Mark Burstein, SVP at Inspectorio, transforming how brands manage supply chain risk, quality and compliance through an AI-powered platform.
Every supply chain executive I speak with is exploring how to bring AI into their operations. With supply chains facing unprecedented disruption and complexity, the potential for AI to drive meaningful impact is significant. Yet one question consistently rises to the top: “Where do I start?”
Supply chain pressures are increasing, but there are barriers to deploying AI.
The pressures facing supply chains today are increasingly complex and interconnected. Trade policies continue to shift, tariffs are putting pressure on margins, regulatory requirements are expanding and geopolitical conflicts are disrupting established trade flows. At the same time, supply chain leaders are being asked to manage this growing complexity and deliver more, often without additional headcount. That’s why Gartner predicts 60% of supply chain disruptions will be resolved without human intervention by 2031. However, there are significant barriers to AI implementation.
The primary obstacles to effective AI adoption are insufficient data quality, an inability to manage organizational change and a lack of structured data integration across different departments. Eighty-five percent of executives said their data is at least somewhat fragmented, making it difficult to scale AI, according to a recent benchmark study by Pymnts Intelligence. Companies must also realize that AI adoption is a major organizational change, not simply a software installation. And only 21% of organizations are pursuing holistic, multi-tier network visibility, according to Inspectorio’s “State of Supply Chain Report 2026,” based on responses from nearly 200 supply chain professionals.
Clearly, there’s room for improvement.
Here are four pillars for successful AI implementations.
Organizations that are successfully deploying AI today focus on four pillars: starting with business outcomes, ensuring data readiness, aligning outcomes with AI user cases and leading with organizational change. Here’s a closer look:
1. Start with business outcomes, not technology.
Companies that successfully implement AI begin by identifying three to five business processes that are clearly inefficient, for example, labor-intensive supply chain mapping or costly product inspections. They then set well-defined, measurable outcomes, such as an 80% reduction in manual reviews. Companies are gradually focusing on large-scale, complex use cases, but even projects with limited scope can produce significant ROI.
2. Ensure that data is AI-ready.
This means that supplier and product data is centralized, not siloed in different systems, emails and spreadsheets. Quality and compliance records must be digitized and consistently structured, and historical audit and inspection data must be easily accessible for AI training. Data must be accurate to ensure that AI outputs can be trusted. AI readiness is critical; Gartner predicts that organizations will abandon 60% of AI projects that do not have AI-ready data by the end of 2026.
3. Align outcomes with AI use cases.
This involves four phases:
• Context: Building high-quality data based on a company’s core processes and industry best practices
• Prediction: Leveraging historical process data and the resulting outcomes, in order to optimize operations
• Automation: Defining roles and processes where there is an opportunity to shift from individual, manual management to oversight of AI
• Evolution: Monitoring AI-augmented processes and continually identifying opportunities for refinement
By following these steps, one company made measurable improvements to address corrective actions by its suppliers, realizing a 33% improvement in first responses, 32% improvement in time to close and savings of 100,00 labor hours annually.
4. Lead the organizational change.
Inspectorio’s survey found that 25% of respondents weren’t sure if AI was integrated into their supply chain operations. That’s a failure of executive sponsorship. Organizations with strong executive sponsors are seeing measurably higher AI adoption success. Companies should identify internal champions for AI who can communicate AI wins, build internal credibility for AI and allay any fears that AI will replace tasks, not people.
Follow this road map for the first 90 days.
Where can companies start? Here’s a road map for the first 90 days:
• Days 1–30: Begin by identifying the AI executive sponsors and assessing the areas of the businesses where AI can initially be applied. Next, discover your data foundation and quality gaps, and establish an internal communication plan and messaging for the AI initiatives.
• Days 31–60: Select one “quick win” use case for AI automation, keeping in mind that it must have measurable outcomes. Identify two to three internal advocates and get their commitment to the project, then establish the baseline metrics to track success.
• Days 61–90: Measure the results of the initial project, comparing them to the initial baseline, and clearly document the return on investment. Then decide how to replicate success in other areas: Which outcomes with similar datasets should be considered next? Project leaders should share the wins internally to build organizational momentum. Finally, after completing the initial project, companies will have the experience they need to draft a framework for AI governance and measurement.
A disciplined approach is essential.
AI requires a disciplined, outcome-driven approach, and careful preparation is essential. It’s well worth it, though. Brands that treat AI adoption as a well-designed, thoughtful journey, with buy-in throughout the company, will be the companies that can overcome today’s supply chain challenges and create a lasting competitive advantage.
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