AI Assistant Discovery: Learning the Limits of Early GenAI
A major coffee chain wanted to explore using AI for personalized customer service on their ordering platform. They envisioned an assistant that could handle order modifications, provide real-time updates, and create personal connections with customers - pushing the boundaries of what was possible with early 2023 GenAI technology.
The Challenge
A major coffee chain wanted to explore using AI for personalized customer service on their ordering platform. They envisioned an assistant that could handle order modifications, provide real-time updates, and create personal connections with customers - pushing the boundaries of what was possible with early 2023 GenAI technology.
Our Solution
Led a comprehensive discovery process that revealed the gap between stakeholder expectations and technical capabilities. Successfully communicated complex technical limitations in business terms, helping the client understand why their vision wasn't achievable with current technology while mapping a realistic path forward.
Our Approach
- 1Evaluated early Gemini capabilities against client requirements
- 2Built prototypes to demonstrate actual vs expected behavior
- 3Translated technical constraints into business implications
- 4Identified specific gaps in personalization capabilities
- 5Documented learnings for future implementation timing
- 6Maintained stakeholder alignment through clear communication
Technologies Used
Results & Business Impact
Key Outcomes
- ✓Clear understanding of technology-expectation mismatch
- ✓Prevented premature deployment and brand risk
- ✓Established realistic AI implementation timeline
- ✓Built foundation for future success when tech matures
Business Impact
- →Saved significant resources by identifying limitations early
- →Protected brand from problematic AI deployment
- →Educated leadership on realistic AI capabilities and timelines
“While we didn't get the AI we initially wanted, we got something more valuable - a clear understanding of what's actually possible and a trusted advisor who could explain it without the technical jargon.”
Key Learnings
Sales promises vs technical reality often clash in early AI
Transparent communication about limitations builds more trust than overselling
Sometimes the best implementation is knowing when NOT to implement
Bridging technical and business language is crucial for AI projects