The Business of Data Analytics and Artificial Intelligence - Part III

As we continue our discourse towards assessing the actual cost of building and deploying an Artificial Intelligence solution for your business, the time has cometh for us to look at humans, who eat sleep and breathe data analytics.  

Humans Cost Money 

As we humans have mouths to feed, bills to pay, places to go and people to see. Problems that a machines and servers are not saddled with.  

Before an AI model is to be trained, you have to manually clean and label large datasets. A process that is expensive, requires trained manpower and once the training is done, you need to train new data sets to maintain accuracy. There is a continuous feedback loop. A lot of human burden can be gradually reduced but even that requires a lot of fodder to keep the fire burning.  

Training aside, the core engineering team required to design a successful system is literary double that of a non-ai software solution.  

“We did start the fire, and we have to keep it burning” 

It may seem that with increased automation, human intervention will be required less but so far, the data suggests that is not possible. Even if you achieve complete automation for certain tasks, one has to weigh in the improved margins – to justify the input costs.  

Near Zero is Impossible 

There are systems engineers, data engineers and cloud engineers – a core of highly specialized and trained set of people who make for a successful ai platform. You will never reach near zero costs associated with SAAS or an on-premise software solution. 

To conclude for now, the basic function of an AI application is to process input data and generate relevant predictions. The cost of operating such system will always remain high and one must step into it judiciously.