Technology is increasingly impacting our professional and personal lives. In this fast-paced economic environment, most client-centric businesses are stretching their technology bandwidth to achieve a higher return on investment. Adding new skills that are relevant to technology breakthroughs, machine learning and big data will change the game for professionals and organisations alike, while new technologies are opening up fresh avenues for students to grasp concepts through machine learning and big data.

By 2020, 85 per cent of customer interactions will be managed without a human (source: Gartner). Some 80 per cent of executives believe artificial intelligence improves worker performance and creates jobs (source: Narrative Science). Gartner and IDC sources estimate the number of connected devices will rise from 10.3 billion in 2014 to a likely high of 29.5 billion in 2020. Similarly, the value of connected devices will rise from $655.8 billion to an estimated $1.7 trillion.

Internet of Things, the trigger

This puts in perspective the phenomenon of ‘Internet of Things’ or IoT. IoT encompasses a number of disciplines that include embedded computing, middleware, big data, cloud, domain knowledge and business consulting. IoT is the trigger for the expansion of artificial intelligence or AI, and machine learning or ML. This article deals more with ML.

Machines, since the steam engine days, have in many ways reduced human misery and effort, while replacing tasks, jobs and roles. The assembly line ensured safer and better jobs for workers, while also increasing productivity. The underlying thought has been to deploy human capital, instead, towards more creative and cognitive challenges, than waste them on repetitive, mundane and sometimes risky tasks. The advent of mainframe computers, followed by PCs, which in turn has been followed by hand-held devices, have all followed this trend.

Different from other tech advances

The key differences between today’s ML environment and other technological advances are two-fold:

a. The need for extensive entry of data (inputs) by human beings has reduced, and will decline further. This has been made possible by imaging, sensors and mobile connectivity. Embedding a chip on machines enables them to work more efficiently and ‘talk’ to other machines, while generating valuable information

b. The penetration of the hand phone has assumed critical mass and has become smarter.

Now, smartphones can increasingly be connected to other objects/machines/systems, providing real-time information to the consumer /decision-maker.

As more devices evolve into the IoT ecosystem, the embedded chips in each of them can ‘talk to another’ chip on another object. This sets the stage for the machine to ‘learn on its own’. The machine is able to track the trends or changes in the information it is receiving in real time and comparing it with past information. This enables the machine or device to provide a more accurate output to the receiver human, who then uses the output to take decisions that are beneficial to all the stakeholders in the ecosystem.

The human factor

While ML could replace many roles/jobs that are currently structured in a particular way, it will also trigger the creation of newer jobs and roles. Finally, one must remember that it is the human being who is behind the architecture of IoT, AI and ML.

AI and ML are merely tools to enhance capabilities across functions, making them more analytical, creating strategic roles and enabling unbiased, logical decisions.

One of India’s leading private banks has deployed 200 software robots across 200 business processes to perform a fifth of internal jobs and a million transactions every day. The results of the same are reduction in the response time to customers by up to 60 per cent and increased accuracy up to 100 per cent. It has also enabled the bank’s employees to focus more on value-added and customer-related functions.

HR professionals too have realised the need for integration of technology with HR if they want to go beyond their traditional responsibilities and play a more proactive role in their organisations.

ML can empower HR to provide one-on-one and intelligent experiences at every step of the employee’s journey in the organisation. It will give them in-depth insights into employee expectations and make real-time recommendations on training needs.

Impact on education

On the education front, the future will see teachers using big data to collate and assess large volumes of unstructured numbers to adopt specific teaching strategies.

Technology will be a saviour for students and professionals, helping place greater emphasis on certain topics by focusing on individualised learning and responding to the needs of the students. The emphasis will be laid on making sure that concepts are understood at a deeper level by every student. It will increase inter-connectedness among classrooms far and wide across the globe and make learning a part of life outside class.

With the relentless speed of disruption in technology, there is a constant demand to innovate. There are certain skills that robots can’t replicate, such as creativity, emotional intelligence, critical thinking, adaptability and collaboration. And so, higher education institutions need to emphasise the imparting of these skills to students.

It is their duty, after all, to train the leaders of the future to cultivate and exploit creativity, and to learn collaborative activity, complex communication and the ability to thrive in diverse environments.

The article by Prof. Dr. Uday Salunkhe is Group Director, WeSchool and Vijayan Pankajaskhan is WeSchool’s Dean-HR & Industry Academia Interface, appeared on http://www.bloncampus.com. The views expressed are personal.