ML Journal

GenAI is a necessary tool for competitive advantage, but companies will need to navigate the risks and capitalize on the opportunities—or risk being left behind. 

 

TAKEAWAYS:
● Most supply chain and operations executives (73 percent) are planning to deploy GenAI, yet only 62 percent have reassessed projects, and merely 7 percent have completed implementation.
● Organizations further ahead in the autonomous supply chain journey are 5.2 times more likely to see success with GenAI, further widening the digital gap.
● The greatest gains from GenAI come when projects align to a strategic vision, data are AI-ready, and value is maximized by addressing cyber and data risks.  

 

Since 2020, the global economy has been in a new paradigm, marked by disruptions and changes that are more forceful, appear more frequently, trigger more interconnected and widespread impacts, and often strike immediately. Chief operating officers and supply chain managers know this all too well—the supply chains they oversee exist on the frontier of this high-risk climate, bearing the brunt of its disruption.

In response, many companies refocused on boosting resilience in supply chains, which involved diversifying operations across multiple countries and suppliers. Supply chains that had once been ultra-lean gained protection against external shocks—but often at the price of reduced efficiency.

Enter generative artificial intelligence (GenAI), which can help companies leapfrog their technical maturity and accelerate their path toward an autonomous supply chain. GenAI is being used to not only analyze and interpret vast amounts of data but also to create new scenarios, generate innovative solutions, and remove frictions in real-time. As a result, managers have end-to-end visibility and more human time for higher-order work.

“Companies see GenAI as a critical capability for remaining competitive in the future and are investing in it accordingly.”

 

Unlike traditional AI, which primarily focuses on data-driven insights and automation, GenAI can design new processes, forecast future demands with greater accuracy, and seamlessly identify the most cost-efficient routes and carriers in the event of a disruption.

But the combination of GenAI and traditional AI is a game changer in bridging the gap to self-driving supply chains, because of GenAI’s breakthrough capabilities and the ways in which the strengths of the two technologies complement each other. It is a compelling vision, but one that has so far remained elusive.

EY global research of 460 supply chain and operations executives has found that even among organizations that have started preparing for GenAI in their supply chain, only 28 percent have achieved a low-human-touch supply chain, and only 50 percent have achieved end-to-end visibility across the supply chain.

The Great AI Reset

Companies see GenAI as a critical capability for remaining competitive in the future and are investing in it accordingly. Three-quarters (75 percent) are planning to deploy GenAI in their supply chains, and 80 percent believe it can reinvent supply chains. In addition, 69 percent believe that failing to integrate GenAI into their supply chains will put them at a competitive disadvantage.

Despite this optimism, a retrenchment is taking place. In the past 12 months, 62 percent of respondents have reassessed their GenAI supply chain initiatives, and only 7 percent have gone on to complete deployment.

Why?

Two reasons:

1. Concern and lack of understanding around the unique risks created by GenAI; and
2. Challenges of implementing this complex technology.

Our findings suggest this reset is about achieving scale and maximizing impact. In-depth interviews with seven supply chain and operations executives revealed that it was tougher than expected to make the technical leap from proof-of-concept to GenAI at scale.

Leading the Way to Autonomous Supply Chains

Organizations further ahead on the journey to autonomous supply chains (front-runners) have created strong digital foundations that enable them to adopt and take advantage of GenAI quickly. This momentum is likely to widen the digital divide, unless those who are behind (followers) take prompt action.

GenAI is giving organizations a faster pathway to the autonomous supply chain. Front-runners are more ambitious about using GenAI in the next two years, anticipating deploying GenAI in 12 use cases for the supply chain on median compared to eight for followers. Front-runners are going beyond the supply chain to align more closely with other business functions and external parties and driving end-to-end visibility across the supply chain.

Most organizations’ supply chains (82 percent) are using both AI and GenAI across a wide array of use cases to take advantage of their different, often complementary strengths. Traditional AI is rules-based, requiring prepared datasets and predefined logic to solve business problems. GenAI is great for text-rich environments and unstructured data, creating new content based on the data it has been trained on. For example, companies using traditional AI for demand forecasting and quality optimization are finding that a GenAI layer improves accuracy and democratizes adoption of the tools.

Substantial GenAI Growth Expected

Substantial growth in GenAI is anticipated over the next two years. Given that front-runners already use traditional AI more and are more confident about future growth, the gap between them and followers is likely to widen. Looking more broadly at where front-runners are focusing the first wave of use cases, those with high GenAI deployment today and continued high anticipated use in two years will likely focus on

• Product design
• Logistic network design
• Global trade optimization
• Demand forecasting

These are areas where traditional AI has long been available but has been limited by the need for highly trained data scientists, which has made them too expensive for many. GenAI provides the benefit of a natural language, interpretive layer that can become a democratizing force that puts these tools in the hands of the workforce. These areas are also supply-chain functions with well-defined datasets, a high percentage of unstructured data and high value to be gained.

