AI Ready: Ahmad Ghabboun

Ahmad Ghabboun built a Demo Day–winning AI product during his MSIS program — after arriving with no plans to work in AI at all. He breaks down how his mindset shifted, how his design background made him a stronger prompter, and how to build AI fluency that actually holds up in interviews. Useful for students and early-career professionals trying to get AI-ready without faking it.

Ahmad Ghabboun is a Master of Science in Information Systems (MSIS) 2026 Graduate at the UW Foster School of Business. Before Foster, he spent roughly fifteen years in UX and product design, building web applications for startups. At Foster he built several generative-AI tools in his coursework, including Synapse, which won Best Business and Tech Product at the MSIS Demo Day. He is targeting product management and technical product roles.

What you'll learn
 * Why naming the specific AI model you use — and justifying it — matters more in interviews than saying "I use AI"
 * How a design background translates into sharper, more technical prompts
 * How to keep a human in the loop so AI assists your judgment instead of replacing it
 * Why AI's tendency to agree with you makes human and second-model pushback essential
 * How to stay current with fast-moving tools without trying to learn everything
 * The difference between a productivity mindset and a learning mindset in school

Key moments
 * The third-quarter AI classes that moved AI from "not on my list" to his career focus
 * The origin of Synapse: manually juggling answers across Gemini, Claude, and a third model
 * How Synapse runs a dual-model validation and a judge step to flag gaps for technical PMs
 * Why interview proctoring now detects AI use — and what a "perfect" AI answer signals to interviewers
 * Ethan Mollick's "jagged edge" and why it shifts with every model release

Resources mentioned

Lovable; Replit; Gemini; Claude; ChatGPT; Jira; Azure DevOps; GitHub; Ethan Mollick's "jagged frontier" of AI capability.

AI Ready: Ahmad Ghabboun Discovers His Interest in AI

Hannah Hoffmaster went from a self-described two-out-of-seven in technical skill to building multi-agent AI tools in a single year at Foster. This episode is for anyone — technical or not — trying to understand what genuine AI fluency looks like and how to build it.

Hannah Hoffmaster [https://www.linkedin.com/in/hannah-hoffmaster/] is a student completing the one-year MSIS program at the University of Washington Foster School of Business. She came to the program with some knowledge of statistics and R, but little coding experience. Through her coursework — including Prof. Leo Bousioux's AI and Generative AI in Business class — she developed the ability to design and build AI-powered tools, including a charity comparison platform and an ADHD-focused scheduling app. She describes experimenting with AI as something she now does for fun.

We covered alot of ground in this episode:

 * How to think about AI as a build tool when you have no coding background
 * Why "trust but verify" is the core discipline of working with AI, and how to operationalize it
 * How to design a multi-agent workflow around the parts of a task you don't want to do
 * What a deliberate, build-first job search looks like in a fast-moving field
 * How to stay current as tools change — by building, researching versions, and talking to peers
 * Why holding your career goals loosely can be an advantage in an uncertain market

Resources mentioned: GiveWise (Hannah's project); Offload and the "Nudge" chatbot (Hannah's project); Claude Code; Supabase; GitHub; Vercel; Lovable; ChatGPT; Gemini; Codex; Prof. Leo Bousioux's AI and Generative AI in Business course; Foster's AI club.

AI Ready: Hannah Hoffmaster - How a Non-Technical Student Became AI-Ready in One Year

On this episode, I speak wtih Léonard Boussioux — Assistant Professor, Foster School of Business; Adjunct, Paul G. Allen School of Computer Science & Engineering, UW. PhD, MIT (machine learning & operations research). Co-founder of Universal AI [https://www.leobix.us/uai]. "Professor Leo," as his students call him, is a leader in AI education, research, experimentation, and adoption. He and I are on the Foster AI Taskforce, and sat down for this conversation in August of 2025.

