project44 Accelerates AI-Powered Supply Chain Transformation at Decision44 Event

Supply chain visibility provider project44 is aggressively embedding artificial intelligence across its platform, fundamentally altering how shippers and logistics providers manage operations, according to insights shared at the company's Decision44 customer event. Three senior product leaders outlined distinct AI strategies that signal a broader industry shift toward cognitive automation in maritime and supply chain logistics. **Mo: Chatbot Solving Chargeback Disputes** Staff Product Manager Nimrit Vest oversees Mo, a specialized supply chain chatbot that analyzes company operational data rather than general web content. The chatbot's primary use case addresses a persistent pain point: resolving chargeback disputes by identifying delay origins and responsible parties in multi-party supply chains. Mo employs layered artificial intelligence skills—understanding p44's data semantics combined with customized 'context modules' that incorporate customer standard operating procedures and business rules. Previously at Flexport in operations, Vest manages three senior engineers overseeing around-the-clock coding agents, signaling the resource intensity required for enterprise AI applications. "The line between what an engineer and a PM can do has blurred extremely fast," Vest observed, noting that product managers now draft skill files in Claude and review engineering work via GitHub with AI assistance. **Intelligent TMS Suite Compressing Development Cycles** Senior Staff Product Manager Ilias Pagonis leads the Intelligent Transportation Management System (TMS) product suite, managing distributed teams in Bangalore and Amsterdam. His background at Nike in supply chain operations informed the strategy of deploying AI to dramatically accelerate development. "Something that took weeks takes days if not hours," Pagonis stated regarding prototype development timelines. The team is leveraging AI-generated product requirements documents from customer interview transcripts, with approximately 65% of code initially drafted by artificial intelligence. Critically, Pagonis emphasized the importance of architecture: decomposing workflows into atomic components reduces hallucination risks in AI systems and builds customer confidence. The procurement workflow alone contains 6-7 specialized micro agents, each designed for discrete functions. **Autopilot: Human-Centered Workflow Automation** Director of Product Management Nick Ruggiero developed Autopilot, targeting low-risk data quality agents as a foundation for broader workflow automation. This cautious approach addresses lingering industry skepticism about autonomous systems. "Agents collaborate with humans in the same way that humans collaborate with humans," Ruggiero explained, describing how Autopilot integrates into transportation management systems without requiring new infrastructure investment. **Industry Implications** The project44 initiatives reflect industry-wide recognition that AI can compress development cycles, enhance operational insights, and reduce manual dispute resolution—critical advantages in maritime logistics where documentation complexity and multi-party coordination create friction. The emphasis on auditability and human oversight suggests vendors are learning from early AI deployments that failed to gain adoption due to trust concerns. As supply chain software providers race to differentiate through AI capabilities, project44's structured approach—decomposed agents, context-aware systems, and human-centric workflows—may become a benchmark for responsible AI integration in maritime logistics.