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A recent StratView Research market report on artificial intelligence AI in supply chain forecasts approximately 30% growth rate annually for the next five years — a projection that should surprise no one. The IBM Institute for Business Value and Oxford Economics surveyed more than 300 global chief supply chain officers and chief operations officers from organizations implementing AI-enabled automation, and found that those with higher investment in AI for supply chain operations achieved a 61% revenue growth premium over their peers.
These impressive statistics reflect a fundamental truth: In the complex world of supply chain management, efficiency gains translate to significant competitive advantages.
Yet despite AI’s proven value, a persistent narrative frames the technology as a threat to human employment. This perspective fundamentally misunderstands AI’s true capabilities and pragmatic function in the supply chain ecosystem.
AI Is a Tool
All the potential power and profitability in AI is contingent upon how effectively it translates into real-world results — which rely entirely on human expertise and oversight. Even the most sophisticated generative AI system requires human guidance to function reliably and deliver tangible benefits. For supply chain professionals at every level, from the C-suite to the loading dock, AI serves precisely two functions: It either enhances job performance efficiency, or frees capacity to focus on other critical responsibilities. That’s it. It cannot replace the nuanced judgment and contextual understanding that human experts bring to the table.
Consider a simple domestic analogy: loading a dishwasher. An experienced adult, through years of practice and pattern recognition, can arrange items efficiently to maximize the number of dishes cleaned effectively in one load. A child attempting the same task produces dramatically different results. The critical factor is accumulated understanding and experience — qualities that AI can’t replicate.
What AI can do, however, is be trained on that accumulated understanding, in the form of data to accelerate how organizations develop and distribute expertise throughout their workforce.
It’s All About the Data
Poor data integration remains the primary obstacle for most organizations seeking greater supply chain visibility in pursuit of efficiency. Ironically, data integration assistance represents one of the most appropriate early use cases for AI implementation in supply chains.
Most organizations have accumulated disparate technologies over time, in the form of legacy systems, inconsistent data formats, and outdated infrastructure that resist simple modernization. There’s no simple way to lift-and-shift it all to establish a modern optimized data estate.
From small businesses with applications running on a server under someone’s desk to global conglomerates managing complex multi-cloud deployments, fragmented data limits workflow optimization. The lack of integrated data across source systems creates a cascade of challenges: identifying common fields, standardizing formats, creating integration hubs, and developing source-to-target mapping. All represent —such monumental effort that most supply chain operations default to spreadsheet-based management. The dirty secret of the supply chain world is that most of it still runs on Excel.
This represents precisely the type of problem where generative AI, in particular, can deliver extraordinary value. By automating data translation, eliminating duplication and mapping at scale, AI yields tremendous benefits in speed, accuracy and simplification of complex data pipelines.
Start Small, Think Big
The key to effective AI implementation lies in aligning it with specific strategic values and identifying opportunities for quick wins. Technology will continue evolving, but the human element is always going to be the greatest implementation challenge. Supply chain and logistics organizations vary dramatically in requirements and systems — no two are going to face the exact same obstacles — but modern data infrastructures can accommodate virtually any model or strategy when properly mapped to particular operational needs.
Success depends on building trust and securing organizational buy-in from the people actually involved in the process. This requires identifying compelling use cases that demonstrate clear value, and securing both executive and team champions who can articulate a cohesive vision and guide implementation. By starting with a focused application that addresses a specific pain point, organizations can establish proof points that resonate with stakeholders and build momentum for broader transformation. As with any new tool, people need the opportunity to work with it and experience its effectiveness to be able to conceptualize benefits before they’ll commit to adoption.
AI represents an unprecedented opportunity to revolutionize supply chain management — not by replacing human workers, but by augmenting their capabilities and freeing them to focus on higher-value activities. Let the bots take care of data translation and cross-referencing spreadsheet cells, so the humans can more readily determine the ports and routes and delivery paths. AI technology’s true power emerges when organizations address their foundational data challenges, and implement solutions that complement human expertise rather than attempting to substitute for it.
Supply chain organizations that recognize AI as a tool for human enhancement rather than replacement will position themselves to capture that 61% revenue growth premium. In doing so, they’ll establish supply chains that are not only more efficient but also more resilient, adaptable and human-centered.
Rico Mawcinitt is global head of supply chain and logistics at Hakkoda, an IBM Company.
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