This class project, in partnership with Starbucks, focused on optimizing their innovation testing process. The goal was to streamline workflows, enhance data synthesis, and improve overall efficiency for Starbucks’ testing teams by integrating AI tools.
Objectives
The needs were to clarify feedback, manage data inflows efficiently, and streamline the synthesis process for quicker, more accurate research analysis.
The project aimed to achieve the following objectives:
To make ambiguous feedback and sentiment clear, distinguishing between operational ease and concept affinity for more accurate insights.
To efficiently manage overwhelming data inflows from various sources, reducing the labor and error rates associated with manual processing.
To streamline the slow and manual synthesis process, allowing for quicker and more comprehensive research analysis.
Constraints
The existing challenges were data security restrictions, high volumes of data to synthesise, and resistance to adopting new tools and processes.
Data Security Restrictions
Access to real Starbucks data was restricted due to security concerns, preventing the use of AI for data processing. And so we used mock data for testing and development.
High Workload
A single team member managed 7-8 tests at once, each with 500-600 participants, leading to a large volume of data to synthesize.
Resistance to Change
Teams were accustomed to older tools and were hesitant to adopt new systems.
Desired Impact
The desired impact was to optimize efficiency, ensure seamless integration, provide accurate insights, and enhance research capabilities through automation.
Efficiency and Optimization: Achieving a more efficient and optimized testing process that saves time and resources.
Seamless Integration: Ensuring the tool integrates smoothly with existing workflows and is easy for the team to adopt.
Improved Decision Making: Providing accurate and actionable insights from synthesized data to inform strategic decisions.
Enhanced Research Capability: Increasing the capacity to conduct and analyze research due to automation and improved data management.
Solution
We proposed immediate interventions, implementation steps and future prospects of integrating AI into the process.
Using agile methods, we effectively collaborated and communicated. We first mapped the current process to identify areas of opportunity, then proposed several immediate interventions along with horizon 2 and 3 AI-integrated solutions.
impact
“The things that really hit home for me are the redesigned survey and nineteen (tool).”
- Sarah Rigor, Sr Operations Consultant Manager at Starbucks
Reflections
Thoughts about the project.
Ensuring the security of sensitive organizational data is crucial, as many AI tools currently lack robust encryption features.
Adopting agile methods across the team streamlines workflows, allows for more focus on the project rather than administrative tasks.
Implementing screener criteria for interviews can help ensure their usefulness and relevance.
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