BusinessDay 26 Feb 2018

40 BUSINESS DAY
C002D5556 Monday 26 February 2018
MondayMorning
In association with
Harvard
Business
Review
We need to approach artificial intelligence
risks like we do natural disasters
PRASHANTH GANGU
The risks posed by
intelligent devices
will soon surpass
the magnitude of risks
associated with natural
disasters. Tens of billions
of connected sensors are
being embedded in everything
ranging from industrial
robots and safety
systems to self-driving
cars and refrigerators.
Our growing reliance on
so many intelligent, connected
devices is opening
up the possibility of global-scale
shutdowns.
INTELLIGENT DEVICE
RECOVERY PLANS:
As with the risks associated
with natural disasters,
companies cannot completely
protect against
smart-device risks by buying
insurance; they must
have worst-case scenario
recovery plans. Managers
have to figure out their
higher and lower risk intelligent
device vulnerabilities,
add in redundant
systems and potentially
set up the A.I. equivalent
of tsunami early-warning
systems. In addition, they
need the ability to switch
to manually controlled
environments in case artificially
intelligent systems
have to be shut down,
and to recall faulty smart
products.
INSURANCE PRODUCTS
AND SERVICES:
Insurers should quantify
their exposure to a global
intelligent device meltdown,
offer new products
and advise companies
and governments.
As they have for natural
catastrophes, insurers
can also encourage
public sector safeguards.
Since insurers cannot
completely mitigate the
outsized risks posed by
extreme weather events,
governments of many
developed countries and
international organizations
provide natural ca-
tastrophe relief through
government agencies.
Insurers need to help mo-
bilize similar public sector
resources to help the
potential victims of an
Are the most innovative companies just the ones with the most data?
A.I.-enabled smart device
disaster.
INTERNATIONAL PRO-
TOCOLS:
Finally, policymakers
should establish international
trust and ethics
guidelines to govern the
development and implementation
of ever more
advanced A.I. products
and systems. To reduce
the future impact from
natural disasters, governments
and international
organizations like the Red
Cross and the World Bank
collect and share data concerning
the destructive
ramifications and the support
required to help victims.
Similar intelligence
will be critical to curb the
impact of potential smart
device shocks as artificial
intelligence evolves.
(Prashanth Gangu is a
partner in the insurance
and digital practices at
Oliver Wyman.)
VIKTOR MAYER-
SCHÖNBERGER AND
THOMAS RAMGE
The cases of startups
with superior ideas
dethroning well-established
incumbents are
legion. For decades, “creative
destruction” ensured
competitive markets and a
constant stream of new innovation.
But what if that is
no longer the case?
The trouble is that the
source of innovation is shifting
from human ingenuity
to data-driven machine
learning. Of course, it takes
plenty of talented, creative
people to build these products.
But their improvement
is driven less by human “a
ha” moments than by data,
and improvements in how
machines learn from it.
Sometimes companies
have to go out and collect a
specific kind of data; think
of Google’s cars roaming
the streets of Silicon Valley.
And sometimes companies
pay for access to data so that
their systems can learn. But
more often than not the data
that fuels innovation is being
generated by users interacting
with an existing digital
service. When we accept Siri’s
suggestion, it’s feedback
to Siri that she got it right.
If innovation is founded
on data rather than human
ideas, the firms that benefit
are the ones that have access
to the most data. Therefore,
in many instances, innovation
will no longer be a
countervailing force to market
concentration and scale.
Instead, innovation will be a
force that furthers them.
The specter of companies
with access to data becoming
data-driven innovation
leaders, leaving smaller
competitors and startups
behind in the dust, should
concern policymakers intent
on ensuring that markets
stay dynamic and competitive.
Their challenge is less to
realize the problem than to
devise a solution that keeps
markets competitive without
stifling data-driven innovation
on the whole.
For many innovative
companies, the next few
years will be a time of reck-
oning: As the power of datadriven
innovation increases,
these more conventional innovators
will have to find access
to data to continue to innovate.
That necessitates at
least two huge adjustments.
First, they need to reposition
themselves in the data value
chain to gain and secure data
access. Second, as innovation
moves from human insight
to data-driven machine
learning, firms need to reorganize
their internal innovation
culture, emphasizing
machine learning opportunities
and putting in place
data exploitation processes.
(Viktor Mayer-Schönberger
is professor at Oxford.
Thomas Ramge is technology
correspondent for brand
eins and also writes for the
Economist.)
(C) (2017) Harvard Business Review. Distributed by New York Times Syndicate