The Use of Use Cases – 6R Framework

Ever since the launch of ChatGPT for the public in November 2022, interest in Generative AI has surpassed all previous tech hype cycles. A look at Google search trends reveals that among the most searched contexts with respect to Generative AI are the “use cases.” This week, I decided to reflect on the “use of use cases,” the challenge of quantity vs. quality, and the balance of futuristic vs. realistic. With a typical consulting mindset, I wanted to draft a simple yet effective framework to calibrate the “use of use cases.” I am referring to this framework as the 6R Framework: Relevance, Realistic, Risk, Reward, Retreat, and Responsible. Let's delve into how this framework can be applied to the use cases of GenAI applications and help us navigate the dilemma of prioritizing the use cases.

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Relevance: Generative AI holds immense potential across various industries and domains, from content creation and marketing to healthcare and finance. The use cases for Generative AI have a multiplier effect across these domains. Take, for example, a use case on content summarization can have different applications in the same industry across different functions and even within a given function, the application can vary for multiple processes/tasks. Identifying the relevance of a Generative AI use case at a task level is the first step. For instance, while content summarization might be relevant for a customer service agent to handle queries related to a product inquiry, the same might not be relevant in a healthcare setting wherein the criticality of the decision might require more details.

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Realistic: Setting realistic expectations is essential when integrating Generative AI use cases into business processes. While the technology has made significant strides, it's crucial to understand its current capabilities and limitations. Realism entails assessing factors such as data availability, computational resources, and the level of expertise required. For context, recently we were trying to create a chatbot that could surface technical information from PDF files using RAG and help to answer questions related to technical queries integrated with LLM. After spending many hours of effort and experimentation on this use case, the results of the chatbot were not at par with good old “keyword” based search overlaid with a simple visualization tool for this particular scenario.

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Risk: Like any transformative technology, Generative AI carries inherent risks, including ethical concerns, biases in generated content, and potential misuse. It's imperative to proactively identify and mitigate these risks through robust governance frameworks and ethical guidelines. Collaborating with diverse stakeholders, including GenAI auditors, bias/ethics testers, policymakers, and end-users, can help anticipate and address potential risks effectively.

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Reward: The potential rewards of leveraging Generative AI are vast, ranging from increased efficiency and innovation to enhanced customer experiences and competitive advantage. By identifying key performance indicators (KPIs) aligned with business objectives, organizations can measure the impact of Generative AI initiatives effectively. It is important to identify the tangible and intangible benefits of the applied use case at the start; develop a business case for its deployment and track the value delivered vs. the costs to implement the use case. The true rewards might not always be quantifiable; however, a good thumb rule is to at least break even on the fixed costs to implement the use case.

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Retreat: Despite the promise of Generative AI, there may be scenarios where a strategic retreat is warranted. This could involve reevaluating use cases that are not yielding the desired outcomes or pausing initiatives that pose significant risks or ethical concerns. A proactive retreat strategy allows organizations to course-correct and reallocate resources effectively, ensuring long-term sustainability and responsible deployment of Generative AI. This is the most important attribute of the framework, which means the ability to decommission a use case and release resources to focus on other priorities. A use case should only be pursued if it can be called off without a loss of significant resources and repute provided things do not go as planned.

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Responsible: Responsibility lies at the heart of ethical AI deployment. Organizations must prioritize responsible practices throughout the lifecycle of Generative AI projects, from data collection and model training to deployment and monitoring. This entails promoting transparency, fairness, and accountability in decision-making processes, while also fostering a culture of ethical awareness and continuous learning.

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In conclusion, the 6R Framework offers a structured approach to harnessing the power of Generative AI while mitigating risks and maximizing rewards. By focusing on relevance, realism, risk, reward, retreat, and responsibility, organizations can unlock new possibilities and drive positive impact across diverse domains. As we navigate the evolving landscape of AI, let us embrace these principles to ensure that human efforts are spent wisely on the execution and implementation of the most beneficial use cases rather than contemplating on the debate of the “use of use cases."

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