Three Key Ethical Considerations for Manufacturers Embracing AI

Artificial Intelligence is no longer a futuristic concept for manufacturers. While finance and insurance have long used algorithms for credit decisions, the manufacturing sector is only beginning to unlock AI’s potential. By integrating cutting‑edge machine learning with proven automation systems, manufacturers can dramatically boost productivity and open new revenue streams.

Antony Bourne, President of Industries at IFS, emphasizes that as AI becomes more pervasive, ethical frameworks must be built from the ground up. He identifies three critical ethical challenges that leaders should confront as they scale AI investments.

A recent IFS study surveyed 600 manufacturing professionals, 383 of whom are senior decision makers working with ERP, EAM, and FSM solutions. The results are striking: over 90% plan to invest in AI. Coupled with 5G and IoT, AI will enable real‑time communication between enterprise systems and automated equipment, unlocking production models like configure‑to‑order and even fully custom manufacturing.

Despite the clear productivity, cost‑saving, and revenue benefits, the industry is now confronting its first wave of ethical dilemmas. Below are the three primary considerations manufacturers must weigh.

1. What will AI do to your workforce?

AI’s most immediate contribution is to create new product and service offerings that were previously impossible. For manufacturers servicing aftermarket contracts, NLP‑enabled chatbots can handle a large portion of routine customer interactions, freeing human agents to tackle complex issues. When AI is integrated with connected devices, remote troubleshooting becomes faster and less labor‑intensive.

However, as labor hours required to generate value shrink, questions arise about the broader economic impact. A 2017 NCCI study found that from 1990 to 2016 U.S. manufacturing output grew by more than 70%, while employment fell by over 30%. In 2016, production was 70% higher with only 70% of the workforce, driving labor productivity growth of 140%. Automation has reduced costs, but new AI‑generated jobs may be short‑lived during transition periods, raising concerns about the need for shorter work weeks, new business models, or even different economic systems.

Enhancing, not replacing, human skills

Optimists argue that the efficiency gains from AI will outweigh transitional costs. Rather than outright displacement, AI often acts as a “guide‑on‑the‑side,” empowering workers to make better decisions and increasing overall productivity. Moreover, IFS research indicates a positive outlook: 45% of respondents believe AI will create jobs, while 24% see no impact on headcount.

2. Are you accurately assessing AI’s productivity and profitability?

Digital transformation is not a buzzword; it’s a fundamental shift in technology strategy that must be embraced company‑wide. Successful transformation begins with transparent, data‑driven ROI assessments that involve all key stakeholders from the outset. Executives must communicate realistic expectations and avoid overpromising.

Valuing AI is complex

Unlike early IT projects that focused on metrics like process speed or inventory turnover, AI in manufacturing must contend with entrenched processes, stretched supply chains, aging assets, and global competition. Establishing credible metrics requires a rigorous, collaborative effort between executives and vendors to pinpoint tangible value opportunities.

Capital investments and process changes need board approval, so executives must distinguish between incremental benefits from narrowly scoped AI pilots and broader systemic gains. Clear, evidence‑based projections help secure stakeholder buy‑in and protect against future claims of overpromise.

3. Take responsibility for AI outcomes—both good and bad

AI decisions can have unintended consequences, and ultimately the organization and its leaders bear responsibility. The legal fallout from autonomous vehicle accidents illustrates that liability is assigned to the human decision‑makers behind the algorithm, not to the algorithm itself.

AI is a human tool

Margot Kaminski, Associate Professor at the University of Colorado Law School, warns of “automation bias”—the tendency to trust machine decisions over human judgment. Because most users lack the technical expertise to evaluate AI outputs, they risk making flawed decisions based on opaque systems.

Explainable AI—systems that provide a clear audit trail of decision logic and data sources—is essential. Kaminski notes that explanations must be tailored to diverse audiences, from lawyers seeking legal justification to data scientists demanding technical detail. Rigorous documentation ensures accountability and supports challenging decisions when necessary.

Ready to evolve digitally with AI?

Manufacturers are moving beyond mimicking human intelligence to harnessing machine capabilities that surpass human limits. A true digital transformation will embed AI across every process, eliminating repetitive tasks and freeing human talent for higher‑value work.

While this shift reduces production costs and increases value creation, it also reshapes labor roles and liability frameworks. Organizations that strike the right balance—embracing AI’s benefits while safeguarding workers and society—will lead the industry. Will yours be one of them?