Intelligent process automation: The engine at the core of the
next-generation operating model
Full
intelligent process automation comprises five key technologies. Here’s how to
use them to enhance productivity and efficiency, reduce operational risks, and
improve customer experiences.
Since the financial
crisis of 2007–09, many companies
have applied lean management to improve cost efficiencies, customer
satisfaction, and employee engagement simultaneously, and many programs have
achieved substantial impact on all dimensions. Progress on digital, however,
has been more uneven.
In the insurance
sector, for example, an October 2016 FIS study found that 99.6 percent of
insurers surveyed admitted they face obstacles in implementing digital
innovation, while 80 percent recognize they need digital capabilities to meet
business challenges. This difficulty has been compounded by the boom in
“insurtech” investments in 2016—topping $3.5 billion in funding across 111
deals since 2015.
As macroeconomic
conditions continue to put pressure on profit margins across sectors, cost
productivity and unlocking new value are back at the top of the
senior-management agenda. The question is, what else can be done?
That’s where
intelligent process automation (IPA) comes in. We believe it will be a core
part of companies’ next-generation operating models. Many companies across
industries have been experimenting with IPA, with impressive results:
·
Automation of 50 to 70 percent of tasks . . .
·
. . . which has translated into 20 to 35
percent annual run-rate cost efficiencies . . .
·
. . . and a reduction in straight-through
process time of 50 to 60 percent . . .
·
. . . with return on investments most often
in triple-digit percentages.
New technologies that
promise double-digit or even triple-digit same-year returns should rightfully
be viewed with skepticism. But our experience shows that the promise of IPA is
real if executives carefully consider and understand the drivers of opportunity
and incorporate them effectively with the other approaches and capabilities
that drive the next-generation operating model.
What is intelligent process automation?
In essence, IPA “takes
the robot out of the human.” At its core, IPA is an emerging set of new
technologies that combines fundamental process redesign with robotic process
automation and machine learning. It is a suite of business-process improvements
and next-generation tools that assists the knowledge worker by removing
repetitive, replicable, and routine tasks. And it can radically improve
customer journeys by simplifying interactions and speeding up processes.
IPA mimics activities
carried out by humans and, over time, learns to do them even better.
Traditional levers of rule-based automation are augmented with decision-making
capabilities thanks to advances in deep learning and cognitive technology. The
promise of IPA is radically enhanced efficiency, increased worker performance,
reduction of operational risks, and improved response times and customer
journey experiences.
IPA in its full extent
encompasses five core technologies:
·
Robotic process
automation (RPA): a software automation tool that automates routine
tasks such as data extraction and cleaning through existing user interfaces.
The robot has a user ID just like a person and can perform rules-based tasks
such as accessing email and systems, performing calculations, creating documents
and reports, and checking files. RPA helped one large insurance cooperative to
reduce excess queue procedures affecting 2,500 high-risk accounts a day,
freeing up 81 percent of FTEs to take on proactive account-management positions
instead.
·
Smart workflow: a process-management software tool that integrates
tasks performed by groups of humans and machines (for instance, by sitting on
top of RPA to help manage the process). This allows users to initiate and track
the status of an end-to-end process in real time; the software will manage
handoffs between different groups, including between robots and human users,
and provide statistical data on bottlenecks.
·
Machine
learning/advanced analytics: algorithms that identify patterns in structured
data, such as daily performance data, through “supervised” and “unsupervised”
learning. Supervised algorithms learn from structured data sets of inputs and outputs
before beginning to make predictions based on new inputs on their own.
Unsupervised algorithms observe structured data and begin to provide insights
on recognized patterns. Machine learning and advanced analytics could be a game
changer for insurers, for example, in the race to improve compliance, reduce
cost structures, and gain a competitive advantage from new insights. Advanced
analytics has already been implemented extensively in leading HR groups to
determine and assess key attributes in leaders and managers so as to better
predict behaviors, develop career paths, and plan leadership succession.
·
Natural-language generation
(NLG): software engines that create seamless
interactions between humans and technology by following rules to translate
observations from data into prose. Broadcasters have been using
natural-language generation to draft stories about games in real time.
