A Platform to bring AI and RPA together

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:

Now that you have brought those two different worlds together, there is almost no limit on what you automate. You can blend both rule based and the model based automations in the same flow to tackle with really challenging and complex tasks...

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. 

Sometimes you need more than RPA...

While you can automate many processes with the "basic" RPA, there are always cases when you need more than this. You can of course collect data from disparate sources automatically and process it, but you cannot find patterns and insights to make complex decisions. In order to achieve this, you need AI.

When you have both of them in place, you get the most benefit out of your automations since then you can easily overcome the barriers. AI enables automation of processes that include:

• Uncertainty, when for instance you cannot determine an outcome with 100% certainty (like loan defaults, property valuation)
• High Variability, when there is too much variability to apply any rules (like language translation, resume matching)
• Unstructured Data, since it is pretty challenging to process it (like extracting information from articles, images and videos)

Let's pretend we are a contact centre manager and our agents are receiving thousands of emails every day. We want to know how many of them are complaints and also what kind of complaints. We want to then categorize them and distribute them to the right skilled agents so that they are answered. This is a very typical scenario, where you can and must use automation in order to take the unnecessary load from your agents so that they can focus answering your customers instead of classifying those emails. To make the scenario even more complex, assume that you are receiving emails in 10 different languages...

Without using the AI, which can read the emails, translate them, look into the content, understand if the feeling in an email is positive and negative, you cannot achieve much. And by using it, you can have grateful, happy agents and even happier customers since their emails will be returned quicker...  

At the end, it all comes to allow you to focus on the things you need to do, which is the funnier part of your job, especially when you can become more creative by doing the tasks that also make you more productive. Those things that make you "You" and unique. Marry AI and RPA when you think of automation next time... You will not regret it :) !

Basic RPA

 The End-to-End Automation process consists of three base concepts:

• Build
• Manage
• Run

The software robots need to be instructed somehow so that they can perform the tasks we want them to. We need to BUILD and program them by giving those instructions before they start to fulfil any requirements from us. Different design tools can help us with this. You can either choose to write some programming code or if the tool permits, use some drag and drop functionalities while teaching the robots what to do, which makes your life easier, for sure.

When programmed correctly, the robots can RUN 7/24, without taking any breaks and making mistakes. If a robot makes a mistake, it is either the fact the environment where it operates has changed (like an updated webpage causing a link the robot is trained to click on disappears) or the developer has not programmed it correctly. So you know what and who to blame!

The in-between component in this picture, is about how to MANAGE the robots. By programming, the robots learn exactly how they should tackle with different tasks, but how they can be made aware of when to run, when to stop and which of them will run which task? Even in more complex scenarios, a queue mechanism may come into the picture if there are too many actions and the actions need to be prioritized. MANAGE part helps with those challenges.