AI Workshop Microsite
A practical playbook for DePaul student founders: where AI creates real value, where risk lives, and how to run safe, fast experiments that support your social-impact mission.
The goal of this session is to help founders move from AI curiosity to repeatable execution.
This workshop reframes AI from "magic" to workflow. Instead of chasing every new model, you identify the highest-friction parts of your startup and use AI to speed those tasks up: drafting, synthesis, analysis, prototyping, and communication. The focus is practical adoption, not hype. If a tool cannot save you time this week, it does not make the cut.
For social-impact ventures, the biggest opportunities are often simple and immediate: faster grant drafts, clearer stakeholder updates, more consistent customer discovery notes, and quicker turnaround on program materials. You also saw how agent-style tools can help organize files, generate structured outputs, and support small teams that need leverage without adding headcount.
The session also addresses failure modes that matter for founders. Generative AI can hallucinate facts, lose key context in long threads, and confidently return incorrect claims. We reviewed why human review remains mandatory, especially for anything legal, financial, medical, or brand-critical. You cannot outsource judgment to a model and still protect trust with funders, partners, and community members.
Finally, the session anchors execution in a clear framework: prevention, verification, and audit trails. Choose the right tool, ground outputs in trusted sources, verify important claims using multiple references, and label AI-assisted work internally. This gives you a way to experiment confidently while keeping quality, privacy, and accountability intact as your startup grows.
Use these as low-risk starting points you can test in your current sprint.
Use AI to turn rough notes into first-pass grant narratives, donor updates, and executive summaries. You keep ownership of strategy and facts, while the model helps with structure, clarity, and tone. This cuts blank-page time and improves consistency across outreach materials.
After interviews, AI can cluster pain points, extract recurring language, and surface objections you are missing. That helps you prioritize product and messaging decisions using actual user evidence rather than assumptions. The value is speed plus pattern recognition.
Founders often have impact data but struggle to communicate it clearly. AI can convert raw metrics into plain-language impact summaries for partners, advisors, and community stakeholders. You keep credibility by verifying every number before publishing.
AI is useful for drafting onboarding checklists, volunteer scripts, and process documentation that small teams usually postpone. With a founder review pass, you can standardize daily operations faster and reduce handoff confusion as your team scales.
Use web-grounded AI research to map likely partners, funders, and policy signals relevant to your mission. This is especially useful when you need quick situational awareness before meetings or pitches. Verify key facts with multiple trusted sources.
A starter stack by function, with responsible-use guardrails built in.
Use tools like ChatGPT, Claude, or Gemini for first drafts, tone adjustments, and audience-specific rewrites.
Use NotebookLM and web-grounded model modes when accuracy matters and sources are required.
Use OpenAI Codex or Claude agent workflows to generate small web prototypes, automate repetitive edits, and accelerate iteration.
Use AI in tools your team already knows (Microsoft Copilot, Notion AI, Slack AI) for summaries, action items, and documentation.
Keep momentum by committing to one small implementation cycle this week.