Navigating Ambiguity to Launch an Enterprise AI Copilot
How we guided an Enterprise B2B SaaS company through the challenges of developing and launching an AI Copilot, addressing architectural weaknesses, organizational misalignments, and unclear product direction to establish a foundation for future AI-driven innovation.
The Challenge
The client faced significant hurdles in their AI Copilot project, including a lack of clear alignment between product/technical teams and leadership on AI capabilities and timelines. Shifting requirements, ambiguity between a demo vs. a full product release, and a development process that bypassed standard SDLC practices led to communication breakdowns and impacted team morale. Architectural weaknesses such as in-memory session management, fragmented configuration, and inconsistent error handling also posed risks to scalability and reliability.
Our Approach
We undertook a multi-faceted approach involving deep-dive interviews with stakeholders across engineering, product, and leadership, a comprehensive architectural review of the AI Copilot system, and an analysis of development processes and team dynamics. We provided actionable recommendations for prompt engineering, output fencing, data availability strategies, and QA automation.
The Solution
We delivered a detailed findings report outlining current state, architectural issues, organizational challenges, and strategic opportunities. Key recommendations included strengthening team structure by assigning a Scrum Master/TPM, empowering product ownership, formalizing communication rhythms, and aligning the Copilot project with portfolio-wide SDLC and Agile practices. Technical solutions involved improved prompt structures, output fencing mechanisms, and proposals for scalable session management. We also introduced automated testing frameworks to accelerate development and improve release readiness.
Results
Our engagement led to a clearer understanding of the project's complexities and fostered better alignment between leadership and technical teams. The identification of critical architectural and process issues allowed the client to refocus efforts, prioritize essential fixes for a preview release, and develop a more realistic product roadmap. Recommendations for improved prompt engineering and output fencing were adopted, enhancing the AI Copilot's reliability. The groundwork was laid for addressing data availability limitations through initiatives like a planned QueryAPI and considerations for a semantic data layer.
55
Merged PRs in Feb 2025, highest month
4
Core engineers driving most development
3-4
Months estimated to recreate core Copilot functionality
"Your quick impact and ability to constructively challenge our thinking has been invaluable. The detailed feedback and strategic insights are helping us navigate complex challenges and get to the best answers for our AI initiatives."
Senior Executive
Product & Engineering Leadership, Enterprise B2B SaaS
Project Details
Industry
Enterprise B2B SaaS
Company Size
Enterprise
Location
Global operations
Services Provided
- AI Product Strategy
- Technical Architecture Assessment
- LLM Prompt Engineering & Optimization
- Agile Process Improvement
- QA Automation Strategy
- Stakeholder Alignment
- Roadmap Development Support
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