Teach Teams to Fish: A Project Prioritization Case Study (1 of 3)
This is Part One of a three-part post (read parts 2 and 3).
<div class="concept-tag">Project prioritization</div>
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<div class="concept-text">Project prioritization is a common challenge for organizations of all sizes. Many companies find themselves overwhelmed with dozens or even hundreds of project proposals annually, each competing for limited resources and budget. The key question becomes: How do you systematically identify which projects will deliver the most value while remaining feasible within your constraints?This challenge is particularly acute in larger organizations where proposals come from multiple departments, each with their own priorities and perspectives. Without a structured evaluation process, decisions can become political rather than strategic, leading to suboptimal resource allocation.</div>
At Luminate, we believe in teaching our clients to fish rather than fishing for them. Recently, a client came to us with a common challenge: hundreds of project proposals flooding in annually, each competing for limited resources. But instead of just developing a prioritization framework for them, we showed them how to collaborate with AI to build it themselves.
"We want to use AI, but we don't know where to start," their CTO told us. "And we need this framework to last beyond just one consultation."
This approach — teaching AI collaboration while solving a specific problem — creates lasting value. The client learns both how to work with AI and gets their immediate need met.
Starting the AI Journey
First, we needed to show our client how to approach AI as a collaborative partner. We sat with their team and demonstrated how how we start a conversation with AI about this challenge:
We then showed them how to iterate on the response. We pointed out how the AI gave us broad principles, and it was time to make it more specific:
This demonstration helped them understand the iterative nature of AI collaboration.
Building the Framework Together
Then we guided the client through developing three critical stages, teaching AI prompting principles along the way.
We guided the client through developing three critical stages, teaching AIÂ prompting principles along the way:
1. Initial Assessment Stage
We showed them how to develop this by asking:
Then we demonstrated refinement:
Pro Tip: We taught them to always include their context and constraints in prompts.
2. Detailed Analysis Stage
Here, we demonstrated ways to build on previous responses:
Teaching Moment: We showed them how to identify when AI responses needed human expertise to validate them.
3. Decision and Implementation Planning Stage
By this stage, they were starting to craft their own prompts, with our guidance:
Key Lessons We Taught
Throughout the process, we emphasized these AI collaboration principles:
- Start broad, then narrow down
- Always provide relevant context
- Ask for explanations, not just answers
- Iterate on responses
- Validate against real-world experience
A breakthrough moment came when our client independently asked AI:
This showed they were beginning to understand how to blend AI's analytical capabilities with their own organizational knowledge.
Moving Forward
In our next post, we'll share how we guided the client to implement their framework, including how they learned to use AI for creating visual process flows and handling stakeholder communication.
The real success wasn't just the prioritization framework they developed - it was their newfound confidence in collaborating with AI to solve business challenges.
Go on to Part 2, where we explain how we teach teams to use AI for process design, visualization, and documentation creation through iterative prompting and practical examples.