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. In supply chain management, AI can be tied to concrete goals like reducing lead times, optimizing costs, or improving inventory planning. Furthermore, AI could play a crucial role in meeting corporate environmental, social, and governance (ESG) goals by suggesting improvements to sustainability, like optimizing routes, reducing waste, and improving energy efficiency in logistics operations.

By aligning AI adoption with business objectives, companies can avoid the “tech for tech’s sake” trap and ensure meaningful impact. Here’s what businesses can do: 

Identify key challenges AI can address—whether it’s demand forecasting, real-time tracking, or inventory optimization. Identify the biggest pain points AI can solve.  Set measurable objectives that align AI initiatives with overall business or ESG goals, ensuring tangible ROI. Start with pilot programs to validate AI use cases before scaling across the organization.  3. Build AI Literacy Across the Organization AI adoption is not just a technological shift but a cultural one. Supply chain professionals aren’t AI experts, making AI literacy central to ensuring teams can trust AI-driven insights, like demand forecasts or suggested areas for automation. 

Beyond AI literacy, businesses must also ensure that teams understand data interoperability and traceability standards—essential for AI-driven insights to be trusted and actionable. Standards enable AI to process and analyze data from data carriers like RFID, IoT sensors, and 2D barcodes, allowing companies to make informed sourcing and logistics decisions.

To ensure AI is understood and embraced, employees must be equipped with the knowledge and skills to leverage it effectively. This includes:

Training teams on AI capabilities to demystify the technology and encourage adoption. Educate teams on how AI can be paired with information-sharing standards to support procurement and company-wide initiatives, such as reducing emissions through optimized routing. Encouraging cross-functional collaboration between IT, data science, and supply chain teams to ensure smooth AI integration.  Empowering decision-makers with AI-driven insights to enhance strategic planning. AI is most effective when combined with human expertise and standardized data frameworks. Businesses can drive smarter, more strategic decision-making by equipping teams with the knowledge and tools to leverage AI effectively. “Ignoring AI governance can lead to biased decision-making, regulatory fines, and security risks. The implications go far beyond supply chain decision-making.”

4. AI Without Governance is a Risk—Regulation and Compliance Matter As AI becomes more embedded in supply chain operations, businesses must navigate the ethical and regulatory challenges that come with it. Adherence to data privacy regulations and ethical AI practices is essential for preserving trust and ensuring operational continuity. 

Ignoring AI governance can lead to biased decision-making, regulatory fines, and security risks. The implications go far beyond supply chain decision-making. As stakeholders and consumers increasingly demand accountability for sustainability, applying AI to ESG requires further oversight to safeguard against greenwashing.

In an era of increasing scrutiny over data privacy and ethical AI use, companies that prioritize governance will build trust and long-term resilience. Here’s what to keep in mind:

Bias mitigation: Ensuring AI models are trained on diverse, high-quality datasets can help avoid skewed decision-making.  Regulatory compliance: When using AI to process customer or supplier data, regularly auditing AI systems is essential to ensure accurate data and compliance with evolving state and global regulations like the EU’s General Data Protection Regulation (GDPR) or California’s Consumer Privacy Act (CCPA). Transparency and explainability: Businesses should be able to understand and justify AI-driven recommendations. Interoperability is central to ensuring teams can trust and validate AI’s decision-making processes. 5. AI is an Ongoing Investment—Continuous Improvement is Key AI is not a one-and-done initiative—it requires ongoing evaluation and adaptation. Implementing AI and then neglecting updates can result in outdated models and reduced effectiveness. 

To ensure that AI adoption remains a continuous evolution rather than a one-time project, businesses should:

Assess AI performance against KPIs regularly to identify areas for improvement. Track outcomes and iterate on use cases to foster a culture of ongoing AI optimization. Encourage experimentation and iterative improvements to foster a culture of continuous AI optimization. AI is evolving rapidly, and businesses that commit to ongoing investment will stay ahead of the curve. The Big Takeaway: AI is Only as Strong as What’s Behind It Supply chain professionals can’t afford to delay AI adoption. Inefficiencies, rising costs, and competitive pressures demand action. 

AI has the power to revolutionize supply chain management, but without a strong data foundation and strategic approach, businesses risk falling short of their full potential. As AI evolves, businesses that leverage structured, shareable, and actionable data across their supply chains will be set up for success. By focusing on data quality, aligning AI initiatives with business objectives, building AI literacy, addressing ethical considerations, and fostering a culture of continuous innovation, organizations can accelerate AI adoption and drive real-world impact.

As we enter a new era of AI-powered supply chains, businesses that take these steps today will be best positioned to reap the benefits tomorrow.

Bob Czechowicz is Senior Director of Innovation at GS1 US.

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