Tech • IA • Crypto
Telus: AI Boost Bites: Build Your AI Research Sidekick
5/10TL;DR
Telus integrated customized AI “gems” into its UXR workflows, cutting analysis time and turning large-scale customer feedback into actionable insights.
KEY POINTS
Data overload in UXR
Telus’ User Experience Research team faced an excess of customer feedback, with tens of thousands of comments available but limited ability to extract meaningful insights at scale. While quantitative metrics were clear, understanding the underlying reasons behind user behavior remained buried in unstructured text.
Shift from data handling to insight generation
The team identified that too much effort was spent managing and sorting data rather than acting on it. This created a bottleneck where insights were delayed, reducing the team’s ability to respond effectively to customer needs.
Embedded AI integration approach
Instead of deploying AI as a standalone tool, an integration team worked directly within existing workflows. This led to the creation of customized AI assistants, referred to as “Gemini gems,” tailored to the team’s specific research and analysis processes.
Custom AI personas for precision
Each AI gem was designed with a clearly defined persona, such as a UXR research assistant tasked with delivering deep analysis, concrete recommendations, and verbatim customer evidence. This specificity improved the relevance and usability of outputs.
Structured data for reliable analysis
Proper data preparation was essential to success. Consistent formatting, clear headers, and organized datasets ensured that AI-generated insights were accurate and not distorted by inconsistencies in input data.
Actionable insight generation
The AI tools enabled researchers to quickly identify top friction points in products, then drill deeper by generating supporting customer quotes and suggesting concrete actions for product teams. This bridged the gap between analysis and decision-making.
Significant efficiency gains
The implementation resulted in an estimated 450 hours saved annually, freeing researchers from manual analysis tasks and allowing them to focus on advocacy and strategic work.
Democratization of insights
By making these AI tools accessible across the organization, Telus enabled non-specialists to independently explore and validate customer insights. This reduced reliance on a single research team and broadened data-driven decision-making.
Cultural shift toward AI adoption
The initiative contributed to a transition from curiosity about AI to confident, everyday use. Teams began embedding AI into routine workflows and promoting its value internally.
CONCLUSION
Telus demonstrates that effective AI adoption depends less on introducing new tools and more on integrating them into existing workflows to unlock faster, scalable, and actionable insights.