Situation
Our retail client spotted an opportunity to uncover operational improvements using AI.
Our client had over 20 data sources tracking operations with varying taxonomy and unstructured text comments.
Our solution gave an organizational “one view” of the top issues overall and segregated by store.
The retailer needed an AI solution to help it capture identify, classify, and resolve operational issues across multiple data sources such as call centers, telemetry data, emails, customer complaints, customer feedback, returns, and incorrect fulfillment.
Action
We leveraged a set of pre-trained generative AI transformer models to process multitudes of sources of data to identify the gaps and missteps in retail operations. An Operations Center of Excellence (CoE) was established to identify and act on retail operations improvement opportunities.
Our solution produced an organizational “one view” of the top issues overall and segregated by store. The goal is to use the top issues to send the relevant data to teams downstream such as store operations and vendor management. Then it’s used to arrive at machine recommended resolution and outcomes to improve the associate and customer experience.
Multiple generative AI models were created to classify, prioritize, and define the root cause analysis.
Natural Language Processing (NLP) including Generative AI models will be built across the entire lifecycle of issues from identification to root cause analysis and routing.
Results
Expected and preliminary results include:
- Better resource management and prioritization across all stores.
- Associates are now automatically assigned issues for resolution.
- The process uncovers the root cause of resolution and feedback loops to prevent issues from resurfacing at prior levels.
Associates are now automatically assigned issues for resolution.
Data helps downstream teams know when to reallocate resources.
We leveraged pre-trained generative AI models to identify the gaps and missteps in operations.