Defining a Use Case
The first stage in the generative AI application lifecycle is defining a use case. This phase is the foundation that sets the path for the entire project by doing the following:
- Defining the problem to be solved
- Gathering relevant requirements
- Aligning stakeholder expectations
Getting this stage right is imperative, because it informs all subsequent steps and ultimately determines the success or failure of the generative AI application. During this crucial phase, teams must carefully analyze the problem space, consult with subject matter experts, and translate business needs into technical specifications that can guide the development process.
Knowing which information to include in your business use case is important to identify early on.
Business use cases
A business use case is a structured narrative that describes how a system or process should behave from the perspective of an actor or stakeholder. It helps to communicate the functional requirements of a system or process.
Parts of a use case
A well-defined business use case typically consists of the following parts:
Brief description
A high-level summary of the use case's purpose and objective
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Addressing business use cases with generative AI
When it comes to resolving business problems using generative AI, there are various metrics and approaches that can be employed.
Key metrics:
Cost savings
One of the primary metrics is the potential cost savings that can be achieved by using generative AI. This includes reductions in labor costs, process optimization, and efficiency gains.
Time savings
Generative AI can automate and streamline various tasks, leading to significant time savings. Measuring the reduction in time required for specific processes or activities can be a valuable metric.
Quality improvement
Generative AI can enhance the quality of outputs, such as written content, creative designs, or analytical insights. Metrics like accuracy, coherence, and creativity can be used to measure quality improvements.
Customer satisfaction
If generative AI is used to improve customer interactions or experiences, metrics like customer satisfaction scores, net promoter score (NPS), or sentiment analysis can be valuable indicators.
Productivity gains
Generative AI can augment human capabilities, leading to increased productivity. Metrics like output volume, error rates, or task completion times can measure productivity improvements.
Approaches to addressing business problems with Generative AI:
Process automation
Generative AI can be used to automate repetitive or time-consuming tasks, such as content generation, data analysis, or customer service interactions. This approach can lead to significant efficiency gains and cost savings.
Augmented decision-making
Generative AI can be used to enhance decision-making processes by providing insights, recommendations, and decision support. By analyzing large and complex datasets, generative AI models can uncover patterns, trends, and actionable insights that can inform and improve business decisions, ultimately leading to better outcomes.
Personalization and customization
Generative AI can be used to create personalized and customized content, products, or experiences for customers or stakeholders. This approach can improve customer satisfaction, engagement, and loyalty.
Creative content generation
Generative AI can be employed to generate creative content, such as written text, images, videos, or audio. This approach can be valuable for marketing, advertising, entertainment, or educational purposes.
Exploratory analysis and innovation
Generative AI can be used to explore new ideas, concepts, or solutions by generating novel combinations or variations. This approach can foster innovation and help businesses stay at the forefront of technology.
It's important to note that the specific metrics and approaches will depend on the business problem at hand, the industry, and the organization's goals and priorities.
Move on to the next lesson to learn more about selecting a foundation model.