EY: Overcoming Hurdles to Introduce GenAI into Supply Chains

EY research: GenAI is gaining a foothold in supply chain management, but overcoming execution hurdles will take time

As Gen AI starts to become prevalent in supply chain management solutions, how can businesses overcome the hurdles in implementing the technology?   Research from EY is highlighting the need for system integration, talent and data to help organisations feel the business benefits.  

Sumit Dutta, Principal of Supply Chain & Operations at Ernst & Young LLP argues in the whitepaper the technology can bring with it “transformative implications for businesses across the world” but there are “roadblocks” in implementing it in terms of data security, talent procurement and incompatibility with legacy systems.

The research identifies three key questions for procurement and supply chain professionals need to ask:

  • What are the key application areas of GenAI in supply chain management?
  • What are the primary challenges supply chain leaders face in adopting GenAI and how can they overcome them?
  • What are the hallmarks of a successful GenAI programme?
EY Office Budapest (Credit: EY)

Investment in GenAI solutions in supply chain management 

The most recent CEO Outlook Pulse Survey 2023 on AI and other pieces of research from technology vendors and consultants is pointing towards the “eager” investment in AI-driven products and services.  “In planning, AI’s role was evidently seen across supply chain control, digital twins for virtual modelling and streamlining of product development. In sourcing, it facilitates contract management, procurement 4.0 through real-time inventory analysis and risk assessments of vendors,” says Dutta.

From a wider supply chain perspective, AI is proving valuable in manufacturing in terms of warehouse automation, predictive scheduling and task management. The whitepaper also identifies GenAI is finding patterns of data without needing human intervention, which makes it extremely adaptable in different use cases. 

“Major retail chains and health care industries are already piloting the use of GenAI for tasks such as summarising and analysing customer feedback and generating novel small-molecule entities for drug discovery,” adds Dutta.  

Although the EY analysis considers these gains as ‘promising’ it also identifies a series of hurdles to adoption.  “GenAI has its own roadblocks in terms of data security, talent procurement and incompatibility with legacy systems, to name a few,” writes Dutta. “Added to this is the cost and complexity of achieving regulatory compliance."

"However, by employing a systematic strategy of preparation, prioritisation, contemplative presentation, and thorough evaluation for timely corrective measures, businesses can steer through these hindrances effectively and leverage GenAI to its full potential.”

GenAI implementation is more than a technical challenge

The whitepaper identifies how procurement and supply chain management have more than an IT or technical challenge on their hands when looking to integrate GenAI into their workflows.  

EY is surfacing five key ‘roadblocks’ to GenAI implementation.

  • Data Breach Concerns: While Generative AI offers innovative possibilities, its reliance on internal and external datasets poses significant risks of data breaches and leakage. The training process involves sensitive information, raising security concerns for organisations.
  • Talent Shortage: Many organisations struggle to recruit and retain specialised talent for Generative AI projects. From software engineers to business analysts and data scientists, the demand for skilled professionals outstrips the available supply, hindering implementation and development efforts.
  • Legacy System Compatibility: Older systems often lack compatibility with the advanced requirements of Generative AI. Outdated technology stacks and architectures may lack the necessary interfaces or APIs for seamless integration, necessitating extensive customization and redevelopment efforts.
  • Regulatory Challenges: The dynamic regulatory environment adds complexity to Generative AI deployment. Developers must stay updated with evolving regulations to ensure algorithm compliance, mitigating the risk of fines and penalties associated with non-compliance.
  • Cost and Complexity: Implementing Generative AI solutions entails substantial investment in computational resources and talent. These solutions require high-performance computing services and specialised expertise for design, implementation, and maintenance, posing significant cost and complexity challenges for organisations.

“AI’s moment is now,” says Carmine Di Sibio, EY Global Chairman and CEO. “Every business is considering how it will be integrated into operations and its impact on the future. However, the adoption of AI is more than a technology challenge.”

Carmine Di Sibio, EY Global Chairman and CEO (Credit: LinkedIn)

Find out how Lenovo is using AI to develop a resilient supply chain and procurement operation.


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