Machine-reengineering is a way to automate business
processes using machine learning. Although machine-reengineering is new,
companies are already seeing striking results with it, particularly in boosts
to speed and efficiency. Studying 168 early adopters, we’ve seen speed
improvements of two times or more for most business processes — and some
organizations are reporting speed improvements of 10 times or more.
How do companies do it? Our
study found that organizations are using machine-reengineering to establish new
forms of human-machine collaboration that break through the bottlenecks of
complex digital processes. In some cases, such as interpreting images or
writing reports, machine-reengineering directly helps workers perform digital
tasks. In other cases, machine-reengineering helps people uncover insights that
are buried in a mountain of data. Here are some examples of how companies are
using the speed and smarts enabled by machine-reengineered processes.
Scanning Images, Voice and Text
As companies implement
digital strategies, they introduce new labor-intensive tasks to sort through
all the data they’re collecting. This data is highly unstructured and produced
in a variety of formats at an ever-larger scale, which requires people to
arduously scan through it for specific items to complete a single process step.
Human-machine collaboration focused on digital-data scanning can accelerate at
least three kinds of routine digital tasks.
Previewing video. Clarifai, based in New York City,
uses machine learning to find people, objects, or scenes in videos in far less
time than a person can. In one demonstration, a 3.5-minute clip was analyzed in
just 10 seconds. The technology can pick out kinds of people — mountain
climbers, for instance — to help advertisers more efficiently match ads to the
videos. It can also be used to help editors and curators find new ways to
organize video collections and edit footage. This kind of auto–editing
assistant can dramatically change the day-to-day tasks of workers in media,
advertising, and film.
Interpreting
images.
MetaMind, in Silicon Valley, offers a service called HealthMind, which uses
computer vision to analyze medical scans of brains, eyes, and lungs to find
tumors or lesions. HealthMind relies on deep-learning techniques for natural
language processing, computer vision, and database prediction algorithms. The
upshot of HealthMind is that doctors spend less time interpreting images and
more time consulting with their patients.
Documenting and
data entry. Machines
can learn to perform time-intensive documentation and data entry tasks, letting
knowledge workers spend more time on higher-value problem-solving tasks. The
London-based startup Arria, for instance, helps its customers automatically
generate reports in industries ranging from health care to finance to oil and
gas. The company’s natural language processing technology learns how to write
reports by scanning texts and determining relationships between concepts. Then
it scours incoming data to build new reports. Arria has found that the process
changes can increase knowledge workers’ productivity by 25%. Engineers, for
example, have saved up to 40 hours of reporting task time each month.
Uncovering Buried Insights
Increasing the amount of
data in a workflow can increase the amount of time needed for insight and
action. We’ve seen this in stock trading, marketing, and manufacturing, where
more data streams make it harder to find information that is urgent or
meaningful. With machines as sidekicks, though, people can more quickly find
valuable insights buried in big data. Our research found companies demonstrate
this in at least four types of analytical tasks.
Market monitoring. Dataminr, based in New York City,
uses a variety of indicators to identify tweets containing relevant information
for stock traders. By monitoring the propagation of information
throughout the network, Dataminr evaluates relevance and urgency. An alert sent
to a trader that provides even a three-minute advantage can translate into
significant profit. News services are using Dataminr to find breaking news, which
lets reporters cover stories faster.
Predictive
modeling.
SailThru, also out of New York City, helps marketers deploy more effective
promotional emails by analyzing email and web data to build customer profiles.
SailThru’s system learns customers’ interests (biking versus rock climbing, for
instance) and purchasing behaviors, and then predicts which individuals will
make which purchases and when, delivering the right message when it’s most
effective. The Clymb, a SailThru customer that sells outdoor gear, saw a 12%
increase in email revenue and an 8% increase in total email purchases within 90
days of turning on SailThru’s personalization. After combining personalization
with predictions, The Clymb saw a 175% increase in revenue per thousand emails
sent, as well as a 72% reduction in churn.
Root cause
analysis.
Sight Machine, a manufacturing analytics company based in San Francisco and
Livonia, Michigan, helps its customers solve complex quality control issues.
One problem that Sight Machine’s customers face is interpreting alerts: A
quality problem or incident can trigger hundreds of alert codes from
potentially thousands of different kinds of sensors along an assembly line.
Sight Machine’s software uses machine learning to interpret the patterns of
these alerts, helping engineers to quickly pinpoint the few alerts that
represent the root cause of the problem, separating them from the ripple effect
alerts.
Predictive
maintenance.
Machine learning can also aid human decision making by discovering meaningful
patterns in factory data that people would otherwise be unable to find.
Consider Sight Machine again: By analyzing data for patterns that occur before
trouble hits, the company’s systems help manufacturing engineers anticipate and
prevent problems. For one client deploying a new robotic manufacturing line,
Sight Machine was able to reduce downtime by 50% and increase performance by
25% within one month — far better than the 1%–2% performance increases typical
of the client’s industry.
It’s still early days for machine-reengineering, so we
expect our research to uncover many more new types of machine sidekicks. But
it’s already clear that machine-reengineering has the power to help manage the
data deluge — and resulting bottlenecks — that modern organizations face.
Workers can become more efficient and effective, which improves workflows as
well as the bottom line. If data is the path forward, machine-reengineering is
paving the way.
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