So much has been written about how to use ChatGPT
But it repeats itself.
So let’s look at some non-obvious use cases where you could integrate Generative AI into your business processes.
The use-case I collected here are brainstormed with ChatGPT.
Some are where you would have to train your own GPT or LLM, others are where you would be able to use ChatGPT right out of the box and leverage existing models directly.
Because I really wanted it to come up with something unique, but I let you be the judge of that.
My idea was generating personalized sales scripts by using general templates and customer preferences data from a CRM.
By leveraging ChatGPT and other generative AI tools in these ways you can create more value with less effort, personalize customer experiences, and stay ahead in this innovation leap we are experiencing right now.
Evaluating your ChatGPT Ideas
Let’s start with defining how we should evaluate non-obviousness of ChatGPT applications.
It must come from a position of how to leverage this very specific Generative AI, like “ChatGPT”, for a very clear universe, like “in business.”
Then aim for highlighting innovative, non-obvious applications that can create significant value.
To refine and improve your ideas, consider the following suggestions.
1. Focus on Specific Industry Needs
Tailor each application more closely to specific industries' unique challenges and opportunities. By providing concrete examples that address particular industry pain points, you make the use cases more relatable and compelling.
Example: For an automated content creation tool, highlight how it can solve the content volume challenge in the digital marketing industry by generating SEO-friendly articles, thus driving traffic and engagement.
2. Highlight Integration with Existing Systems
Detail how these AI applications can integrate with existing business systems or workflows. Offering guidance on integration can help businesses visualize the implementation process and understand the infrastructure adjustments needed.
Example: For personalized learning and development, explain how an AI-driven system can work with existing LMS (Learning Management Systems) to provide custom content and track progress.
3. Include Metrics for Success
For each application, suggest metrics or KPIs (Key Performance Indicators) that you would use to measure the success of implementing Generative AI in your universe. This adds a layer of practicality, helping evaluate ideas according to goals and assess ROI.
Because you want to be creative around how to climb that mountain, while the choice of mountain is not up for debate.
Example: For enhanced data analysis and visualization, metrics could include time saved in report generation, increase in data-driven decisions, or improvements in customer satisfaction scores.
4. Consider Scalability
Discuss how these AI applications can scale with a business as it grows. Scalability is a critical factor for businesses looking to invest in new technologies.
Example: For interactive customer support, explain how the system can handle increasing volumes of queries through scalable cloud infrastructure and AI model training processes.
5. User Adoption Strategy
Propose strategies for encouraging user adoption and engagement with the new AI features. This could involve user training, incentives, or gamification elements.
Example: For innovative product development ideas, suggest setting up a feedback loop where users can suggest features and vote on the ones they find most valuable, thus engaging them in the development process.
By incorporating these improvements, you can make your ideas more robust, actionable, and aligned with the needs and concerns of potential users.
This will enhance the appeal of your proposals and also increase the practical value to your businesses if you are looking to innovate with AI.
The Obvious Non-Obvious Use-cases
Since Rumsfeld we know of the 2-by-2 evaluation framework of Knowns and Un-knowns
| Known Knowns | Un-known Knowns ||