Leo rejects the career advice you've heard your entire life: pick a lane, specialize, go deep. His counter-argument is that AI now lets you be — in his words — a specialist of everything. In this conversation, we dig into what that actually means for MBA students, career switchers, and anyone trying to figure out how to use AI without offloading their thinking to it.

We cover how Leo teaches non-coders to ship working products in a single class session, how he uses six different AI models to plan a vacation (and why), the new category of jobs emerging around human-AI collaboration, and why the people who panic about AI are usually the ones who haven't played with it yet.

3 Key Takeaways

1. Drive the AI. Don't delegate to it. The students who get worse at thinking are the ones who treat AI as a ghostwriter. The ones who get sharper treat it as a collaborator — pushing it in specific directions, rejecting outputs, iterating.

2. Build something this weekend. Reading about AI is not learning AI. Leo's students — most with zero coding background — ship working websites and games in a single class. Vibe coding tools like Lovable have collapsed the gap between idea and prototype to minutes. If you're an MBA recruiting into product, strategy, or consulting, the ability to prototype your own thinking is now a baseline skill, not a bonus.

3. The new jobs are at the human-AI seam. Automation creates a new category of work: deciding where humans belong in the loop, designing the workflows, catching the 5% of edge cases that have outsized consequences. Moderator, orchestrator, AI workflow consultant — these roles barely existed two years ago. Position yourself there. Learn more about Leo at https://leobix.com, or on LinkedIn [https://www.linkedin.com/in/leonard-boussioux/].

AI Ready: Prof. Léonard Boussioux on Why You Don't Have to Specialize Anymore

Nathan Fitzgerald didn't come up through tech. He spent years as a lobbyist, moved into marketing, got laid off in 2024, and treated that moment as a forcing function: how do I build a skill set that doesn't become obsolete?

That question led him to Foster's MSIS program — and to a clear-eyed view of what AI can and can't do.

In this conversation, Nathan talks about what it actually looks like to learn AI tools from scratch when you're mid-career. We discuss the concept of cognitive offloading — the risk that you let AI do the thinking for you and end up unable to defend your own work. He talks about using PRDs as a prompting strategy, managing AI like a distributed workforce, and how he built a scrollytelling website for a job interview that he couldn't have made any other way.

Nathan's perspective is useful because he's not a tech native. He's someone who had to figure out where he brings value when the tools are doing more and more of the work — and he has concrete answers.

Key Takeaways

Cognitive offloading is a real risk. If AI writes the paper, you can't defend the paper. Nathan's rule: learn independently, then bring that knowledge to the tools.

Treat AI like a workforce, not a single tool. Break projects into tasks, write a PRD before you start prompting, and think of yourself as the manager. The pre-work is what keeps the output on track.

Portfolio over résumé. You can now show your thinking, not just describe it. Nathan built a full website to demonstrate his communications framework for a single job interview. That raises the bar for what "prepared" means.

AI ready means today, not ever. When asked if Foster made him AI ready, Nathan's answer: "I am — for today." Not a destination. A posture.

About Nathan Fitzgerald

Nathan Fitzgerald is a graduate student in the UW Foster School of Business MSIS program. Before Foster, he worked in government affairs and marketing, most recently before a 2024 layoff that prompted his return to graduate school.

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Conversations on Careers and Professional Life is hosted by Gregory Heller and produced at the UW Foster School of Business.

AI Ready: Nathan Fitzgerald

Show Notes — Anshula Singh | AI Ready on Day One

Series: AI Ready on Day One Show: Conversations on Careers and Professional Life

About This Episode

Anshula Singh came into Foster's MBA program with five years of software engineering experience — building products at Salesforce and ServiceNow, working with machine learning, helping train early LLMs from the inside. She wasn't new to AI. She was already watching it closely.

In her second winter quarter, she took Software Entrepreneurship — a course where students pitch ideas on day two, form teams, and spend ten weeks building a company. Anshula's team built Authscript, an AI platform to automate prior authorization forms in healthcare. They got far enough to pitch in front of VCs.

