EY’s Alison Clark emphasizes that AI in supply chains demands business-driven strategies, highlighting the importance of data and coordinated efforts for success.
Supply Chains Embrace AI with Business Leadership
Supply chains have a history of adapting to technological advancements, and AI-driven operations are no exception, says EY’s UK Advanced Manufacturing & Mobility Leader, Alison Clark. While digitalization presents a formidable business objective, Clark asserts that supply chains, with their centuries-old legacy, have always harnessed new technologies for adaptation and improvement.
Strategic Alignment of AI Initiatives
For organizations embarking on transformation journeys, Clark emphasizes the need for strategic alignment in AI initiatives. These endeavors must serve operational purposes and be commercially viable, all while prioritizing IT to meet business requirements. Additionally, supply chain and technology leaders must recognize that AI relies on two fundamental elements: data and channels for data transmission.
Challenges in Data Capture and Alignment
One of the challenges facing supply chains is the lack of digital infrastructure for data capture, as well as misalignment in communication and incentives for data sharing. Clark underscores the importance of identifying tangible business problems that AI can address. She emphasizes that AI, though enabled by technology, must be led by business acumen. By focusing on specific business issues, companies can deploy point solutions that will foster data standardization and interoperability, advancing AI maturity.
Complexities in AI Deployment
Clark acknowledges that mapping, monitoring, and managing AI deployment in supply chains can be arduous tasks. These complexities often result in low coordination and misalignment in data quality and compatibility.
The Need for Actionable Data
Clark underscores the critical role of actionable data in AI success. Ineffective solutions hinder momentum, making it essential for companies to invest in data architecture and skill sets. Empowering IT teams accelerates experimentation and enhances AI deployment success. Achieving this, however, involves breaking organizational silos, revamping legacy IT architectures, and guiding people through these transformative changes.
Considerations for AI Implementation
Timing is another critical aspect of AI adoption in supply chains. AI upgrades are not instantaneous, and decision-makers must navigate uncertainties related to technology’s impact on market standing, value propositions, and reskilling plans. Developing the necessary technical capabilities, either internally or through partnerships, is also crucial.
Gartner Survey Validates AI Adoption Trends
Alison Clark’s insights align with a recent Gartner survey, where 50% of supply chain leaders plan to embrace Gen AI in the next 12 months, with an additional 14% already implementing AI solutions. Chief supply chain officers are allocating 5.8% of their budgets to Gen AI, recognizing its supportive role in broader digital transformation objectives.
Clark’s perspective underscores the importance of a business-driven approach to AI implementation, the value of actionable data, and the challenges inherent in supply chain AI adoption.
