Sabiha Afrin

Asst. Director of Marketing and Communications
American University, SPA

Sabiha Afrin leads digital marketing for American University’s School of Public Affairs, where she built a small but mighty team from the ground up. She oversees SEO/AEO, paid media, Salesforce Marketing, email, and AI workflows across the school’s degree programs and policy centers.

She started her career on the agency side and now directs agencies from the other chair. After seven years in education and content marketing, she knows what works, what does not, and what can be handed off to technology so her team has more time to focus on new ideas.

Marketing gets people to notice you. Branding gives them a reason to remember you. Sabiha's work sits somewhere between the two. She is interested in how technology can support both, and she builds her own agents and automations without an engineering team or a dedicated budget.

This year, she has led sessions across the AU community on using AI in marketing and personal branding for graduate students, faculty, and staff. She also founded Branding Inward, a free AI brand strategy suite for people who find most branding advice too loud. She has spoken about micro-influencer programs and social strategy at higher education and marketing conferences and has published her research in a peer reviewed journal.

Sabiha Afrin’s Session

12:45 p.m.–1:30 p.m. PT — Wednesday, November 18, 2026

Marketer Turned Builder: How to Ship an AI Project Manager That Unblocks a Lean Team

Lean marketing teams are used to doing more with less, but the thing quietly capping their output usually isn't effort or capacity. It's the bottlenecks, the handoffs, approvals, and review loops where a deliverable stops moving and sits waiting on someone. Every stall compounds, and on a small team there's no slack to absorb it. 

In this session, Sabiha shows how she built an AI project manager agent in Python using the Claude Agent SDK – with no engineering team and no budget line – and the results that followed. Deliverable turnaround dropped from about three weeks to one, and review cycles shrank sharply. The team didn't get smaller. It got room to take on the work it had been turning down. This is a practical, build-it-yourself blueprint for spotting the bottleneck that's actually slowing your team, drawing the line between what an agent should own and what stays human, and understanding what a first working agent actually takes in hours and skill.


Key Takeaways:

  • Identify the bottleneck that's actually slowing your lean team, by auditing where work waits instead of where your people work.
  • Draw the line between what an agent should handle and what stays human, using your review loop as the test.
  • Build your first working AI agent using the Claude Agent SDK (or equivalent), following the actual steps involved and a realistic view of what the effort takes in hours and skill.
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