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Why we must humanize AI for global supply chains

The rapid adoption of AI/ML is driving us towards a new world where computer models are increasingly shifting from “informing humans” to “taking decisions for humans”.

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Photo: Philipp Katzenberger via unsplash.com
Photo: Philipp Katzenberger via unsplash.com

This is sponsored content written by GEP

Artificial intelligence and machine learning (AI/ML) are seeing a Sputnik-moment as companies globally drive their digital initiatives evermore expansively towards automation, flexibility and resilience. 

The Fourth Industrial Revolution, according to the World Economic Forum, envisions truly autonomous and self-correcting supply chains that can make profitable data-driven, real-time decisions for enterprises.

The rapid adoption of AI/ML is driving us towards a new world where computer models are increasingly shifting from “informing humans” to “taking decisions for humans”. 

Gartner predicts that by 2024, 75% of organizations would have operationalized AI — ranging from simple data science use cases to simulating and operating complex supply chain systems, thus handing over more power to AI.

The humanization of AI is thus critical — for a better understanding of these decisions; acceptance of data-driven “intelligence” and influencing to align them with human values. Adoption of smarter and more responsible AI can enable organizations to simplify and leverage AI in generating value and creating higher business impact within a shorter period.

Cognitive user interfaces will add human touch to supply chain AI

Today, across supply chain operations, virtual assistants are deployed to provide contextual guidance for buying, execute routine purchases, assist with the completion of purchase requisitions, handle order-to-cash operations, resolve disputes, make credit decisions and more. 

The ability of AI algorithms to take on cognitive capability has been rapidly advancing in recent years which makes AI more accessible. 

In 2022 and beyond, we should see companies investing in new cognitive user experiences, particularly with virtual reality and the metaverse, to help supply chain specialists drive innovation in product and process design, collaboration and immersive planning.

Explainable AI will help build trust in intelligent supply chains

Organizations see great potential for AI in sales and operations planning (S&OP) like demand planning and control tower. 

However, supply chain managers struggle to justify AI/ML to their executives due to the black-box nature of AI. Relying on AI-generated demand planning and predictions in control tower would implicitly tie the company’s financial decisions with AI without the ability to reason with logic. 

Supply chain companies should build-in a foundation for Explainable AI as part of AI strategy to seamlessly integrate AI into areas such as demand planning, inventory management, production planning/scheduling. These methods provide tools and frameworks that allow users to understand how the AI model weighs information, allowing the user to interpret the predictions and perform what-if analyses.

Businesses must nurture AI for responsible, ethical and sustainable supply chains 

AI is transforming supply chain industry and solving important, real-world challenges at scale. This vast opportunity carries with it a deep responsibility to build AI that works for everyone – one that mitigates ethical risks and aligns with goals like ESG and supply chain sustainability. Experts have expressed concerns about how capitalism driven applications of AI will affect humans, the environment and free will, according to recent research by Pew Research Center and Elon University.  

Supply chain leaders must follow a holistic framework to deploy AI responsibly.
Most AI models in supply chain are designed to optimize for lowest costs whereby the risks tied to collecting fair data and non-financial factors are ignored, leaving communities, the environment and trading partners to pay the price for unsustainable behavior. 

ESG and sustainability are a complex web of interconnected systems and decisions. For example, 85% of ESG-related impacts, including greenhouse gas emissions, are attributed to supply chains, necessitating immediate and meaningful solutions. 

The success of AI in providing the solutions in this context is not just about model accuracy but also about incorporating them into the organization’s ESG priorities. It is still early days, but formal efforts have begun towards establishing frameworks for responsible AI. 

Conclusion

Every day, new AI breakthroughs promise to change the way we manage and conduct global business. 

However, the full potential of AI cannot be realized if we do not humanize the algorithms with trust, transparency, and collaboration. It is a mutual partnership between humans and AI to transition from a linear objective of profit maximization to solving problems for the greater good – a sustainable, ethical and responsible world that puts equity for all at the center.

Saratendu Sethi is vice president for AI at GEP, a leading provider of supply chain strategy and software to Fortune 500 and Global 2000 enterprises worldwide.

GEP is
spearheading the World Economic Forum’s AI task force to ensure AI is applied ethically and compassionately in businesses’ procurement and supply chains.

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