In an effort to consolidate information and portfolios, my career stories and a few other items have been moved to luis-figueroa-ops.github.io, one place for my operations impact, my tools, and my certificates. This site remains as an archive of the process that got me there.
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Hello! Welcome to my portfolio site.
My Purpose
Turning systems, data, complex processes and procedures, and AI into practical solutions that elevate how field teams operate and perform, by forecasting operational needs, bridging business and technology, driving adoption, improving workflows, and leveraging my analytical and AI capabilities from my past Verizon role while continuously advancing through independent upskilling projects to deliver meaningful impact.
Google_PM_Cert_Coursera ARK9AH46X9YO.pdfAI doesn't break processes. It exposes them.
Most AI failures aren't technology problems. They're operations problems. Before you automate, you have to diagnose. This Operations, Augmented carousel uses three verified 2025 case studies to show what happens when companies skip that step - and what the billions lost in 2025 actually tell us about what it means to augment a process you fully understand.
Auditing an Inherited Process You Did Not Build
You just inherited a process. Nobody left you a manual. That is the most common operational handoff in any organization - and it is also the riskiest. The process works until it does not, and when it breaks, you are the one who owns it.
The fastest way through it is to audit before you optimize. Talk to the people doing the work. Watch the process run. Collect everything - notes, emails, screenshots of the spreadsheet that was never supposed to become the system of record.
Then use AI to turn that raw input into a structured process map. One prompt can convert your audit notes into a working draft you can bring into a team conversation the same day.
What is the messiest inherited process you have ever had to figure out on your own?
How I Built an Automated Weekly AI Content System - No Code Required
I used to spend 45 minutes every Monday just setting up to write. Picking a topic. Pulling up my template. Remembering my own voice. Before a single real sentence.
So I automated all of it.
Using Claude Cowork, I built a scheduled task that runs every week without me. It reads my content context file, checks my used topics log, writes the CapCut script in my exact template, and drops the LinkedIn post - in my voice - into the right folders with the right filenames. By the time I open my laptop, the work is already done.
The AI Skill: teaching Claude to understand a system before it executes one. Voice guide, formatting rules, topic constraints - it reads all of it first.
The AI Productivity: I never start from scratch again. I start from good enough to edit.
Zero lines of code. Just a scheduled task, a context file, a template, and a topics log. The system prompts itself.
"72% of enterprises have AI in production. Only 9% describe their governance as mature." - McKinsey, 2024
That gap isn't surprising to anyone who has worked in operations.
I've been on the front lines of getting a tool properly approved - navigating legal, compliance, and system stakeholders before anyone touched it. But what happens when AI ships inside that same approved tool years later and nobody revisits the framework? Same approval. Completely different risk profile.
This is an active conversation happening across industries right now. And it's one operations practitioners are uniquely positioned to speak to.
I run a weekly content series on AI productivity and operations. I use these tools every day. And my position is still this: AI skill and productivity isn't the first conversation. Identifying legal, compliance, and access concerns is. Once those are resolved - then you build the workflows, then you enable the team, then you scale.
Fix the process first. Optimize before you automate.
Swipe through to see how one ops story maps to a governance gap most organizations haven't closed yet.
MavenTech AI Analyst Accelerator Full Presentation Video
Traditional courses couldn't teach me the way I learn. So I trained AI to do it instead. I didn't know SQL or Power BI 3 months ago but I do know advanced Excel. So I figured out how I learn best, then trained AI to build the exact curriculum I needed. Interactive. Conversational.
It instructs me, quizzes me, answers my questions, and teaches me how to use AI the way companies actually want. This video shows where that journey has taken me.
8,800 deals. 30 reps. One dataset. SQL → Excel → Power BI.
I didn't wait for the right course. I built it.
I didn't put my career on pause. I used the time to level up in ways my next employer will feel immediately.
4 months ago I didn't know where to start with AI. I wasn't sure what was hype and what was real. I didn't have a roadmap. I just knew I couldn't afford to sit on the sidelines and wait for it to make sense on its own.
So I started. Imperfectly. One thing at a time. And now I look at what I've actually built - and I barely recognize where I was when I began.
If you're in that early stage right now - confused, overwhelmed, not sure which tool to learn or where to focus - I want you to know that's exactly where I was.
You don't need a perfect plan. You need a first step. It's still early. That's not a warning. That's an opportunity. The slide below is a snapshot of what 4 months of showing up looks like. (January 2026 - April 15, 2026 when this was posted)
Every industrial revolution had winners and losers. The difference was never who got the technology first. It was who was ready to absorb it.
We're in the 4th Industrial Revolution right now - and most organizations are treating AI like a software update. It's not. It's a workforce restructuring event. The companies that aren't positioned for it yet aren't necessarily behind - but there's a window. And windows close.
When the rubber meets the road, the organizations that survive won't be the ones who panicked and bought every tool on the market. They'll be the ones who already had the people, the processes, and the infrastructure to absorb the shift.
That's what I'm building toward - and what I think every operations professional should be thinking about right now.
Swipe to see why ops people are the most underestimated asset in the AI era.
VSL Process Mapping: L1 · L2 · L3 Documentation
Most operational failures aren't process failures.
They're people failures. Someone wasn't informed. Someone didn't have access. Someone was left holding a problem with no clear path forward.
I've spent my career making sure that doesn't happen - understanding everyone the work touches before touching the work itself.
When a company reorg eliminated an entire support layer overnight, that instinct is what drove every decision that followed.
The slides tell the rest of the story.
Have you ever inherited a broken process and had to rebuild trust with the people inside it before you could fix anything else? That's the real work.
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