Five Practical Steps Businesses Can Take to Overcome AI Adoption Challenges

Artificial intelligence is reshaping industries at an unprecedented pace—but supply chains are struggling to keep up. The technology promises the sector improved efficiency, predictive insights, and routine task automation. However, according to McKinsey, only 1% of businesses are considered “AI mature,” a sign that most are struggling to figure out how to make AI work for them.

To bridge the gap between AI’s promise and reality, supply chain professionals must focus on building AI-ready operations. Here are five practical steps to overcome AI adoption challenges and unlock the technology’s full potential.

1. Data Silos Are Stalling Progress—Standardization Unlocks AI’s Full Potential AI can only be as powerful as the data that fuels it, and data readiness is a chief challenge for the supply chain sector. Many supply chains operate with siloed data spread across multiple stakeholders, systems, and formats, leaving businesses with fragmented, inconsistent, or incomplete insights.

This lack of standardization means AI systems struggle to generate accurate insights. Businesses must prioritize:

Data standardization: Implementing globally recognized standards to seamlessly identify, capture, and share information about items, locations, and assets ensures data consistency across suppliers, distributors, and logistics partners, creating a unified foundation for AI-driven decision-making. Data quality audits: Regularly assessing and cleaning data helps eliminate duplicates, errors, and inconsistencies that can skew AI-generated insights. Interoperability: Ensuring that AI systems can seamlessly integrate with existing resources and platforms. Data standards serve as the connective tissue, enabling AI to operate effectively by providing structured, high-quality data that enhances visibility, traceability, and automation. Without a solid data foundation, AI adoption is an uphill battle.

Bob Czechowicz

2. AI Without a Strategy Is Just an Expensive Experiment—Align Tech with Business Goals Too many companies invest in AI without a clear use case, leading to wasted resources and minimal return.