Industry 4.0: Disruptions mainly in data-driven industries


Termed the fourth industrial revolution, or Industry 4.0, AI implementation has become common in the last few years. According to a 2018 survey by Ethical Corporation, companies claiming to implement AI jumped from 38% in 2016 to 61% in 2017. Of these companies, 95% said they were interested in adopting AI solutions for insights into data while 50% say they want to use it for improving accuracy through automation. 

AI adoption is currently less a case of cyborgs everywhere and more data-driven. Machine learning is seen as the new oil. Indeed, AI-powered solutions allow for reduced inefficiencies in business models. For example, with AI implementation in the insurance industry (dubbed the Insurtech industry), premiums underwritten by artificial intelligence are predicted to reach $20 billion as soon as 2024 (currently around $1.3 billion in 2019). These large-scale advances have created what Diginomica referred to as the 2018 AI hype machine.

Timeline of AI in 2019

Source: PwC 2019 AI predictions


Beyond the hype: AI implementation in 2020


With AI appearing in our day to day lives, the focus is not on the if but the how of AI. However, AI adoption is made difficult by risks and uncertainties. For example, regulatory uncertainties recently increased with the GDPR. Business leaders are unsure which investments are most relevant for AI transformation which can be costly, often taking longer than predicted. Accordingly, PwC identified the Priority List for AI implementation. The list includes the necessity to make AI responsible, training employees and AI specialists to work together, cybersecurity, the convergence between AI and other tech, etc. Most importantly, AI experts including Microsoft recommend acting strategically to create the right, scaleable AI foundations.

In the immediate future, AI will reportedly be used in three main sectors: cost savings, new revenue, and customer experience improvements. For DXC, it is clear that the majority of large businesses will produce customer experience products centered around AI. Further, the analogy of the rich and famous is used to portray AI as “democratizing services, information and convenience” in a tangible way. For instance, online chatbots will become usably intelligent, and we would start trusting AI to do stuff for us. On the data side, Augmented Analytics would cause “the next wave of disruption” in 2020 as per Gartner’s Top Strategic Predictions, which could reduce business dependency on data scientists.


Students entering the workforce in 2020


Finally, another interesting prediction: students entering the workforce in 2020 will be less inclined to accept the status quo of repetitive manual work, which will create pressure for change and increased AI implementation. Perhaps we might even see AIs listed in employee directories in the next 12 months.


Written by Tanya Beck


AI in Business and Finance: The Latest Addition To Creative Destruction
Joseph Schumpeter’s theory of creative destruction was first thought up in the mid-20th century. It was a theory that looked at how the continuous creation of technology effectively ‘destroys’...
Ethical considerations for the use of AI in and around military conflicts
Seeking to build a virtual fence dividing North and South Vietnam, the US military forayed into the modern electronic battlefield in 1970. Amongst the tens of thousands of devices it dropped into...
The Black Box Problem: accountability in the age of the adolescent algorithm
As algorithms mature, existing legal models begin to show their age. Autonomous vehicles, or AVs, have become a topic of national conversation that generate equal parts optimism and pessimism about...
National AI Strategy: a race to global AI leadership?
Developing national AI strategy: the race to global AI leadership   In the last few years, countries have focused on national AI strategy in a race to both take advantage of AI and prepare for...
The role of AI in data analytics and consequences for its ethical impact
The need for data analytics Data analytics is the process of observing, organising and then understanding huge quantities of raw data. With the world as it is becoming ever increasingly reliant on...