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Generative AI has the potential to revolutionize industries by automating complex tasks, enhancing creativity, and driving innovation. However, identifying the right use cases requires a structured approach. Here are in my experience three essential steps to guide you in this process.
1. Start with the problem, not the solution
It is tempting to try out new, breakthrough technologies like generative AI and focus on proving technical functionalities as a goal. The pitfall is that this approach can lead to solutions in search of a problem, where the technology drives the initiative rather than addressing real customer needs or business opportunities. This can result in wasted resources on projects lacking clear value or relevance to the organization. Instead of starting with the technology, begin by asking yourself what specific challenges your customers, employees, or organization as a whole face or what opportunities exist for improving your services or workflows. By working backward from these problems, you can determine how generative AI might provide innovative solutions. This approach ensures that the generative AI solution directly addresses a real business need and is worth the effort and investment.
An example to illustrate this process: a customer is looking to book her summer vacation via a travel booking website, a task that typically takes hours of searching for destinations, hotels, flights, and activities. This process can be overwhelming and time-consuming, often leading to frustration and decision fatigue.
A use case could be leveraging generative AI to make that experience much more pleasant and engaging. For example, generative AI could analyze her past travel history, reviews, and stated preferences to curate a personalized travel itinerary. It could recommend destinations, accommodations, and activities tailored to her interests. Additionally, a generative AI-enabled application trained on numerous travelers’ reviews and stories could enrich her itinerary with visuals and narratives, to help her envision and get excited about the trip. This personalization use case not only saves time but also enhances the overall travel planning experience, making it more enjoyable for the customer and increases their engagement and loyalty with the website.
If you explore the problem correctly, you may also discover that not all problems are best solved with generative AI, sometimes traditional AI/ML or even predictive analytics could be the solution. For more inspiration on the opportunities, you could check out AWS AI Use Case Explorer for hundreds of real life examples across many industries and organizations. 2. Prioritize the use cases by value to customers and the organization
Once you've identified potential use cases, the next step is to prioritize them based on the quantifiable value they bring to both customers and the organization. This should include both tangible business outcomes like revenue growth, productivity gains, and error reduction, as well as leading indicators like Net Promoter Score, Customer Feedback Score, or Adoption Rates.
Then you should establish a scoring system to rank these use cases, focusing on those that offer the highest value and align with your organization’s strategy. This prioritization helps in allocating resources effectively and ensures that the most impactful projects are tackled first.
3. Gatekeeping the use cases with feasibility and ROI assessment The third step is to conduct a feasibility assessment to determine the technical and operational viability of each use case. This involves evaluating the availability and accessibility of data, the complexity of the AI models and customisation required, and the readiness of your infrastructure. Collaborate with technical experts to understand the potential challenges and limitations. Start a small-scale experiment to evaluate the feasibility and provide rough estimates on costs, effort, resources and timing of full implementation.
By filtering out impractical and negative ROI use cases, you can focus on those that are both valuable and achievable, thereby maximizing the chances of success.
By focusing on these three steps – identifying real customer or business problems, prioritizing use cases based on their value, and conducting feasibility and ROI assessments – you can ensure that your AI use cases are aligned with your organization’s strategic goals, making them both practical and impactful before implementation.
Remember that successful implementation of generative AI is an iterative process. As you gain experience and insights from your initial projects, you'll be better equipped to tackle more complex challenges and uncover new opportunities. Stay agile and be prepared to adapt your approach as the technology and your understanding evolve.
Generative AI's potential to transform industries and drive innovation is immense, but realizing this potential requires a strategic approach. Don't let this opportunity slip away – start exploring how generative AI can address your pressing business challenges today and position your organization at the forefront of this technological revolution.
Learn more about AWS generative AI