Its critical to do business and technical assessment of the AI solution before(not after) starting the implementation
Imagine that you are either
- The Data Scientist, who is going to implement the solution, will require technical feasibility and complexity assessment like is the right data available? how inference will be integrated with the business process? and is the acceptance criteria achievable with the given data set and constraints? etc. Or
- Business Manager sponsoring the project and want to ensure that the investment you are going to make will generate real value.
Lets take an example of a machine learning based forecasting solution. Forecasting is the science of predicting the future. Accurate forecasting is relevant in a broad set of business scenarios, such as:
- Product demand
- Cash flow
- Inventory planning
- Staffing at call centers
- The selling price of crops
- Deliveries per zip code etc.
A sample of business challenges specific to transportation industry need, that accurate forecasting can solve:
- How many on-demand rental bikes are needed at a time around a 3 block radius?
- How many on the ground operation specialists are needed to transfer bikes from one location to the other?
- How many trucks are needed daily to deliver goods on time to a warehouse in a specific
Lets say your company want to develop a solution for any of the above business challenge, what could be the key questions that will help you measure the technical feasibility so that you have the right cost estimates and expected ROI of the solution so that BOD can approve investment?
A have listed down some of the questions that will help you do exactly that.
Here are a set of discovery questions that should be used for the technical and business feasibility of the use case:
- What tools do you use to generate the forecasts today?[look for excel that will imply that there is an opportunity with significant improvement]
- How do you generate these forecasts today? explain the process? [look for download data, run in Excel thru a static algorithm, add a buffer of (say) +/-20%, this implies that just automating the process significant savings in manual labor can be achieved]
- Please share the sample inputs and outputs for forecasting? [This will help you to look at the granularity]
- How accurate are the predictions?[Most of the time business team will not be measuring the accuracy]
- What are the areas of improvement in the current process, that you have in mind?[It can be accuracy or automation of the process of both]
- What data sets are you using currently for the forecast? What additional data sets would you like to incorporate?
- How much would it benefit you to produce a forecast that is, say, 20% more accurate?[This will help clarify the business impact for the investment they are going to make]
At the end of this exercise you will have a very good idea about the technical complexity of the use cases the projected ROI will help business leader get the right budget approval easily. Its a win-win for both you and client.
Feel free to reach out me out if you any similar pre-sales questions.