3 hours
AI Business: From Idea to Impact
Explore how to leverage AI for business innovation, from identifying opportunities to implementing AI solutions.
Learning Objectives
- Understand AI capabilities and limitations
- Identify AI opportunities in business
- Design AI-powered products and services
- Navigate ethical and practical considerations
Topics Covered
- AI Fundamentals for Business
- Identifying AI Opportunities
- AI Product Design
- Implementation Strategies
- Ethics & Responsible AI
- Future of AI in Business
Hands-On Activities
AI Opportunity Assessment
30 mins
Identify and evaluate AI opportunities in your organisation using a structured assessment framework.
Instructions
- List 10 repetitive, data-heavy processes in your organisation
- Score each on: data availability, impact potential, feasibility
- Identify the top 3 AI opportunities
- For each: define the input data, desired output, and success metric
- Create a 1-page AI opportunity brief for your top candidate
Responsible AI Checklist
20 mins
Evaluate an AI solution against ethical and responsible AI principles.
Instructions
- Review the AI solution case study provided
- Score against 6 responsible AI dimensions: fairness, transparency, privacy, safety, accountability, inclusiveness
- Identify potential bias risks
- Propose mitigation strategies for each risk
- Create a Responsible AI statement for the solution
Exercises
group · 30 min
AI Use Case Design Sprint
Design an AI-powered solution for a real business problem in a rapid sprint format.
- Review the assigned business challenge
- Identify where AI can add value (automation, prediction, personalisation, generation)
- Sketch the user experience with AI
- Define the data requirements and sources
- Present your AI solution concept (3 min pitch)
individual · 20 min
Prompt Engineering Workshop
Practice writing effective prompts for large language models to solve business problems.
- Start with a business task (summarise report, draft email, analyse data)
- Write your first prompt — observe the output
- Apply prompt engineering techniques: context, examples, constraints
- Iterate 3 times, improving output quality each time
- Document your best prompt patterns
Practical Examples
Consumer Goods
Unilever AI Recruitment
Unilever implemented AI in their graduate recruitment process, using AI-powered video interviews and gamified assessments to screen 250,000+ applicants.
OUTCOME: Reduced time-to-hire by 75%, increased diversity of hires by 16%, and saved £1M+ annually while improving candidate experience.
Energy
Octopus Energy AI Customer Service
Octopus Energy deployed AI to handle customer service emails, training models on their best human agent responses.
OUTCOME: AI now handles 44% of customer emails, with higher satisfaction scores than human agents. The AI generates responses with more empathy and accuracy.
Industry Success Stories
Financial Services
JPMorgan Chase
Challenge
Lawyers spent 360,000 hours annually reviewing commercial loan agreements for errors and risks.
Approach
Developed COIN (Contract Intelligence), an AI system trained on 12,000+ historical agreements to review and extract key terms automatically.
Result
Reduced review time from 360,000 hours to seconds, with higher accuracy than human reviewers. Saved millions in operational costs.
Useful Resources
Prediction Machines — Agrawal, Gans, Goldfarb
BOOK
Google AI Principles
FRAMEWORK
ChatGPT — OpenAI
TOOL
AI in Business (MIT Sloan)
VIDEO
AI for Everyone — Andrew Ng (Coursera)
ARTICLE
Key Takeaways
AI business acumen
Practical AI application skills
Ethical AI framework
Fostering real-time entrepreneurship
to help accelerate career growth for venture builders, product designers and innovators.
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