The struggle to stay relevant

It’s been a veritable bloodbath this year. Cisco is firing around 7% of their workforce and Microsoft , HP and Intel have also announced that they will be streamlining their workforce. In India, IT behemoths like Wipro, Infosys, IBM and others have also announced that they are looking to automate many roles and make middle and lower level employees redundant.

While there is a lot of outrage amongst employees about how it is unfair to remove jobs or fire people, they fail to understand that this is just part of the usual technological upgradation cycle. Be it the introduction of spinning jennies which disrupted the lives of cotton weavers, to industrial automation which led to the firing of thousands of factory and foundry workers, every technological innovation has an effect on employment.

To stay ahead of the bloodbath which will happen in the Indian IT and industrial sectors, it’s important we turn towards new skills, soft or technical. Students should move away from joining engineering and look at focusing at UI/UX and design courses. They could also look at understanding how the application of automation and artificial intelligence/ data science is going to change existing roles.

For example, if we look at the traditional role of a business analyst in many organisations, it requires understanding an industry and then analyzing and representing data to identify insights and drive business decisions. However this can also be accomplished by artificial intelligence and IBM Watson and Wipro Holmes are already attempting the same. So it’s essential we understand what are the drawbacks of AI / data science so we can build up expertise which will be relevant and complementary for organisations.

Another key area for India to focus should be hardware design and manufacturing. While the Make in India initiative has just started, it should be a priority to convince major semi conductor companies to start R&D and manufacturing units in India. States should look at not just providing land and tax benefits but also pair these companies with educational institutions ( IIMS / IITS/ IISERs) to increase the no of research publications from both students and researchers.


If we were to compare China and India, we lose not just in terms of access to organisations like Intel/ Samsung but also in terms of research conducted. Similarly if we are to take the lead in research in AI/ Data science/ Deep learning, it will be a great opportunity to create a rival ecosystem to Palo Alto, which will in turn drive investments and create opportunities for Indians, whether it be in terms of jobs or in terms of career growth.



Pissing in the pool

UberPool or OlaShare, aim to change the mentality of people using both personal and public transportation in India. By providing fares at 30-50% discount of their regular fares, which are themselves subsidised, they have become an attractive alternative for people in urban India.

However, I have heard complaints of how these carpooling initiatives often end up wasting time and money for people. Some stories speak of drivers waiting way beyond the stipulated time for passengers and often turning back to add an additional passenger. Others are disgusted about fellow riders and often feel uncomfortable having to travel with strangers.

The true challenge for both Uber and Ola lies in the sales incentive structure that has been built. Designed to enable both customers and drivers to push carpooling, it rewards drivers who take up car pooling by considering each passenger as a ride, and customers get a discounted fare. This results in drivers often going way out of their route to pick up additional car poolers , even if that inconveniences the existing passengers.

At the same time, passengers have not been informed of the risks of carpooling. They expect the service to be an extension of the current services and are dismayed by the difference in service quality. This can have a impact on future usage of carpooling and the cab service itself.

The answer lies in technology. While both Ola and Uber are using machine learning algorithms and data science to help improve efficiency, they need to improve the algorithms and build in features to avoid wastage of time and petrol. After all improving the time per ride allows drivers to pick up car poolers and build on their incentives.

Similarly carpooling firms need to incorporate customer satisfaction into the incentive mechanism. If drivers realise that an unsatisfied customer will also impact their sales incentives, it might help reduce some of the incidents. However customers also need to understand that if they do not turn up on time, they can lose the fare / can be penalised by not being allowed for future car pooling rides.

The challenge lies in marrying technology with the human touch. Understanding not just the patterns of rides but also the subtle tricks played by drivers and customers. That’s where Uber can potentially use the data of a Didi Mau while Ola still lacks access to the data and talent pool who can suitable make sense of such data.

While driverless cars are still some time away, I foresee a point where we may have automated shuttles with no drives plying on roads.At this time, rather than rebuilding Indian cities to accommodate new modes of public transportation, maybe it’s time we focussed on how options like carpooling or driverless shuttles may help reduce congestion, along with an approach of charging drivers of cars , a la Singapore.