Structured performance data can be piped into a natural-language engine to
write internal and external management reports automatically. NLG has been used
by a major financial institution to replicate its weekly management reports.
·
Cognitive agents: technologies that combine machine learning and
natural-language generation to build a completely virtual workforce (or
“agent”) that is capable of executing tasks, communicating, learning from data
sets, and even making decisions based on “emotion detection.” Cognitive agents
can be used to support employees and customers over the phone or via chat, such
as in employee service centers. A UK auto insurer that uses cognitive
technology saw a 22 percent increase in conversion rates, a 40 percent
reduction in validation errors, and a 330 percent overall return on investment.
What might IPA look
like in action? Let’s take an insurance company where a human claims processor
pulls data from 13 disparate systems to provide a “business as usual” service.
With IPA, robots can
replace manual clicks (RPA), interpret text-heavy communications (NLG), make
rule-based decisions that don’t have to be preprogrammed (machine learning),
offer customers suggestions (cognitive agents), and provide real-time tracking
of handoffs between systems and people (smart workflows).
The value of IPA
While IPA takes over
rote tasks, human workers can focus on delighting customers and thinking about
how new troves of data outside the organization—from news, events, social
media, embedded sensors, and elsewhere—can help achieve business goals.
Although the full range
of benefits comes from implementing the complete IPA suite, companies can
unlock significant value quickly through individual elements. RPA alone can
drive significant productivity gains.
One large financial
institution used an RPA transformation at scale to automate 60 to 70 percent of
tasks in record-to-report processes and create annual run-rate efficiencies of
30 percent or more. Using the same methodology, another institution achieved an
80 percent reduction in processing costs in excess queue procedures. Yet
another financial institution in the FT500 used robotics to unlock a £175
million annual reduction in costs and save over 120 FTEs.
In addition, IPA helps
leaders get the most out of decades of investments in a multitude of complex
systems and make many complicated decisions simultaneously. We have also seen
businesses insert controls to activate additional processes triggered by new discoveries
in real time. Creating an unsupervised machine-learning platform coupled with a
natural-language generation engine, for example, will soon allow the processing
of structured daily performance data to create deep insights that help leaders
make better decisions, while shifting internal management processes at the same
time. No longer will painful-to-create reports with limited functionality be
required, only to pile up on desks. In the insurance industry particularly,
there are areas where IPA could have massive impact.
How to get started on your IPA transformation
IPA does not require a
significant infrastructure investment since it addresses the presentation layer
of information systems. RPA software, for example, sits on top of existing
systems, enabling it to be implemented to achieve rapid returns without
changing the IT back end. In some cases, companies can get RPA systems up and
running—and delivering value—in as little as two weeks.
In our experience, the
following steps are the most important in driving successful IPA
transformations at scale:
1. Rapidly align on IPA’s role in operating model
Any effective IPA
initiative must be grounded in a clear understanding of the overall strategy of
the business and the role of the next-generation operating model in helping to
achieve it. That requires a clear articulation of the target end state and the
journeys to focus on to reach it. Such clarity allows business leaders to
evaluate and align on the approaches and capabilities to implement to drive the
operating model. In many cases IPA has an important—even dominant—role in
driving the change, but its greatest value comes when companies understand how
it can work with the other capabilities and approaches in the operating model.
Automation is coming, and now is the time to define the art of the possible and
apply it strategically where it makes most sense.
2. Design around the full portfolio of IPA solutions to
maximize impact
Organizations should
not dabble with a few IPA technologies. The world moves too quickly for that
approach to work effectively. The full impact comes when IPA technologies work
together.
Organizations need to
envision and implement holistic optimization programs to maximize return on
investment. Though it is easier and faster to implement automation projects in
silos, such an approach is inherently flawed. By themselves, individual
technologies are insufficient to capture value. Instead, fundamental process
redesign is required to transform the way a group works.