Then new federal legislation made their product obsolete almost overnight.

In this conversation, Anshula talks about what it takes to build an AI startup under time pressure, what the experience taught her about when to use AI and when not to, and her advice for business students figuring out where they fit in a landscape that keeps shifting.

Key Takeaways

Know when to kill the idea. Find your idea killer early — before you're burning capital defending a thesis that no longer holds.

Customer discovery still beats AI research. The most valuable insights came from talking to people. AI supported the market sizing; the real signal came from humans in the room.

Make AI work with how you already work. Don't reinvent yourself for the tool. Figure out where AI makes you more productive in your existing workflow, and start there.

Keep your own voice. As models improve, your distinct perspective becomes more important, not less.

AI is ambient infrastructure. The question isn't whether it changes your work — it will. The question is how you position yourself within that change.

About Anshula Singh

Anshula Singh is an MBA candidate at the UW Foster School of Business. Prior to Foster, she spent five years as a software engineer at Salesforce and ServiceNow, where she worked on machine learning applications and contributed to early large language model development.

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Conversations on Careers and Professional Life is hosted by Gregory Heller and produced at the UW Foster School of Business.

AI Ready with Anshula Singh

Two questions are on every student's mind right now: How will AI affect the job I'm trying to get? And how do I show up actually ready to use it?

AI Ready — a miniseries from Conversations on Careers and Professional Life -- will feature students from Foster's MBA and graduate programs talking honestly about how they're learning to work with AI. In the classroom. In recruiting. In the work they're already doing.

Foster launched an AI strategy this year built around a straightforward premise: graduates should leave ready to use AI as a real professional tool. That means a required AI bootcamp, a hackathon, and courses designed around practical AI applications in business.

I wanted to hear from the students themselves — what's actually landing, what surprised them, and what they think employers need to understand about this generation of graduates.

Coming This Season

Conversations with Foster students and faculty on:

 * Building real AI products with no prior technical background
 * Using AI in startup development and pitch competitions
 * What cognitive offloading actually costs you — and how to avoid it
 * Keeping humans in the loop when the tools keep getting better
 * What "AI ready" looks like in practice, not just on paper

Conversations On Careers: AI Ready - Miniseries Teaser

Ted Jordan spent 24 years at Microsoft as a global program and account manager before becoming a consultant, professional speaker, and guest lecturer at UW Foster School of Business. His talk — From Outsider to Ally — reframes how we think about networking: less about tactics, more about making the other person feel seen.

In this conversation, Ted shares the specific approaches he used to build relationships inside one of the world's most complex organizations — and what he teaches MBA students about doing the same.

What we cover

 * The subject line that gets a response every time
 * Why curiosity is more valuable than a great question list
 * How to turn a single conversation into an ongoing relationship
 * The unsolicited message that makes you unforgettable to a hiring manager
 * What new interns and first-year employees consistently get wrong
 * Why your network becomes your sales force when you're in transition
 * How Ted got his job at Microsoft — from a lunch invite and a three-word email

Key takeaways

 1. Signal that you see someone as an expert — before you ask for anything
 2. Follow the thread, not your script
 3. Close the loop: act on advice, then report back
 4. Your immediate colleagues are your most underused network
 5. Depth beats breadth — stay long enough to become indispensable

Connect with Ted LinkedIn — Ted Jordan [https://www.linkedin.com/in/ted-jordanseattle] or visit TedJordanTalks.com [https://tedjordantalks.com] to learn more about Ted.

Conversations on Careers and Professional Life is hosted by Gregory Heller, career coach and communication instructor at UW Foster School of Business.

Ted Jordan From Outsider To Ally - Cultivating A Network to Advance Your Career

Layoffs are back in the headlines. Job postings are down. Hiring cycles are longer.

