Integration of AI into existing supply chain management systems does , however , involve many careful steps and wider considerations .
One of the main areas of attention is data . AI needs large amounts of data to make predictions , and while supply chains typically have no shortage of data , it needs to be accessible , prepared for analysis , and attention paid to increasing its quality . AI-adopting businesses also need to ensure they have good processes behind decision-making , overhauling them in preparation . They should also address the data literacy of their workforce , so they can use the technology to best effect .
The human element remains vital . AI research progresses daily , but even as systems become smarter , human input is needed . AI applications and skilled employees should complement each other , rather than create what some call a ‘ lights out ’ or fully autonomous supply chain that replaces the human . We know AI can find patterns in massive amounts of data beyond the cognitive capacity of homo sapiens , but it lacks the three C ’ s : it cannot derive meaning from context , collaborate by building
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