We know that making bots smarter is the next generation
of robotic process automation
often referred to as hyper automation.
AI and RPA is frequently considered accelerated capture
or process mining capabilities,
but that's not the full story.
AI matters with RPA.
When we say bots working more intelligently,
we mean providing resilience
where the bots make less errors, facilitating AI,
where the bots allow enterprises
to easily apply artificial intelligence
directly to their tasks in cognitive processing,
allowing bots to make judgment calls
that previously were reserved for humans.
I'm Zach Silverstein, IBM RPA America's Technical Lead,
and I'm excited to present to you today
about how AI powered automation can infuse RPA
with cutting edge capabilities
to make your bots work smarter for you.
Not every task we deal with is perfectly structured
for a traditional RPA implementation.
The tasks our knowledge workers execute
often require some cognitive thought processes
such as categorizing tickets, understanding an email,
or cross-checking names.
With IBM RPA, we can accelerate our implementation
of those no-thought and low-thought automation opportunities
natively in just a few lines.
Some of these include are easy-to-implement knowledge basis
as well as our AI processing through drag and drop commands.
So let's talk about our scenario.
We have ACME Magazine Company
implementing a handwriting capture solution
on their pre-addressed mail.
ACME has a list of their current customers,
but if they get a response flyer,
a human must still manually validate
that the submitting customer isn't already subscribed.
Oftentimes people may use nicknames or the capture solution
may not perfectly extract the handwriting.
So previously RPA couldn't be applied well.
Let's see just how easy it is to us automate.
This is our IBM RPA studio,
on left-hand side we can see our toolbox
with over 650 commands
including AI, browser automation, terminal integration,
and a ton of other pre-built commands.
Right in the middle of this screen,
we can see a command pallet that we've partially filled out.
Now let's take a look at our two files.
We have our CSV file of the new customers
from the capture system and an Excel file
of our existing customers.
We can see some capture errors, similar characters,
and even people submitting cards with their nicknames.
Let's go ahead and now execute that bot.
We can see that there's only one perfect matching record
based on first name and last name.
Now let's add a bit of AI.
We'll go ahead and grab the approximately equal command
and drop it right into our script.
We're going to provide the source text
for the existing customers, first name and last name,
and we're going to compare that with the target text
of the new customers first name and last name.
We support many different algorithms,
but due to the type of data we're working with,
we'll use a weak Tolerance Jaccard Distance
for its flexibility.
We've now simply applied AI to our bot.
Next let's have our bot print out the results
of that AI action.
We'll grab our log message command
and drag it into our script,
and we'll type is existing customer first name last name
similar to new customer first name last name,
and then our flaq.
Let's now go ahead and execute the script.
The results we can see reflect
AI cognitively comparing the names.
Now we can see our bot is already using AI
in just one command
to apply fuzzy logic to painful everyday scenarios
that plague businesses.
With just a few clicks,
we can make your bots work smarter for you using IBM RPA
and its AI powered automation capabilities.
Thank you for watching,
if you have any questions, please drop us a line below.
If you want to see more videos like this in the future,
please like and subscribe.
And don't forget if you want to learn more about
AI powered automation from IBM,
please check out the links below.