The next wave of use cases, those with lower GenAI deployment today but high anticipated use in two years, potentially includes

• Supplier management
• Production yield or quality optimization
• Risk management
• Customer service chatbots and product training

These high-growth areas also offer clear commercial returns, either by improving the speed of customer service through chatbots or reducing costs through quality optimization in manufacturing. Demand forecasting is another key area of focus for GenAI. It’s perceived to be a use case that will resolve a lot of pain points for the supply chain and offers clear metrics that can make the business case an easier sell to the CEO and the Board of Directors.

Many of these use cases suffer from back-end challenges when using traditional AI (quality optimization, predictive maintenance) and require bespoke solutions. Customer service chatbots also have a high degree of risk given the chatbots’ direct interaction with the public. Both factors may be contributing to the time horizon suggested here.

Three Actions to Overcome Implementation Challenges

Implementing GenAI in the supply chain involves a complex interplay of technical, organizational, and operational challenges. Organizations should take the following three action steps to advance their journey to the autonomous supply chain.

1. Align people and investments to strategic vision

For front-runners, the top factors for success in GenAI deployments are securing support from leadership (67 percent), building support from third parties (65 percent), and availability of technical talent (64 percent). The largest gaps between front-runners and followers are in prioritizing the strategic vision and support from third parties, which highlight the importance of a cohesive vision and external support in ensuring GenAI pilots and implementations are focused on delivering business value.

A cohesive, strategic vision can clarify investment priorities against the seemingly endless list of use cases of GenAI, minimize the risk of multiple business units duplicating investments, and improve AI outcomes by guiding the augmentation of large language models (LLMs) with reusable algorithmic patterns, such as retrieval-augmented generation.

2. Prioritize data readiness when considering use cases

The demands of GenAI are shining a spotlight on the myriad complexities of enterprise data management. Despite data availability, quality, and privacy being top of mind when prioritizing use cases, organizations are still struggling. Maintaining data quality is the number one implementation challenge cited by respondents (38 percent), with access to data (33 percent) another top challenge.

Any organization hoping to compete in GenAI needs to get its data house in order, by prioritizing data cleansing, standardization, systems, and engineering to reduce latency, and enhancing metadata so data can be consumed by retrieval augmented generation (RAG) systems to improve the accuracy of GenAI outcomes.

3. Maximize GenAI value by mitigating cyber and data risks

GenAI is a nascent technology, so it is not surprising that 40 percent of respondents say their organizations do not fully understand the new risks and challenges of GenAI in supply chain. GenAI poses new vulnerabilities—for example, through prompt injection attacks designed to provoke LLMs into leaking sensitive data or manipulate their outcomes. Front-runners are more likely than followers to focus on the new risks that GenAI poses, such as inaccuracies and hallucinations, exposure to legal liability through IP infringement, overreliance on untested technology, brand or reputation damage and job insecurity.

The need for stronger cybersecurity is paramount as organizations look to deploy GenAI. Supply chain and operations leaders need to work closely with cybersecurity teams from the beginning to help with the secure adoption of GenAI in the supply chain. This includes embedding the cyber team in use case identification and governance to ensure the value potential of GenAI in the supply chain is maximized.

GenAI’s Potential and Pitfalls

GenAI is emerging as a transformative tool, enabling more autonomous supply chains with end-to-end visibility and real-time problem-solving capabilities. EY research suggests that GenAI adoption is crucial for competitiveness, but challenges in understanding risks and implementation complexities have led to a strategic reassessment. Front-runners in GenAI adoption are leveraging it for improved demand forecasting and operational efficiency. To overcome these challenges, companies must align strategic visions, prioritize data readiness, and mitigate cyber risks. For more information, visit Will GenAI accelerate autonomous supply chains? | EY – US.  M

Authors:

 

Ayoub Abielmona is an EY Global GenAI Supply Chain Leader.

 

 

 

Matthew Burton is an EY EMEIA Supply Chain and Operations Leader.

 

 

 

Sumit Dutta is Principal, Supply Chain & Operations, Ernst & Young LLP.

 

 

 

David Guarrera is Principal with EY Americas Technology Consulting, leading Generative AI initiatives.

 

 

Jocelyn Hallum is EY Global Supply Chain Transformation, Planning and PLM Transformation Solution Leader.

 

 

 

Glenn Steinberg is EY Global Supply Chain and Operations Leader.