Last week I hosted our ninth Blue Prism Café, with Naresh Venkat, AI
Business Development & Strategic Partnerships at Google Cloud and
Vivek Khurana, Global Automation Service Lead for Pfizer. Naresh drives
the Google and Blue Prism partnership focuses on drive adoption of AI
and intelligent automation in the enterprise, with many exciting Google
AI capabilities being shipped in the current Blue Prism product. Vivek
is on a fascinating journey to optimize and evolve Blue Prism’s Digital
Workforce Platform in a highly regulated operating environment.
the session, we tackled the big question on everyone’s minds — how can
we take the next step with process automation to make digital work more
intelligent? An exciting discussion indeed.
What is becoming very clear is that we are
undergoing seismic business change across the workplace — with both
routine and non-routine based tasks already being transformed by
automation. To keep pace with this, we are already seeing a shift from
rule based decision-making automation to a more advanced judgment and
self learning-based intelligent automation. This evolution means more
intelligent, digital workers with new skills that include knowledge and
insight, planning and sequencing, visual perception, collaboration,
learning and problem solving.
These skills bring the thinking and the analytical capability to make
operations smarter and autonomous. And this is no longer a theoretical
scenario and is occurring now — as we move from humans consuming data —
to machines consuming data and then executing on new, rethought process.
However, many are still confused about how AI is defined in the
context of automation. Google Cloud’s take on this is interesting;
Naresh thinks the best way to describe it is if you think of AI as the
vision — and machine learning as the tools. So, AI is where you’re
trying to replicate what a machine does in the most appropriate and most
efficient way and machine learning is the tool you’re using to do it.
provides some excellent insight on the potential of automation and AI
gained from within his own highly regulated, operating environment at
Pfizer. He is seeing a shift from pure, rule-based, decision making
automation — to a more advanced, self-learning, ‘intelligent automation’
— which is driven by AI. He sees AI as ‘intelligent assistance’ —
namely a set of capabilities that are used to solve complex business
problems. The basis of this — which is a combination of audio
recognition, image recognition, or natural language processing — is
already being applied en masse at Pfizer.
One of many areas that are driving Blue Prism forward is an exciting
tech innovation partnership with Google Cloud. The first initiative is
providing access to the best of Google Cloud’s data sources to satisfy
the ‘big data’ needs of Blue Prism’s users. The second involves easy
access to Google’s AI APIs that include Vision, Speech and Translate —
from within Blue Prism’s Digital Exchange platform.
initiative is integration of machine learning engine into the workflow.
This is where workers can directly feed data into the Google Cloud
machine learning engine and use that training data to perform
statistical analysis. This gives users the ability to build their own
machine learning and prediction models — right from within Blue Prism.
To make it easy for users to access these services, Blue Prism has also
created an authentication integration system.
From an adoption
perspective, Google Cloud’s Naresh believes that RPA is perfectly suited
for injecting machine learning into business processes. In fact, to
achieve natural language processing in an email automation flow, it
should be as simple as a drag and drop and a couple of clicks.
real-world use cases and examples using artificial intelligence are
cited that include invoice automation support and using sentiment
analysis for automating support calls and tickets. Another interesting
area is the integration that Google Cloud has done with Blue Prism on
its cloud machine learning, which is unique and can potentially enable
non-deterministic decision-making process too.
all about understanding data. The central core for intelligent
automation is bringing data sets together from various disparate systems
that exist in an organization, then when AI and machine learning is
added, an outcome is achieved that couldn’t have been done without those
data sets. In fact, insights can be derived from the data and used to
drive automation and change the way people do things, or work is done in
the workflows — in interesting ways.
During the Blue Prism Café session, our expert panel answered some key questions from the audience, here’s the pick of these:
Will AI replace humans?
a word, no. But the key here is for humans to responsibly guide the
development of machines and AI systems – step by step – and not just
from a technology standpoint but also from an ethical standpoint too.
Ultimately, we are going to partner with machines to better secure our
well-being and create a better future.
What are some ways AI can be implemented into business practices – other than machine learning?
could simply be a rules-based piece of code that’s looking for specific
things in a piece of document and there’s a specific action – without
involving machine learning. AI is very probabilistic, so a simple script
could be written which looks for specific patterns in files.
are a lot more capabilities too like audio recognition, image
recognition. These features are very powerful, they’re already in
current devices and can now easily be deployed too. Also, consider tech
bots that can now be talked to as if they were human. They can also pull
in content from the right sources to be able to determine intent and
then point people in the right direction.
How can you use AI where sufficient datasets aren’t available to train the model in newer organizations?
depends on what kind of AI is being chosen. It may be worth trying off
the shelf APIs, which have been pre-trained with datasets from Google
Cloud – then as the accuracy of those models is improved, data can be
fed to create customized models. However, when building customized
models, humans must be in the loop and the feedback process. As new
datasets come in, they can be fed data so they learn from the original
one. Look at the output of that and then use it to make new data to
improve and deploy on that model.
Can you give examples of how you can scale up the use of AI with RPA?
Pfizer, we’ve seen several used cases where we do attended automations
or we’re using human judgment to do a parallel check after the machine
has proven the insights. So we add that as an additional step in the
workflow just to validate – before we actually start making decisions
based on that. There are several strategies that you can use to scale
up. We do a lot of proof of concepts to pull out the technology before
we actually get those things in production.
Why did you select Blue Prism and how did your first RPA install go? Quick recap of your experience and lessons learned.
went through a detailed process when selecting Blue Prism. We
interviewed several technologies on multiple parameters and were looking
for an enterprise scalable platform that could be handled in a
regulated environment – with all the audit control that we needed. I got
tremendous help from Blue Prism while doing the install. Obviously, we
used our partners to do the install and to get it validated. They’ve
been phenomenal in helping us through this.
There’s a lot of
learning too – especially around the realm of the delivery model itself.
We were able to expand on the helpful templates and model to improve
and validate – so we are now certified for GxP and SOX. If you haven’t
heard of that term from a pharma or healthcare company, it needs a lot
of work to gain compliance and we eventually achieved it.
There are two key takeaways that can be highlighted from our session.
Firstly, artificial intelligence is intelligence assistance. Its aim is
to make human life easier — so we can focus on higher value work.
Secondly, AI is a set of capabilities that is still being developed, the
technology is maturing but I think it’s going to be phenomenal as we go
On a final note, the 6.3 version of Blue Prism is coming
out with additional goodies that Google Cloud has to offer. So, plug in
and give us feedback. We would love to hear from you on what works,
what doesn’t work and how Cloud AI is helping process automation.