This podcast discusses the best practices for scoping AI projects, emphasizing the importance of thinking big, starting small, and iterating often. It covers topics such as aligning AI initiatives with business goals, avoiding haphazard implementation, and navigating challenges in AI projects.
Thinking big and starting small are essential for successful AI projects.
Avoiding scope creep and defining project scope accurately lead to meaningful AI implementation.
Deep dives
Addressing AI Project Scope
Dividing AI projects into smaller addressable parts is crucial for success. Breaking down project deliverables into AI-focused work units ensures a more focused approach. It is essential to define the scope with the narrowest solution to streamline variables and resources. Avoiding scope creep and defining the AI project scope accurately using tools like work breakdown structure are key to project success.
Think Big, Start Small, Iterate Often
Solving significant business problems through AI requires thinking big and starting small. Narrowing down solutions and avoiding grandiose AI projects is crucial. Iterating frequently with outcome-focused iterations and addressing business understanding objectives are vital aspects of successful AI implementation.
Right-Sizing AI Solutions
Ensuring AI projects tackle relevant stakeholder needs and provide real solutions is essential. Avoiding the temptation to implement AI for the sake of AI is crucial. Aligning AI solutions with specific business problems and measurable outcomes leads to meaningful AI implementation.
Managing AI Project Iterations
Keeping AI project iterations short and outcome-focused is key to success. Validating whether each iteration meets business objectives is critical. Understanding the need for early wins and adjusting course based on measurable outcomes ensures effective progression of AI projects.
The best practice for any high risk, emerging technology project with ill-defined goals is: Think Big. Start Small. Iterate Often. But, what does that really mean? How do you think big? And how do you start small? What does iteration look like? And how does this connect to project scope? In this episode of the AI Today podcast we discuss what it means to think big when it comes to AI.