Case Study
Bug Sorting vs Company-Wide Chatbot
The Challenge
Companies often pursue ambitious AI projects aimed at solving everything at once. While the vision of a comprehensive AI solution can be alluring, these projects frequently suffer from scope creep, unrealistic expectations, and difficulty in measuring real value.
The Cautionary Tale
One team began building an internal knowledge chatbot to replace manual searches through company documentation. Despite substantial resource allocation, the project remained incomplete after more than a year, facing numerous obstacles:
Formatting inconsistencies in existing documentation
Complex access control requirements for sensitive information
Difficulty quantifying potential value and ROI
Resistance from employees unfamiliar with AI technology
Recommendations and Results
Our team recommended are more focused AI implementation with a narrow scope in an effort to build familiarity and trust in the tool. By using AI to help sort bug and project intake requests we were able to overcome all of the hurdles with the first AI solution and pave the way for future AI initiatives. Some of the advantages this new approach provided are:
Built trust with users unfamiliar with AI through a simple, tangible example
Integrated seamlessly into existing workflows
Eliminated repetitive manual tasks
Provided clearly measurable time savings and value
The Lesson
When implementing AI for the first time, start with focused, measurable projects that solve specific pain points rather than attempting to build comprehensive solutions. These smaller wins build organizational trust, demonstrate clear value, and create momentum for future AI initiatives.
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