"Success in creating AI
would be the biggest event in human history.
Unfortunately, it might also
be the last, unless we learn how to avoid the risks." - Stephen
Gone are the days of the familiar
status quo, warm and cosy, that has always been there, standing by us through
thick and thin. We are now living in an age of disruption wherein change is the
only constant. Disruption can be viewed as a radical change, necessitating an
adjustment of course or strategy. It is often caused by the launch of a new
product or service driven by innovative and efficient technologies. One such technology
is of Artificial Intelligence (AI) which may be the biggest disruptor society
has ever experienced. But it’s not just a disruptor - AI is also an accelerant
with the potential to enrich human learning, discovery, and productivity
personally and professionally.
AI refers to machine intelligence
or a machine’s ability to replicate the cognitive functions of a human being.
This is done based on the massive availability of data or big data. From
Amazon’s Alexa organizing its loud house party, to Google’s AI bots developing
a new language of their own to secretly communicate with each other, these are
all examples of machines using AI. Such machines have the ability to learn and
solve problems. In computer science, these machines are aptly called
“intelligent agents” or bots.
Over the years, the rise in the
application of artificial intelligence has deeply transformed the very meaning
of ideas, innovation, and inventions. To cope up with change, business models
are undergoing transformations. As we witness businesses across industries
undergo a profound and dramatic shift to upgrade their business models, AI
applications and adoption are offering each business entity as many new
opportunities as it does challenges and risks.
The introduction of AI has
compelled businesses to switch from a “pipeline” model to a “platform” based
business model that is asset-light, data-heavy and algorithmically driven. The
pipeline model involved the use of physical infrastructure and assets that help
create products and services, metaphorically pushing out these goods for
consumers. Platforms, meanwhile, are relatively intangible networks, which
derive their value by matching or connecting both buyers and sellers within a
multi-sided marketplace. Platform models are usually data intensive involving
the use of AI to develop algorithms for providing better customer services.
Application on AI has been most
prominent in the services industry. Several business stories can be considered
to better evaluate the application of AI in their business models. Netflix is
one of the companies that became a success story because of it. They used this
technology to gain granular knowledge of their subscriber base, before
harnessing the strong data base and the potential of machine learning development
to create a user-centric experience and go beyond recommendations with a broad
genre in mind.
Using data science optimisation,
Netflix analysed what type of TV shows a user likes the most. Thereafter, with
its machine learning development, Netflix looked even deeper and delved into
questions like how fast that user finished seasons of a certain series to
determine the degree of preference for a certain kind of genre or series.
Airbnb is another example that
greatly used AI to achieve their mission to change travel. AI is integral to
Airbnb's mission to change travel. The organisation does not use only one ML
algorithm but an ecosystem of algorithms to make the customer experience
better. For instance, AI is used in the ranking search. When the user is
searching for a place to stay on the platform one wouldn’t see listings in
alphabetical order. Instead, an algorithm is seeing the similarities in the
places you click on, how long you look at them and the places you look at in
the most depth. Then, it re-ranks search results to find the places where
Airbnb thinks you’re most likely to stay.
It is not only the services
industry but also the manufacturing and agriculture sector that is rapidly
evolving owing to the application of AI. The technology has become the driving
force behind a new era of mechanization where processes are being modernized to
make production decisions smarter in real time. AI is being used to support and
even change the role of the human workforce in the physical workplace. The most
dramatic impact of the technology is in bringing efficiency and simplicity to
manufacturing’s many complex processes such as machine-to-machine interactions
spanning products and assets, within factories and across global supply
networks. From conventional production line robotic equipment consisting of
one-bot-per-task systems, automation has now evolved to power and manage
multiple tasks simultaneously.
Agriculture or farming is one of
the oldest businesses in the world. AI can help farmers optimize their planning
and implementation techniques. They can now analyse a variety of things in real
time such as weather conditions, temperature, water usage from their farm to
take informed decisions. AI systems can also help in improving harvest quality
and accuracy -this is known as precision agriculture. It uses AI technology in
detecting diseases in plants, pests, and poor plant nutrition on farms. AI sensors can detect and target weeds and
then decide which herbicides to apply within the right buffer zone. This helps
to prevent over application of herbicides and excessive toxins that find their
way in our food.AI can also be used to create seasonal forecasting models to
improve agricultural accuracy and increase productivity.
It is evident that the application
of AI is becoming pervasive across all sectors. However, the increased use of
AI comes with several threats and challenges. One of the crucial problems is
the threat posed by AI in the jobs sector. Augmented use of ML is expected to
render several jobs redundant because it would become more efficient to get the
same task done by machines. This would introduce an urgent need for re-skilling
of the workforce with respect to the requirement of skills in the market.
The growing use of AI has also
made security a critical issue across nations. AI is currently an area where
almost no rules of engagement apply, and each country will face difficulties in
creating security from algorithms and defining international law. While algorithms
have no borders and no global regulations or regulatory body, data has its
origin, ownership and boundaries. However, interpretation of AI warfare remains
outside any legal obligation. In the absence of consensus on AI norms, every
business model and business is at risk.
It can therefore be inferred that
while rapidly automating businesses present promising opportunities, they also
present significant risks. To make the best use of this innovation, it is
essential to remain ready to face challenges and mitigate the posed risks.
About the Author :
is a final year student of Masters in Diplomacy, Law and Business at O.P Jindal
Global University, with a specialisation in Economics and Foreign Policy. She
is currently working as an intern at MitKat Advisory Services with the
information services department.