In the Second Machine Age, it’s not so clear whether
humans will be complements or machines will largely substitute for humans; we
see examples of both…… Erik Brynjolfsson,
Schussel Family Professor of Management Science at the Sloan
School.
Machines have always been
integral instrument available to workforce to deliver work more efficiently and
consistently. They performed routine and well-structured tasks, be it manual or
cognitive, helping workers boost their outputs.
But with improvement in technologies such as AI, Cognitive computing, Natural
Language processing, big data analytics, machines have become lot smarter. Today some of the activities which were unquestionably
human (being complex, non-routine), can also be performed with machines, not
only efficiently but also with higher quality outputs.
There are different projections
about the extent to which Smarter Machines (SM) will be introduced in our work delivery
model, working alongside or instead of human colleagues. Carl Frey and Michael Osborne have conducted
analysis of 702 occupations to rank their susceptibility to technological
advancements linked risk. According to
their estimate, around 47% of total US employment is in high risk
category.
If this is to happen, are our
organisations ready to embrace this challenge, which will manifest itself in
terms of leadership expectations, performance measure, workforce strength,
skill requirements or work related policies and processes? Have we started thinking about the impact of
these smarter machines on our organisation, and what should be our approach to
managing it? Is it time we start asking
these questions?
New machines display significantly
higher capabilities, when compared to traditional machines on three key
dimensions:
SEARCH CAPABILITIES: While traditional machines were able to
search from within structured data sets in prescribed formats, SM are able to
search from structured and unstructured data, coming from multiple sources, in
different formats, almost in dynamic manner.
SOLVING CAPABILITIES: While
traditional machines helped solve structured routine well defined and programmable
set of problems, smarter machines are able to increasingly solve non-routine,
context sensitive problems, with increasing accuracy with every usage. SM can identify patterns and trends from
large data sets and put forth advice with associated probabilities.
SERVE CAPABILITIES: Machines can
now serve responses in any format, and converse with humans as near humans with natural language as
medium of conversation.
Every other day, we see new
examples of SM replacing human work, completely or partially, as enterprises
experiment with them. Some published examples
are shared as case in point:
1. USAA,
a financial service company, uses smarter machine to handle its Armed Forces
customers’ queries with regard to transitioning to civilian-life. Machines uses data base of 2000+ questions
and 3000+ military training documents as search base to solve customer
questions and respond in natural language.
(IBV paper)
2. Associated
Press is using smarter machine from company Automated
Insights to autonomously create Quarterly Corporate Earnings stories, from
data coming from Investment Research firm(ZackS). AI algorithm can process
large number of financial announcements, press releases and other information
and then offer personalised financial advice at large scale and lower
cost.
3. Computer
Assisted Translation technology is being employed to speed up the translation
work, as it memorises earlier translations and use for preparing half cooked
translated material for human translators to perfect.
4. Oncologists
at Memorial Sloan-kettering Cancer Centre are using IBM Watson computer to
provide chronic care and cancer treatment diagnostics using pattern recognition
capabilities, with 600000 medical evidence reports and 1.5 million patient
records and trials as referable data set.
5. Deep
Knowledge Ventures (DKV), a Hong Kong venture capital fund appointed
a computer algorithm named Vital to its
board of directors, claiming to be the first company of its kind to give a
machine an "equal vote" when it comes to investment decisions. (BBC)
6. Law
firms rely on smart machines to scan thousands of legal briefs and precedents
to assist in pre-trail research.
7. Dr
Mark Oleynik is working on creating automated kitchen housed by robotic chef
that can create dishes like the professional chef it learns cooking from. (Economist)
Yes, these examples may take time
to go mainstream, but not as much time as earlier technologies have taken. These capabilities are growing exponentially
and the costs of such machines are going downwards, fast, really fast!
While IT and Operational departments are busy identifying suitable
business use-cases and associated technological solutions for incorporating
these SM in the ways of delivering work, HR department needs to be proactively thinking
of its implications for the organisation, leadership and workforce
management. Let us look at each of these
areas:
Leadership: Leaders working
alongside SM, that can throw unbiased data backed probabilistic
recommendations, demands greater maturity and acceptance from the leaders. It
can be a fight between domain expert taking out of experience and SM sharing
advice that is based on pattern recognition from immense data from multiple
sources. Leaders need to be open to reconciling
between gut feel and data based alternatives to problem solving. They need to
be open to questioning their beliefs as domain experts and to re-examine
counter intuitive suggestions coming out of machines. How do we empower leaders
to have the strength of conviction to go their way, despite what SM advices? Are
leaders going to be made accountable for going against SM advice, in case of things
going wrong? On the other hand, how will
you keep domain experts motivated, if in most of the cases they have to go by
SM advice. Finally, leaders need to be
smarter themselves in defining and asking right kind of questions to their
smarter assistants (ie SM).
Workforce Management: SM introduction will replace or redefine some
jobs and consequent workforce requirements.
Those jobs that are high on creative intelligence, social intelligence
or perception and manipulation seems to be least impacted, all others jobs are
vulnerable. Tpo begin with, using the available thought-ware and general
guidelines, we need to have view on the extent of staff that is working on
vulnerable jobs. It would have
implications on the hiring vs contracting decisions, especially with medium
term horizon. Also is there a way to reconfigure set of vulnerable jobs to
ensure that certain category of revised jobs have greater creative
intelligence, social intelligence and perception component? This readjustment effort
takes time hence the need to take cognizance of the impending challenges and
start thinking now.
Skill Development: The scope
for skill development will have two clear objectives- enhancing skills among
staff to work with SM and enhancing skills among employees to help tide over
the transition as their existing jobs get make reconfigured/replaced and they
have to look for alternate ways to stay employed and relevant. In the first
category fall learnings with regard to natural language processing, database
system and administration, and interface design, that would be spearheaded by
technical department and supported by HR.
The second category include trainings with regard to critical thinking,
evidence based decision-making, social intelligence and change management,
where HR has to take lead.
Use-case participation: SM introduction will most likely take the
use-case based pilot approach to implementation. While pilot may be focussed on validating the
value generating potential of the new delivery model (using smarter machine),
it is important for HR to also use the pilot to study the change impact
analysis of the proposed solution. For
example, in the assisted decision making by manager, how will be the
accountability of the wrong decision is tracked and established? This may require some changes in the
policies, as well.
Perception management: Also managing the expectations of the
workforce on the efficacy of the new solution needs to be addressed as the SM
effective benefits may take some usage to get to its full potential. Here HR has to play its traditional role of
managing naysayers that are quick to dismiss any new disruptive solutions and
maintain the positive culture around the need to experiment and be open to new
ways of working. SM idea shall be hitting our enterprise shore, in some form or other, and we need to be partnering with its carriers/sponsors to ensure organisation is ready to embrace it without much pain,
Do you agree? Are there some other aspects of SM introduction that organisations should take note off?
Share your views and comments, as always
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