According to "AI:Built to Scale" by Accenture, 75% of the executives believe they risk going out of business by 2025 if they don't scale AI across their organizations. Yet, only 16% of them could move the AI projects into production. It is good that the awareness is there when it comes to adapting AI into the lines of business, but why are they failing, then ?
Let's consider the following (dream) scenario:
Assume that a company has already implemented RPA and they have fully understood and embraced all the benefits of it. Their efficiency has increased, they have gained 20000 hours a year by automating many mundane tasks. Their employees are happier than ever because they have robot colleagues taking care of all the boring parts of their jobs. The management now has started to think what would be the next step in their automation journey since they see also a huge potential in automating other processes that are not rule based. (As you know, this is mainly RPA's job 😉). Who will then take care of those kind of automations where there are exceptions, uncertainties, variabilities and unstructured data to deal with ?
This is when AI comes into the picture, but how can we make sure that AI and RPA can meet on one common platform?
This is a technological challenge, for sure. As data scientists and RPA developers have two different worlds they are living in. They use different platforms while developing automations and training their ML models.
So having AI in mind is perfectly fine, but you should also have a platform to consume AI and ensure that it works hand in hand with RPA.
AI Center is a good example to this. (You can read more about it by clicking here.) It is not a platform to develop ML models, but it is a one where you can deploy the ML models you have developed. You can also use the ones provided in the platform, called OOB (Out of Box) models. You can create data sets and then run pipelines (training, evaluation or full) by using the ML model and the dataset. The product of a pipeline then becomes an ML skill, which you can let RPA developers consume in their automation flows. You can of course create an ML skill without running a pipeline. To make an ML skill more intelligent is also possible via the platform as you can train those skills.
The following picture summarizes pretty well what I have talked about:
In my next blog entry, I will discuss some examples where different ML skills are embedded into RPA flows to manage difficult automations, like language translation, text understanding, email classification, sentiment analysis, etc.