A detailed roadmap for
implementation should be created to identify all automation-enhancement
opportunities and allow businesses to sequence IPA initiatives by balancing
their impact with the feasibility of scaling solutions from initial use cases.
Start your IPA journey by rapidly creating an overview of current tasks and the
resources and capabilities needed to carry them out. Then deploy an experienced
ring-fenced incubator team to redesign processes and group workflows based on a
deep understanding of lines of business and IPA capabilities.
3. Build a rapid minimum viable product (MVP)
Even though it’s
important to design for a full IPA portfolio, it can be daunting to start
working on everything at once. Many executives have been burned by promising
complex data-warehouse projects, some of which have taken up to a decade to
complete and have run vastly over budget. As with other digitization efforts,
it’s better to select—with a bias to speed and impact—an end-to-end process or
customer journey to redesign and enhance using IPA, and then work to launch an
MVP, the most stripped-down version of the product that can still accomplish
the task. In this way, you can quickly test what works and what doesn’t and
make changes accordingly.
IPA can deliver
tangible value in weeks rather than years in the form of fewer errors and less
“busy work” for back-office employees. The rapid returns from early pilots help
to secure support from stakeholders and executive sponsors for a much deeper
program to harness the potential achievable from a full IPA transformation.
4. Build momentum and capture value
Any IPA implementation
should combine quick wins with larger longer-term developments. The detailed
roadmap should be rooted in a fundamental process redesign that sequences
automated modules for production and reimagines the way groups should work to
capture value.
Every product line in
insurance, for example, has a different degree of potential for standardization
and automation, and needs to be examined and sequenced. Look at time-intensive
processes in sales, underwriting and pricing, policy administration, claims and
finance, and accounts, and start with a clean sheet when deciding how they will
work in future.
5. Embed lasting capabilities to achieve sustainability
One successful way to
sustain value creation is by creating a center of excellence (CoE) to govern
the transformation and support the rapid deployment of IPA solutions through
capability building, certification and standards, vendor management, and the
creation of a library of reusable solution patterns. Such a CoE should be
centrally located and can be fairly small in size because it can call on
existing lean or process-optimization CoEs, while business ownership and
execution should sit in the lines of business or in digital factories.
Systematic controls
need to be in place, and organizations should embed critical business-analysis
and digital skills in lines of business so that they can own the process. They
also need to redesign organizational structures to capture value, establish a
future-state operating model to scale up their IPA initiatives, create
blueprints for future structures to capture impact and embed new capabilities,
and offer training and workshops to explain why the automation of manual
processes will free up teams to focus on more creative activities.
It’s crucial to engage
your business and your functional teams in the process—for example, by building
bots—and to establish reusable assets such as playbooks. The most successful
way to build lasting IPA capabilities is through a learn-by-doing approach that
combines coaching, on-the-job training, and knowledge sharing. To capture value
at enterprise scale, organizations need people with deep skills in IPA levers,
process redesign, and lean principles as well as domain expertise. Technology
skills alone will not be sufficient. Many organizations opt to bring in
external support to supplement their talent pool and accelerate the
transformation of the enterprise.
6. Carefully coordinate change management and communications
As in any large
transformation program, a robust communications plan will be required to help
manage redeployment, generate excitement, and align the change story with
corporate strategy. Success in establishing the new execution model will depend
on how far it is aligned with the organization’s culture and how well people
are able to adapt to agile practices. In addition, change champions will need
to be developed internally to make the transformation a success.
Companies are using IPA
to invest in and develop new platforms, engage with customers, and win over
advisors, all at a dramatically lower cost. But companies are only scratching
the surface of what is possible. Tomorrow’s winners are those that embrace these
capabilities as part of a next-generation operating model and move quickly to
capture the value from them, pulling away from the laggards who choose to dip
in only one toe at a time.
By Federico Berruti, Graeme Nixon,
Giambattista Taglioni, and Rob Whiteman
http://www.mckinsey.com/business-functions/digital-mckinsey/our-insights/intelligent-process-automation-the-engine-at-the-core-of-the-next-generation-operating-model?cid=reinventing-eml-alt-mip-mck-oth-1703
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