Analytics and Data Management at TIBCO, explains what all U.S. manufacturers need to stay current on in AI regulatory compliance to gain and maintain a competitive advantage.

There are high-value artificial intelligence (AI) use cases that can provide manufacturers with significant competitive advantages, including advanced asset management, predictive maintenance, and anomaly detection. However, US manufacturers should be aware of the potential impact of increasing AI regulation.

The business value of AI for manufacturers

For manufacturers, AI and machine learning (ML) can deliver predictive, prescriptive, and automated insights and actions across a variety of use cases with various business benefits. AI can improve outcomes when operational decision making requires more agility through the insight, scale and speed of trend investigation, anomaly detection, root cause analysis deep or the identification of key factors.

Here are some specific applications of AI in manufacturing; every manufacturer should focus on the data-driven innovations and efficiencies that would have the most impact on their business.

• Yield optimization through large-scale pattern recognition: Visual data can provide insight into manufacturing defects that affect yield, but turning the huge volumes of data from visual streams and sensors into actionable insights requires the application of AI/ML to pattern recognition and anomaly detection. Hemlock Semiconductor is an example of a manufacturer using this AI approach to transform their business and open up new markets. The agility provided has also helped Hemlock maximize resource efficiency while accelerating mean time to resolution of potential quality issues.
• Predictive and prescriptive maintenance via advanced equipment monitoring: Monitoring the condition of equipment using AI/ML on sensor or telemetry data automates the analysis of this data (which may be variations in appearance, vibration, temperature or noise level) to predict failures and prescribe corrective actions, such as ordering spare parts or planning maintenance. A case in point is how Brembo’s manufacturing department uses AI-based analytics for predictive maintenance and machine tool life prediction.
• Optimization of asset management via digital twins: Digital twins are an AI-infused virtual representation of an as-is physical product, with its associated systems and processes, using real-time process data and analytics derived from precise configurations of the pairing subject. For manufacturers, digital twins enable more efficient equipment maintenance and optimize the design, assembly, deployment and testing of production systems. IDC predicted that by 2024, half of G2000 manufacturers will develop ecosystem-based digital operations centers – including digital twinning – to build capabilities (human and machine) that will enable 50% faster time to market.

Manufacturers that lag their peers in adopting AI-based technologies are now at a competitive disadvantage. Research of McKinsey shows that leaders improved their performance on a number of KPIs by 10% or more compared to their laggards. With an overall average performance improvement of 9.5%, Leaders achieved nearly three times the improvements achieved by Laggards.

Learn more: A quick guide to smart manufacturing

Current situation: AI adoption and growth is driving regulation

According recent search according to the Harris and Google Cloud survey, 64% of manufacturers use AI in their day-to-day operations, with approximately 25% using more than 50% of their overall IT spend for AI. When it’s clear that the time has come now for manufacturers to leverage AI/ML, there are legal and ESG dimensions to consider.

AI is a hot topic among the public, often associated with job security threats and social displacement. Due to the potential for both negative and positive impacts of AI – the same technologies that bring value to manufacturers can also reinforce inequalities – legislators, policymakers and government agencies are considering regulating AI to reduce the risk of harm.

As manufacturers pursue the value of AI, building trust and transparency in AI is a critical best practice. It is also imperative to ensure compliance with current and future regulations.

Reliable and transparent AI will reduce the risk of serious harm from faulty AI. The potential for serious harms and public outcry over those harms is driving governments and policy makers to develop AI regulations. There are additional benefits beyond mitigating risk by ensuring AI is transparent and auditable. Auditability allows processes to be more easily reused/cloned once AI has been shown to add value, helping manufacturers accelerate their digital transformation.

Details on US AI regulations and key takeaways

Below are recent developments related to AI regulation in the United States. Note that the EU is also developing regulations on AI. If you have customers, suppliers or partners around the world, you will need to follow these developments. In part two of this series, we’ll discuss steps manufacturers can take to prepare for new and current regulations.

In the United States, specific regulatory guidelines on AI have been proposed on an agency-by-agency basis at the federal and state levels. Federal Trade Commission April 2021 Blog, “Aim for truth, fairness, and fairness in your company’s use of AI,” says the FTC will use its authority to prosecute the use of biased algorithms. The FTC’s ongoing enforcement actions show it is serious about pursuing compliance for AI fairness and transparency. As they wrote, it’s time to “hold yourself accountable – or be ready for the FTC to do it for you.”

Although still in development, the Artificial Intelligence Risk Management Framework (AI RMF or Framework) from the National Institute of Standards and use and evaluation of AI products, services and systems.

Although AI regulation is not yet law for the majority of states, the momentum toward regulation continues. To date, the states that have passed AI regulations include Alabama, Colorado, Illinois, and Mississippi. States with pending AI regulations include California, Hawaii, Massachusetts, Michigan, New Jersey, New York, North Carolina, Vermont, and Washington.

Although the AI regulation is not yet law for a majority of US states, the momentum towards regulation continues.

Learn more: 3 Big Data Challenges for Manufacturers and How to Solve Them

Let’s take a look at the main takeaways for the coming years:

• Don’t focus on AI regulation in a single state or region of the United States; seek the broadest possible approach to AI regulatory readiness: A manufacturer’s legal liability is unlikely to be limited to a physical headquarters or factory site. Now is the time to think globally in your planning. For example, an EU citizen residing in the US who is a partner, customer or supplier of a US-based manufacturer means that EU regulations will apply.
• The US regulatory environment for AI is changing rapidly, US manufacturers need to keep pace: In many cases, although AI is a technological approach to optimizing manufacturing processes, yields and results, you will need the advice of legal consultants who specialize in these matters. Meeting the need for transparency and auditability is not just about your IT and analytics teams.
• Don’t assume your tech stack is free of non-compliant AI; your vendors and suppliers can have AI in their own solutions: Even if your technology teams ensure that your own company’s use of AI is transparent and auditable, the use of AI by your vendors and suppliers can pose a risk. Include their solutions in your review processes.

And after? Since artificial intelligence can deliver game-changing benefits to manufacturers — an almost 10% performance boost, according to McKinsey — its adoption will continue, and so will the regulation that will likely come with it. In Part 2 of this series, you’ll learn five steps you can take now to reduce regulatory risk while pursuing your newfound AI advantage.

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