Artificial Intelligence Altering Business Dynamics


"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 Hawking


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 :

Sakshi Tokas 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.

 

Published On - Feb 25,2020

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