By Thariq Kara, Co-Founder and CEO, BITE Data
Discussing automation and AI in supply chain, logistics, and trade compliance hasn’t always been easy. However, over the past year, there has been a growing acknowledgment—perhaps even an inevitability—that AI will play a significant role in specific practice areas, including that of ‘AI agents’. These are intelligent tools that enhance efficiency and accuracy by collecting data, learning from experiences, and making informed recommendations to help users achieve their goals. At the risk of throwing in another term: one AI advancement gaining traction is multimodal AI—a technology capable of integrating multiple types of data sources (text, images, geospatial data, and structured databases) into a single decision-making process. This approach is proving to be a game-changer for industries that require real-time risk assessment, including trade compliance, supply chain analytics, and defense applications.
The Rise of Self-Improving AI Agents
The emergence of self-improving AI agents marks a fundamental shift in AI development. These advanced models not only interpret data but also refine compliance screening rules, uncover hidden risks, and proactively alert teams to potential issues—often before human analysts detect them. This represents a fundamental evolution from rule-based screening to AI-driven anomaly detection.
Bringing AI Agents and Multimodal AI Together
When an AI agent is equipped with multimodal capabilities, it can leverage multiple information sources to make more informed decisions. By integrating text, images, databases, geospatial data, and real-time analytics, these AI agents deliver more comprehensive and contextually aware insights—providing businesses with a new level of operational intelligence.
What Does This Mean for Trade Compliance and Supply Chain Monitoring? Here are some use cases to provide context for where AI can support trade compliance:
- Enhanced Entity Resolution: AI can now recognize related entities across multiple languages, aliases, and regional data sets with unprecedented accuracy. This is essential for screening restricted parties and determining import/export control regulations based on a commodity name or Harmonized Tariff Schedule (HTS) code.
- Regulatory Adaptation in Real-Time: AI continuously adapts to new compliance requirements without the need for manual updates (as long as the data source is refreshed) – for example, being able to understand downstream effects of new tariffs on your supply chain in an increasingly complex regulatory landscape
- Proactive Supply Chain Risk Assessment: AI-driven simulations can predict trade disruptions based on real-world signals, including geopolitical events, economic trends, and shifting regulations.
The Future of AI in Trade Compliance
As multimodal AI agents become more sophisticated, their impact on trade compliance will expand. Companies that embrace these technologies early will gain a competitive edge through greater efficiency, improved risk management, and enhanced decision-making. However, challenges remain—ensuring data privacy, mitigating bias in AI models, and navigating evolving regulations will be key to responsible adoption.
Multimodal AI may not yet be mainstream in trade compliance, but its adoption is inevitable. As global trade complexities increase, businesses will need AI solutions capable of synthesizing vast amounts of data in real-time to maintain compliance and operational efficiency.
How do you see multimodal AI shaping the future of trade compliance?
