AI tools may be here for the supply chain, but companies aren’t necessarily ready or willing to use them.
Inspectorio uncovers this fact in “Trends in AI Adoption Across Retail Supply Chains,” the first part of its State of the Supply Chain Report 2026 series. Here, HGI delivers an exclusive first look at the report which exposes the limitations, concerns and opportunities brands and retailers face as they grapple with how to leverage this new technology.
The report findings are derived from a survey of how business leaders are using technology to manage global production networks. The results show that while retailer interest in AI is accelerating rapidly, it is still primarily used to support existing supply chain processes.
AI adoption jumped significantly this year, with 40% of respondents in 2026 reporting use of AI across their supply chain operations, compared to 27% in 2025 and 24% in 2024. Despite this growth, AI often remains stuck in the “pilot” phase, and 25% of respondents said they didn’t even know whether their company was using AI at all.
Adoption is also falling short of the technology’s full potential. AI is mainly being used as a productivity tool to improve low-risk workflows, while high-stakes domains (i.e., dynamic sourcing decisions, real-time production rebalancing, demand-driven allocation) continue to rely on human judgment, slow and flawed as that may be. The report found that companies use AI for quality inspection and defect detection (74%), supplier risk monitoring (55%), supplier performance management (47%), and compliance screening (43%).
Organizational and Technical Barriers
Overall, companies show a gap between intent and action. Interest and adoption in AI are accelerating, but many organizations still lack the infrastructure to effectively deploy these tools. As for what’s holding them back, survey respondents cited budget constraints or competing priorities (39%), integration complexity with existing systems (36%), and a lack of internal skills or change management capacity (36%).
Companies must also weigh the “pragmatic with the practical,” balancing internal visionaries who champion AI’s potential with those concerned about negative financial or PR outcomes.
Adoption often comes down to trust and there is still some trepidation. Leadership tends to be less forgiving when it comes to AI, demanding “perfection” before full acceptance while remaining protective of proprietary data. Organizations reject tools that are 80–90% accurate, “while ignoring that very little in business achieves perfection,” the report stated, noting the organizations furthest ahead tend to be those willing to deploy imperfect tools, learn from outputs and iterate to improve.
“Building trust in AI requires two things: understanding the data behind it and being able to validate its accuracy,” said Scott Lercel, VP of global sourcing, Dick’s Sporting Goods. “Today, AI is best used to inform decisions and strengthen connections as organizations learn and improve.”
Even when data is available—and trusted—it is often fragmented and duplicative, creating redundancies across multiple brands. Audits are a clear example, with critical information siloed across systems, leading to audit fatigue that wastes energy, time and money.
“If you don’t have credible data that is specific, structured and matched to actual products, there’s no way you can use AI effectively,” said Thiwanka De Fonseka, chief sustainability officer of Komar. “And manual validation will not work at the scale we need.”
Stronger Monitoring
With AI adoption so focused on process-level tasks, and many companies still lacking the data quality, architecture and integration readiness needed to make AI truly effective, the question remains: can the industry advance to the next level?
To catch risks before they escalate into crises, technology is increasingly needed to connect trade compliance, demand volatility and social responsibility regulations. Industry leaders recognize this, but don’t always act. The survey revealed that almost two-thirds (64%) of respondents cite stronger supplier monitoring as a primary risk strategy, yet only 15% have pursued technology investments specifically to improve sourcing agility.
“These functions hadn’t been very connected before,” said a global trade and compliance executive at a U.S. retailer. “Social responsibility was over here, trade compliance was over there. But now, if your social compliance isn’t right, your goods don’t clear the border. That changed everything.”
To move forward, companies must first achieve what Inspectorio calls “data gravity,” or primary data collection at scale. By capturing structured, first-party data directly from the source (production floors, audit cycles and field operations), AI models can make more accurate compliance and sourcing decisions.
“The results are tangible,” noted Nathaniel Fleming, vice president of product marketing at Inspectorio, discussing the company’s AI-powered supply chain platform Paramo. The report cites one large-scale retailer that reduced its defect rate by 66% over seven years, preventing an estimated 1.4 million defective units annually.
Using a software “intelligence layer,” Paramo’s co-pilots also compress decision cycles. One retailer reduced compliance assessment cycle times by 68% and corrective action volumes by 38%. Taking it a step further, Paramo’s AI agents turn insights into autonomous execution, handling tasks such as document validation, traceability, regulatory compliance benchmarking, and generating corrective and preventative actions when necessary.
Get an early advantage. Part 1 of Inspectorio’s State of Supply Chain Report 2026 is available a week early for HGI subscribers.
The report highlights:
- What respondents who have not adopted AI cite as their main barriers.
- What’s holding companies back from deriving more value from AI by leveraging it in higher-stakes areas like dynamic sourcing and production rebalancing.
- How a global apparel manufacturer achieved a 75% reduction in defect rates across its sample-inspected population.
- How one major retailer saw a 93% drop in failed inspections over seven years, avoiding an estimated $4.8 million annually in re-inspection churn, rescheduling and delay handling across more than 116,000 inspections.
