Maersk Quotation AI Assist (MQAIA)

Strategi

The client's RFQ (Request for Quotation) process was a critical bottleneck in the Quote-to-Cash lifecycle, hampering business responsiveness and revenue potential. The process relied on manual intervention, fragmented workflows, and tribal knowledge to process 74 non-standard RFQ files daily, with each file taking up to 4 hours - amounting to 296 hours of manual effort every day. The absence of standardized formats led to inconsistent data structuring, unstructured file uploads, and frequent manual coordination between Sales and Pricing teams - delaying response times and limiting the Quote-to-Contract conversion rate to just 20%.

Key Challenges:
• Manual Dependency: High effort-intensive process consuming skilled resources.
• Slow Turnaround Time: Delayed quote generation impacting customer experience.
• Tribal Knowledge Reliance: Critical business knowledge confined to select individuals, limiting scalability.
• Lack of Standardization: Unstructured RFQ formats causing fragmented processes.
• Business Impact: Low Quote-to-Contract conversion rate, resulting in missed revenue opportunities.

With rising RFQ volumes and increasing customer demand for faster quotes, the existing process became a significant roadblock to business growth. Traditional automation approaches failed to address the complexity of unstructured RFQ formats, making GenAI the strategic differentiator to transform the process.

Why GenAI?
GenAI-powered RFQ automation redefined the process by automatically structuring, validating, and processing non-standard RFQs - eliminating SME dependency, accelerating turnaround times, and ensuring consistent data quality. The solution seamlessly integrated with pricing systems, enabling real-time quote generation, higher RFQ utilization, and increased conversion rates.

This transformation not only optimized operational efficiency but also delivered tangible business outcomes - positioning the client as a frontrunner in the digital logistics revolution and unlocking significant revenue potential through faster, smarter, and scalable RFQ processing.

Løsning

IBM harnessed its deep AI expertise to design, train, and deploy an enterprise-wide GenAI solution built on the Azure OpenAI stack, transforming the client's RFQ process into a fully automated, intelligent workflow. The solution was tailored to address the complexities of unstructured RFQ documents while ensuring seamless business integration.

Key Solution Capabilities:
• AI-Powered Document Understanding: The GenAI model intelligently extracts, validates, and structures RFQ data, significantly reducing manual intervention and accelerating processing speed.
• Contextual Awareness: Unlike traditional AI, GenAI is equipped with business-specific contextual intelligence, enabling it to understand nuanced RFQ formats, pricing terms, and client-specific requirements - delivering high accuracy even in non-standard documents.
• Adaptive Learning Models: The solution continuously evolves through real-time feedback, enhancing precision and reliability with every interaction - ensuring sustained performance improvement over time.
• Seamless System Integration: Built with native interoperability, the GenAI solution integrates effortlessly with the client’s existing digital platforms, enabling end-to-end RFQ automation from data ingestion to quote generation without disrupting ongoing operations.

By combining cutting-edge AI technology with contextual business knowledge, IBM delivered a solution that not only streamlined RFQ processing but also unlocked faster response times, improved conversion rates, and scalable operational efficiency - empowering the client to lead the digital transformation of the logistics industry.