It’s an increasingly common conversation: How can we solve a customer’s business challenges with AI?
For example, a Google Cloud customer in the financial services industry recently asked us to help them reimagine and redesign their salespeoples’ customer engagement experience, which happens primarily over the phone. Today, that sales process is fraught with unnecessary complexity. Because data is stored across many different sources, salespeople must talk to the customer while simultaneously navigating multiple apps. Solving this challenge would not only help reduce the amount of time needed to close a deal, but also help standardize sales processes, and generate more consistent experiences. Unsurprisingly, AI solutions were top-of-mind when we were exploring potential ways we could help.
In this case, there are AI models that can improve the customer service experience with faster document-processing times, quickly identifying and summarizing the content of documents. However, building products powered by AI requires more than just a model. First, companies need to address both business goals and specific user-centric needs through detailed discovery, requirements, and features that address user pain points and opportunities. However, even the most well-intentioned customers get this wrong. The excitement and eagerness generated by AI make it far too easy to skip a few important steps that ultimately save time and money.
Companies need to first validate if creating an AI-powered product is even the right thing for their business. Building and testing prototypes with users does exactly that — proves out an idea before investing into a fully productionized AI solution. Companies who embrace a prototype-and-test mindset can move quickly, try new things, and become comfortable with failing fast to test if an idea is worth exploring. Rather than just focus on the AI model, this approach expands the impact of the idea and solves a multi-sided problem: It ensures that the needs of the business, what we think of as the money, the user, and the magic, are met; quality AI applications require all three to be successful.
With that in mind, let’s walk through a full-cycle AI journey, starting from business ideation, use-case identification, data requirements, and model training while designing and prototyping that will lead an organization to a successful, user-centric AI product.
Start your journey the right way
The key components of how to design and build an end-to-end AI-driven product from an idea can be summarized as follows:
- Define a user-centered product strategy that dials into your customers’ business needs and technology strategy.
- Understand the user in order to build a product with the right features to solve user needs.
- Plan and execute a design sprint that focuses on Customer User Journey (CUJ), user experience (UX) and design.
- Ideate and define business use cases that can be solved with AI from the design sprint.
- Design and build a clickable software prototypes that shows a visual representation of the user interface and establishes the value of the product to the business.
- Design, iterate and build AI models prototypes demonstrating the use cases, a proven way to answer critical business questions, data-driven design, confirming understanding and exploring data, and AI baseline performance.
- Integrate AI model prototypes with the business use case and UX design as an AI-driven MVP.
