The implementation of AI in supply chain management requires more than just cutting-edge AI tools—it demands a complete modernisation of existing infrastructure.
According to Ben Hussey, Co-CEO at Katana Cloud Inventory, the journey begins with a fundamental shift away from legacy systems.
He says: “The first step for any manufacturer is to move from outdated on-premise systems or spreadsheets to a connected platform with real-time visibility to data across all business tech like accounting, e-commerce etc. to centralise the entire value chain in one place.”
This shift towards modernisation is driven by increasing demands for transparency and agility. Businesses are scrutinising their reporting capabilities to enhance visibility and improve their response to supply chain disruptions.
Through AI-driven tools, manufacturers can automate recurring tasks, significantly increase operational speed and gain better visibility into their supply chain processes.
While AI promises transformative benefits for supply chain operations, a significant hurdle stands in the way of widespread adoption.
Ashish Chaturvedi, Practice Leader at HFS Research, identifies data debt as the primary obstacle to progress.
“Of the several debts enterprises are faced with today – technology, process, skills and data – data debt is considered the most significant barrier to building autonomous and sustainable supply chains,” Ashish explains.
“For example, without clean, consolidated data across departments and systems and continuous collaboration between all entities such as manufacturers, distributors, logistics providers, and suppliers, achieving end-to-end visibility is not possible.”
The challenge lies not just in collecting data, but in ensuring its quality and accessibility across the organisation.
A recent study carried out by HFS Research reveals a cautious approach to AI adoption in the supply chain sector. According to its survey of nearly 500 senior supply chain executives, 62% of organisations have reassessed their generative AI programmes due to risk or implementation challenges over the past year
Despite current challenges, the future outlook remains optimistic. HFS Research projects that, by 2030, two-thirds (65%) of organisations expect Gen AI to play a major role in increasing real-time responsiveness to internal and external issues, while 63% believe the technology will drive alignment across supply chain functions.
The potential benefits of AI integration are substantial.
“AI can help streamline operations across the value chain and better align production with market needs, reduce waste, cut costs, and drive competition,” Ben emphasises.
Moreover, manufacturers can leverage AI tools for strategic resource allocation, enabling them to centralise operations and employ an omnichannel strategy for growth while maintaining visibility.
To maximise the potential of AI in supply chain management, Ashish advises that organisations “must prioritise overcoming their data debts, improving collaboration and streamlining internal processes to set themselves up for success – particularly with Gen AI technology”.
The key to realising value from AI-powered supply chains lies in prioritising use cases that deliver the most significant impact, including demand forecasting, supplier risk management, logistics optimisation, and sustainability initiatives.
As companies continue their digital transformation journeys, the focus must remain on building strong foundations through modern, cloud-based solutions that can support advanced AI capabilities while maintaining comprehensive visibility across the entire supply chain network.
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