Identifying a Problem Statement

Understanding how to identify a problem statement using AI

In order to make any informed decision, you should have a clear understanding of the intended outcome.

Clarifying Intended Outcome through Differentiators

To identify differentiators, think not what you want to build, but what you want to solve. In developing new products or features to an existing product, focus should be put on how your product differentiates from existing providers (or lack there of). If existing providers exist, you should reevaluate whether the effort to build those features yourself is worth more than just buying it.

For example, in our bicycle rental application, how we accept payments in itself is not a key differentiator. All rental companies will need to take in payments. However a customer's payment experience and the processes that power that experience such as single sign on, integrated mobile pay, refund process, or scan a card could be differentiators.

To begin documenting, start with a brief description of the problem or challenge. Elaborate on pain points for users, a gap in the market, or an opportunity to improve an existing process. When thinking about these problems, think in terms in of qualitative or quantitative results. Ask yourself how do you measure whether or not your solution is solving the problem?

To expedite this process we have created a template as a start to capture the key points about this. You can view the template here. Finding balance between execution and research is a fine line and something to be mindful of. It is easy to end up in an endless cycle of analysis This can result in waiting too long to enter the market or ultimately not spending the development time needed to deliver a project at all. On the other hand, jumping into a project too quickly without the right level of research ends can result in products that already exist or do not solve a problem that really exists, often at a financial or time cost to the creator.

Everyone can have an idea, what and how you execute is what drives a successful delivery. Be prepared that it is a process that you are not likely to be successful on your first attempt. Keep going knowing that the journey itself is valuable. Whenever I struggle with this process I remember a lyric from “Always Wear Sunscreen”: “Whatever you do, don't congratulate yourself too much, or berate yourself either. Your choices are half chance. So are everybody else's.”

Let’s go through some prompts on how we can use AI to help build our bicycle rental application:

• How many Americans own bikes?

• How often do American ride bikes?

• What are some problems with bike ownership?

• How many bike rental business are there?

• What are some problems with bike ownership?

• How many bike rental businesses are there?

• What is the growth projection for bike rental businesses?

• What is a radical new idea for bike rentals?

• List out ideas for a bike rental business.

• What are some pain points for a bike rental business?

Some key considerations in responses during ideation, is when the data was last up to date. Markets, customer demands, and technologies change daily and a downside to large language models is they are often created "at a time". Meaning their data has a fixed end date and it cannot provide realtime information. To identify when a large language model was last updated, you could ask it or look at it's configuration.