What does that mean for MBA students and other professionals navigating today's market?

In this episode, I speak with Steve Jaffe, author of The Layoff Journey: From Dismissal to Discovery. Drawing on his experience of four layoffs across a 25-year marketing career, Steve maps job loss—and prolonged job search—to the seven stages of grief.

This conversation is both practical and deeply human.

You'll learn:

 * Why career disruptions are emotional, not just logistical — and how naming the grief process reduces shame.

 * How to separate your identity from your job title before you're forced to.

 * Why networking before you need it is the ultimate resilience strategy in a market shaped by AI screening and hidden job opportunities.

 * How hope and small daily wins compound over time, especially during a long job search.

Whether you've been laid off, are facing a challenging search, or simply want to future-proof your career, this episode offers a grounded framework for building resilience that lasts.

Learn more about Steve and his book at TheSteveJaffe.com [https://thestevejaffe.com]. Check out previous episodes on Resilience and Stress with Polo DeCano [https://conversationsoncareers.com/2018/11/discussing-gratitude-resilience-and-stress-with-polo-decano/], Self-Compassion with Jane Compson [https://conversationsoncareers.com/2018/11/mindfulness-self-compassion-with-jane-compson-phd/], Gratitude with Ryan Fehr, [https://conversationsoncareers.com/2020/01/prof-ryan-fehr-on-gratitude-and-appreciation/] Self-Compassion with Andy Hafenbrack [https://conversationsoncareers.com/2020/01/prof-andrew-hafenbrack-on-mindfulness-stress-self-compassion/], and plenty of episodes that talk about networking [https://conversationsoncareers.com/?s=networking].

The Layoff Journey: From Dismissal to Discovery, with Steve Jaffe

In this episode I break down the difference between slide decks and slide docs—and talk about designing intentionally for each.

Many presentation problems don't stem from weak ideas or poor analysis. They come from using the wrong artifact for the job. Slides overloaded with text are often treated as presentations when they're really documents meant to be read. The result? Confused audiences, long meetings, and diluted messages.

I explain why slide decks and slide docs serve fundamentally different purposes—and why trying to make one file do both almost always fails.

In this episode, you'll learn:

 * Why slide decks are spoken artifacts designed to support your voice, not replace it

 * How slide docs function as read artifacts that must stand on their own without explanation

 * A simple rule of thumb to help you choose the right format before you start building

Whether you're preparing for class presentations, team meetings, interviews, or client-facing work, understanding this distinction will immediately improve your clarity and credibility.

Slide Decks vs. Slide Docs: Why So Many Presentations Miss the Mark

Data doesn't persuade. Insight does.

In this episode, I break down what effective data visualization really means—and why most charts fail to do their job. This isn't about making slides look prettier. It's about helping your audience think clearly, decide faster, and trust your analysis.

Drawing on lessons from Edward Tufte's work and Good Charts by Scott Berinato, Gregory explains how to move from cluttered, confusing visuals to charts that make the point unmistakable.

You'll learn:

 * Why every chart should answer one clear question—and how to define it before you design

 * How to match chart types to intent, so your audience doesn't have to work to understand your message

 * What it really means to simplify ruthlessly, including what to remove, mute, or highlight

 * Why message-driven titles dramatically improve comprehension

 * How context, annotation, and design for the room turn data into evidence

Whether you're preparing for class presentations, internship updates, or executive decks, this episode will help you stop using charts as decoration—and start using them as tools for influence.

Make Your Insights Obvious With Effective Data Visualization

Conversations on Careers and Professional Life, from the Foster School of Business office of MBA Career Management Host Gregory Heller has conversations with University of Washington and Foster School faculty, staff, alumni, executives, current MBA candidates and other experts relating to career development, planning, and resilience. If you're navigating a career change, pursuing your MBA, or looking to develop a resilient mindset to help you with your job search, this podcast may be for you!

Gregory Heller

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