How Your Technical Architecture Can Unleash AI

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How Your Technical Architecture Can Unleash AI

Ethan Morgan, February 24, 20252 minute de lecture

Blue Yonder research found that 80% of global organizations have piloted or implemented generative artificial intelligence (AI) in their supply chains. However, going beyond a pilot and embedding AI into business processes to truly realize its maximum potential is a major challenge. According to the Project Management Institute, between 70-80% of AI initiatives end in failure, highlighting just how difficult it can be to plug existing general AI and machine learning (ML) tools and tech into industry contexts.

One of the biggest barriers is in technical architecture. Point solutions for supply chain processes are not fit to deliver AI the data it needs. Businesses reliant on point solutions and batch processes aren’t able to give AI tools the right quality of data, quickly enough, and lack the scope of end-to-end vision to ensure that the AI tooling they’re adopting will offer valuable decisions or optimizations.

In this blog, we’ll explore three ways that supply chain technology architecture can be improved to allow businesses to adopt and realize value from AI – and show how Blue Yonder’s technical architecture is purpose-built for just that.

AI-tuned data model

A common data model serves as a standardized framework that defines how data is structured and interconnected across various systems and applications. It provides a unified schema, ensuring that data is consistent and interoperable so that different systems communicate effectively.

An AI-tuned data model is structured in a way that allows first-party AI/ML to leverage the data with greater speed and accuracy, leading to faster recommendations and root-cause analyses. For example, data in this form can be used by Blue Yonder’s industry-leading ML and AI capabilities, which create accurate and explainable insights across products. Crucially though, an AI-first common data model also enables the data to be easily ingested and utilized by external AI agents. These agents can engage with both the first-party business data as well as third-party data from beyond the business to make better-informed predictions and suggest the right actions.

Without designing this key architectural piece for the needs of AI, external AI agents will struggle to ingest business data and make relevant recommendations. To make sure our customers can effectively adopt cutting-edge agentic AI, Blue Yonder has delivered an AI-tuned data model in the latest product release (24.4) to both improve our embedded AI performance and to enable easier integration with external AI agents and applications.

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Studios, rehearsals and tuning

Just like musicians need to warm up, get their instruments in tune and develop their sound in a studio environment, AI models often need some experimentation and fine-tuning to maximize their value. However, data scientists don’t always have an easy way to rehearse. It can be tough to connect to relevant internal and third-party data, build or refine models, and put top-performing models into production at scale without the right technical environment.

Thanks to the latest product release, Blue Yonder customers now have access to ML Studio, enabling them to find the machine learning model that harmonizes best with the specific use case. ML Studio lets data scientists use a familiar environment to build machine learning models that address specific outcomes and scenarios. This process allows businesses to identify which models will fit best with their needs, before deploying them live and at scale with ease.

With ML Studio, Blue Yonder leads the way in offering a flexible, customizable machine learning development toolkit, offering extensive potential for achieving additional value from AI.

Scenario modelling

Trying to predict global demand is one of the biggest challenges in supply chain. Building a single plan that works well enough no matter what happens is an impossible task for human planners with limited visibility and access to data, particularly when they must spend a lot of time manually creating each forecast and plan. Even when scenarios can be automatically generated, legacy methods can take hours, delaying critical decisions.

Embedding AI deeply into the processes of key strategic functions like planning is a huge advantage for leading supply chain operators. The ability to model thousands of scenarios in minutes is a game changer, especially when tuned for specific business goals and including highly accurate AI-driven demand signals. Optimized plans allow businesses to better leverage available supply to meet demand as it happens.

Having the technical capacity to deliver such rapid modeling, adjusted specifically for your business and the strategic outcomes you deem most critical, allows planners to quickly construct and evaluate more potential plans for maximum flexibility.

Blue Yonder’s approach sets a new standard for fast, accurate scenario planning that helps businesses adapt much faster to disruption and changing demand,

We’re on a mission to transform the supply chain landscape, empowering businesses to navigate the complexities and volatilities of the modern supply chain.

Our end-to-end platform and digital network create an interconnected ecosystem, breaking down silos and enabling seamless collaboration among trading partners. This integration ensures that planning, execution, and collaboration are streamlined, allowing businesses to swiftly adapt to changing market conditions and customer demands. The result is reduced risk, enhanced efficiency, and optimized performance.

Discover how our latest product updates advance this mission with enhancements in AI and ML capabilities for Inventory Optimization, Demand Planning, Supply Planning, Integrated Business Planning, and Integrated Demand & Supply Planning.

AI isn’t all that we’ve worked on in the new release – find out more about it here.

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