The Reality of AI Consulting: Ambiguity, Requirements, and Different Business Models - Việt Hoàng, Nghiêm

This is a placeholder for a future blog post about the realities of AI consulting. I’ll share insights from my experience working in different consulting environments and dealing with the challenges that come with AI projects.

Topics I plan to cover:

🤔 Dealing with Ambiguity

Why AI projects are inherently ambiguous
How to navigate unclear requirements
Techniques for managing uncertainty in consulting

📋 Requirements Gathering in AI

Why traditional requirements gathering doesn’t work for AI
How to identify what clients really need vs. what they ask for
The importance of iterative development and feedback

🏢 Different Types of Consulting Businesses

Big 4 Consulting (KPMG experience)
Small Boutique Firms (DataSpark experience)
In-house vs. External Consulting
Pros and cons of each model

🎯 Project Management Challenges

Managing client expectations in AI projects
Dealing with scope creep and changing requirements
Balancing technical excellence with business needs

💡 Lessons from the Field

Common pitfalls in AI consulting
How to build trust with clients
The importance of communication and education

🚀 Success Strategies

How to deliver value even with ambiguous requirements
Building long-term client relationships
Staying relevant in a fast-changing field

Coming soon…