Writing on AI workflows and operations

Is there anything more exciting than teaching a consultant something new? I worked with a Wrike consultant who was excellent at helping build a new framework in Wrike, and one thing she pointed out was my use of the Wrike Database features.

In marketing, specs change constantly, especially for social. I got tired of updating every blueprint each time a size changed, so I built a Creative Specs Database linked to each creative asset custom item type across our blueprints. Now when a spec changes, it's one change in the database and it updates across the entire platform, saving hours of rework.

I love that Wrike keeps rolling out features that let us keep scaling our work.

See the full post on LinkedIn →

I accepted a project without fully understanding how much the scope had shifted. I came back from leave to find it had grown from a handful of projects to over 100, all of which needed to go into Wrike.

Instead of entering everything by hand, I used Claude to clean up the data, and through our Claude MCP it pushed all the projects into Wrike for me automatically. What would have eaten up hours was done in minutes.

It's a great reminder of how AI can accelerate our workflows, especially by automating the small, time-consuming tasks that would otherwise take over your day.

See the full post on LinkedIn →

I keep meeting people who want to use AI but feel stuck at the 1:1 chat. They get the conversation, but going beyond it feels intimidating. A developer might call my approach simplistic, and that's fine. I learn by doing and by not being afraid to make mistakes.

When I don't know how to do something in Claude, I just ask it: "Walk me through step by step how I should do this." If a response doesn't make sense, I ask it to clarify. That's really all it takes when you're starting out and trying to understand how the models work.

To help others get past that first hump, I take my own setups and ask the tool to write markdown files and instructions for my coworkers, so they get a faster start than I did. That's how adoption actually grows. You don't leave each person to figure it out alone. Sometimes you have to break it down so the least technical person on the team can pick it up and run with it.

See the full post on LinkedIn →

With Fable 5 about to move to API, I spent my last stretch with it asking one question: where are my own workflows inefficient? It started building skills based on how I actually work, including one that helps other models think more like Fable and produce better output.

A lot of people with Claude licenses have never even tried Fable. The only real limit is your own imagination. In a short window I built:

• An OFT newsletter builder that exports to Mac and PC
• This portfolio site, something I had never made time for
• A European Portuguese learning app, since every other app teaches Brazilian Portuguese
• A social bookmarking platform from one simple prompt
• A code and security audit of my projects
• The bones of a credit card points platform

Here is how it connects to my day job. Every mistake, misprompt, and bad instruction in a personal project teaches me something I can carry into how I use Claude at Blue Yonder. I have started building AI Advisors for the tools I manage, training Claude to pressure test agentic solutions that actually move the needle. AI is meant to work alongside us, not replace us.

See the full post on LinkedIn →

Everyone wants AI to do the impressive work. Strategy, creative, big decisions. But the best first agents are boring: chasing approvals, compiling status updates, checking that a request form is actually complete.

Those small tasks are where a team's week quietly disappears. They're also the easiest to automate well, because the rules are already clear. Nobody debates what a complete intake form looks like. Someone just has to check.

My 7-agent playbook started there. Intake checks, request routing, deadline alerts, follow-up nudges. None of it demos like magic. All of it gives hours back every week. Start boring. The impressive stuff can wait.

Every week I see a team bolt an AI agent onto a workflow nobody has looked at in years. The result is predictable. The same bad handoffs, just faster. The same unclear ownership, now with a bot in the loop.

At Blue Yonder, the 80% productivity gain didn't come from a tool. It came from mapping how work actually moved, cutting the redundant steps, and only then putting a platform under it. Automation was the last step, not the first.

Before you build an agent, put the workflow on paper. If you can't explain the process to a person, you can't explain it to a machine. Fix the process first. Then automate it.