Lesson learned: Which problems are worth solving?
We have met several companies in the past months discussing possible use of AI in their organization, product, or services. Many of them are excited to start their AI journey and take their data – to be able to understand it, to process it and to extract value from it.
Some organizations have just started to look at what AI can do for them, while others have come significantly further - and it is fascinating from the outside to see the different phases the different organizations have come – and the learning one can see from these observations.
We experience that the organizations that have come the furthest has asked them self these questions – and communicate these answers clear to us.
Why do we need to transform?
What do we need to transform?
How can we transform?
This meaning they have defined a clear vision for the company, product or service; they have a plan for what to do and has made an action plan for doing it - using AI.
And AI is all about data and how we use it to gain benefits from it. Earlier we used to say that information is power - but that is no longer the case. It is the analysis of the data, use of the data, analyze, and understand it — that is the ultimate power. AI does not provide value by itself; it enables people to make data-driven decisions.
Therefore: Data storytelling is the process of using narrative-style tactics to bring your data out into the open where it can become easier for decisionmakers to understand and, ultimately, take a course of action that benefits your company. The only way to gain a competitive edge from AI is to derive insights from data and to make sense of it.
So, before you let your passion carry you away starting your AI journey, ask those three questions on behalf of your company before you start.
Next questions will be: Which problems are worth solving?
When you start your brainstorm together with your colleagues, you probably will find a bunch of different use cases where AI could help you improve your organization, product, or service.
One of our clients had both the structure above and below in their presentation to us - and I observed that the AI specialist immediately could extract value from it and start suggesting actions, process, and solutions.
Try to describe and classify those use cases into these categories. Break it down into a small and specific component of the process that you want to improve.
Second question: What problems should we solve first?
Selecting the right case is crucial. Sales, Marketing, Accounting, HR or production or services? What question will give the right insight? Don’t start with an easy one just because you think it is an easy starting point - you need to extract real value out of the project and estimate ROI – the job and the AI journey will be the same anyway. Therefore, you must identify and prioritize ideas to work with where you have historical and available data.
Evaluate the use cases in which it would generate substantial value and contribute to business success. Prioritize the use cases according to which offer the most short- and long-term value, and which might ultimately be integrated into a broader platform to create competitive advantage for your company.
No matter how far you have come, we are ready to help you - every step of the way and – no case to small or too big. Conctact us here
