Work and Build with AI
Course proposal for FABIZ ASE
Daniel Oproiu
A practical, hands-on course for students who want to understand how work, business and company-building are changing because of AI.
The course is useful whether they become employees, managers, consultants, marketers, analysts, operators or founders.
This is not a course about "using ChatGPT better".
It is about learning how to use AI to research better, build faster, make better decisions and understand how modern companies will actually operate.
The goal: students leave with practical AI foundations they can use in a company, in a startup or in their own projects.
Why this course matters now
AI changes what people can afford to do.
Until recently, many business tasks were slow, expensive or dependent on specialist teams.
Research, design, copywriting, development, analytics, support setup, reporting, automation and launch planning all needed time, budget and people.
That meant many ideas were never tested properly.
This course teaches students how AI lowers that barrier.
- Research a market without hiring a research agency
- Build a first landing page or prototype without a full dev team
- Use Claude Code or Codex to create MVPs and internal tools faster
- Create and test marketing ideas with a smaller budget
- Use AI agents for repeatable research, reporting and admin work
- Set up basic customer support with chatbots and knowledge bases
- Estimate tool costs, API usage, hosting and monthly running costs
- Use local or open-source models when privacy or control matters
AI changes what one person, a small team or a junior employee can contribute.
What students will learn to do
Students will not only discuss AI.
They will use it to complete real business work.
By the end of the course, students should be able to:
- Research a market or business problem
- Analyze competitors and customer needs
- Turn messy information into clear decisions
- Shape an offer, campaign, process or product idea
- Build a landing page, prototype or internal tool
- Create basic marketing and sales materials
- Set up an AI-assisted support or operations flow
- Automate simple repeatable work
- Estimate tool costs and monthly running costs
- Think clearly about privacy, security and human review
The course is about practical AI literacy for the new business landscape.
What we will cover
01 — What changed
How AI changes the way people work, build, test, analyze and operate.
02 — Finding problems worth solving
Trends, customer complaints, reviews, unmet needs and business opportunities.
03 — Research without the busywork
Competitors, pricing, customer segments, demand signals and market mapping.
04 — Turning research into decisions
Customer, problem, offer, positioning, value proposition and go-to-market logic.
05 — Building the first version
Landing pages, MVPs, no-code tools, Claude Code, Codex and agentic coding.
06 — Marketing and sales work
Ads, emails, social content, landing page copy, outreach and sales scripts.
07 — Support and operations
Chatbots, audio transcription, call summaries, helpdesk flows, SOPs and reporting.
08 — Tools, costs and launch setup
Domains, hosting, APIs, analytics, email, deployment, security and monthly operating cost.
09 — Using AI responsibly
Privacy, hallucinations, copyright, bias, human review, local models and open-source models.
A course about doing useful work with AI, not talking about AI as a buzzword.
The practical stack matters.
Students should not leave with only a nice presentation.
They should understand what it takes to put something online, use AI safely, keep it running and know what it costs.
Web presence
Domain, landing page, email address, basic SEO, analytics.
AI tools
ChatGPT, Claude, Mistral, APIs, coding agents, transcription tools, local models.
Deployment
GitHub, Vercel, databases, hosted apps, no-code tools.
Customer support
Chatbot, helpdesk, knowledge base, ticket summaries, call transcripts.
Security
Passwords, access control, API keys, privacy, what not to upload into AI tools.
Cost
AI subscriptions, API usage, hosting, domains, tools and support software.
Students should learn to ask:
"What would this cost to launch, run and maintain?"
Built around doing the work.
At least 2 hours per week.
12-16 weeks.
70% hands-on.
30% explanation and discussion.
Each module produces one useful business asset.
Students can work individually or in teams.
They will use AI for:
- Research and analysis
- Landing pages and prototypes
- Coding assistance
- Marketing and sales
- Audio transcription and summaries
- Customer support
- Reporting and automation
- Cost planning
- Risk and quality checks
Tool access
To make the course practical, we should agree on student access to AI tools before the course starts.
I can reach out to OpenAI, Anthropic and Mistral to explore student discounts, education credits or temporary course access.
Possible options:
- Student subscriptions
- Faculty-managed workspace access
- Limited API credits
- Open-source or local model alternatives
- Clear rules on what data can and cannot be used
The course should not depend on whatever free tools happen to be available that week.
Final project
Students pitch something they could actually test.
The course ends with a practical pitch, not a traditional exam.
Students can choose one of two directions:
Option 1 — New business idea
A business, product or service they could realistically test with a small team and AI-enabled workflows.
Option 2 — Business improvement idea
An AI-assisted workflow, internal tool, support system, marketing process or operations improvement for an existing company.
Each team presents the problem, target user, solution, research, prototype or workflow, adoption plan, tool stack, costs, security and 30-60-90 day plan.
A practical blueprint for using AI in real business work.