Vince DiMascio: I'm Vince Damasio, I'm the Chief Information
and Technology Officer for Berry Appleman & Leiden. So RPA,
we've kind of deployed it in this way, where we start with
the core capability you get out of the box, which is
deterministic, conditional processes. So if you know there's
a defined path, then you can dispatch an automated system to
follow that path. When the outcomes become probabilistic,
it's then that you want to include human subjectivity,
either in the form of a model that has been trained and
approved by humans, or through introducing human review
along the process chain. So we do both. We have some systems
where the automation will take steps on its own, based on
tried and true models that we we've developed. We have
others where we suggest and draft and propose what the next
step should be, and then put that forward for human review
in a manner that's much more efficient than a human having
to do the research that would be required to develop such a
draft. We're already seeing material value in our RPA
investment. And that's coming through. It's not so much
about revenue growth as it is about revenue growth as a
byproduct of exceptional client service that comes out of
this. So we're, we have sophisticated technology clients,
many of our clients are the big tech companies and they want
a user experience that's, that's phenomenal. And that means
self service. That means ondemand anytime and so if you have
a robot available to do work, whether it's reviewing
material that a foreign national would submit in connection
with the visa application, or looking up statuses for
applications in progress, things like that, that happens in
real time for us. And that's something that is just not a
sustainable model with human staff.
So we're doing things that we couldn't otherwise do really
with robots to deliver client service. Quality enhancements
is another obvious, you know, automated systems don't
transpose digits when they're typing things into other
systems. Speed is another, writing much more quickly than
any human finger can move. So those those are the value adds
that we're getting from RPA, from the RPA investment. And
then with machine learning, it's similar, the similar
capabilities with the user experience. For example, you can
upload a passport and the system will say, "This looks like
a passport. Here's the information, is this right?" without
having to ask a person to enter information, likewise with
other document types That's a user experience enhancement
that sets us apart and that our clients love. Also on the
sort of back office side with HR IT facilities and those
systems, we have those operations. We have systems that are
specific to each of those business units, for example, that
should be integrated. But the cost of integrating them is
really high, and therefore doesn't happen.
With RPA, we can make that integration happen without having
to spend the dollars it takes to enable an API and write the
code to connect those systems. So that's one example of a
big lift you get from RPA acting as the glue between systems
and likewise, the, you know, the traditional processes that
you hear about often with RPA are, we've made those manifest
at our organization, employee onboarding, separation, so
that we comport with our policies around data privacy, you
know, turning off access to systems, things like that.
There's an operational efficiency angle there. And there's a
risk management angle there that are both very important to
us as a law firm. It's the solution that's going to hold for
us. The change, the rate of change of API systems, API based
systems, is not much slower than the rate of change of UI
based systems. A lot of the UI based systems, including the
systems we build and maintain, our single page apps with
restful endpoints, and therefore the UI may not even matter
even if you're using HTTP to connect and do the work. So
we're finding that this is that big risk, the scary risk
that people have been talking a lot about, "oh UI changes
everything breaks," is not as scary as some make it out to
be. The deployment pipeline, the Security Operations Center
in IT specifically, right, we're looking at using bots in
both of those places. So for example, if a server goes out,
let's say an email notification or some system notification
occurs, then our bot will cordon that system off and do the
diagnostics that an entry level or associate level Security
Operations Center employee might too. And that then will the
report will always be complete, it'll fingerprint the
system, they'll identify what symptoms they've seen. And
they'll lock down the network and forward the report to a
human who's then armed with a reliable set of information
that's already been very recently and accurately collected
by the bot so that that person can take the next steps that
require the subjective human element. And that's that's big
for us.