From Theory to Practice: AI-Assisted Process Design (2 of 3)
This is Part Two of a three-part post (read parts 1 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>
Building on our initial work teaching AI collaboration for project prioritization, our client was ready to move from framework to implementation. This phase revealed how AI can help refine and visualize processes - and how to teach teams to guide this effectively.
The Challenge of Making It Real
"Now we have our framework," the project lead said, "but how do we turn this into something our teams can actually use?"
This is where AI's ability to help with process design really shines - if you know how to ask, assess, persist, and follow up.
Teaching Process Design with AI
We showed them how to start with process mapping using Claude, which will happily create actual workflow diagrams—right in the interface, using an open-source markup code called Mermaid.
The client was delighted when Claude generated a diagram right there in our conversation. "Wait - it can just make diagrams like that?" they asked. This immediate visualization capability helped them grasp how AI could concretely improve their workflow documentation, not just generate text suggestions.
That said, the first diagram was way too complex for what they hoped to see. But that gave us an opportunity to demonstrate approaches to iteration.
This produced a clearer, more focused workflow:
Teaching Document Creation
Now the client was off to the races.
We showed them how to get AI help to craft supporting documents that met their needs:
Pro Tip: We taught them to always request example content along with templates:
Unexpected AI Insights
A powerful teaching moment came when we showed them how to use AI for edge cases:
The AI identified several scenarios they hadn't considered, leading to valuable refinements in their process.
Building Confidence Through Iteration
Then we guided them through increasingly sophisticated prompts:
By the end, they were crafting complex prompts independently, combining process design with their organizational knowledge.
Go on to Part 3: We share how we taught them to use AI for ongoing process refinement and adaptation.