If there is any section of life that disruptive innovation and artificial intelligence has engulfed greatly, it has to be the career world.
The advent of “regular” mobile phones completely shrunk the world into a city and then, smartphones showed up and changed the whole game. Laptops and the internet created a new work model, altering the 9-5 system and allowing people to work from the top of a cliff.
Fortunately, we have found ourselves in another readapting situation, where artificial intelligence is reshuffling the workforce and ultimately changing the way we work.
It has been estimated globally that as many as 375 million people may need to switch occupational categories and learn new skills by 2030. Based on several automated evidences, this is slowly becoming a reality; control will be placed in the hands of non-humans and a new category of jobs will be created for humans. What this shows is that there will always be a need for humans, however the nature of their work will evolve.
While some industries will be completely disrupted, others will undergo major changes. A few have been listed on our list of top six industries that will see the benefit of AI first.
The Popular Idea That Artificial Intelligence Will Someday Replace Every Human Task is Unrealistic.
While speaking at Talent Connect 2018, Rand Hindi, co-founder and CEO of Snips, mentioned that “Artificial intelligence is, quite simply, reproducing human behaviour in a machine.”
As a Data Scientist, he argues, the fear of being jobless due to robot-invasion is largely fuelled by a misunderstanding about what artificial intelligence actually is, and what it can or cannot do.
Rand went further to point out that by using techniques like machine learning, artificial intelligence can learn and make logical decisions based on the data it receives. This means that any task that’s associated with logic can be automated, but certain important tasks require emotional intelligence.
Emotional intelligence is important for solving paradoxes. “A paradox is something logic cannot solve,” says Rand. “The way that you solve this is by using your emotional intelligence to bypass what logic has failed to do.”
Humans are known to combine logic and emotional intelligence to solve a paradoxical problem. Machines, on the other hand, are limited to logic alone and will find it hard to make vital decisions.
Currently, it is more realistic to expect that automation in the workplace will bring us the best of both worlds, instead of putting them against each other.
Humans will never match up the IQ of Artificial Intelligence and machines will also never possess equal emotional capabilities with humans, it is, therefore, best to combine human intelligence and artificial intelligence.
How Data Science Adds Value To Business.
Although still in its early stages, data science has opened a new chapter in exploring the corners of what your product does and what it can do, that no one else has discovered.
Organizations, in order to capture the most value from the wealth of data, need to be deeply involved in a chain of technical capabilities. The process begins with identifying, capturing and storing data – proceeds to analyzing and visualizing data, which ends with a data scientist’s ability to implement valuable insights from the analytics.
In simple form, the supply chain is optimized in the best possible way – insights improve activities in the area of pricing, can predict when and where a company’s products sell best.
As an added advantage, data science ushers business owners into a whole new world of understanding customers on a personalized level.
The Forbes article of McKinsey’s 2016 Analytics offers an analysis of the potential of Machine Learning in improving the current state of Predictive Analytics. Forbes reports that the McKinsey study has identified 120 ML cases across 12 industry sectors and surveyed over 600 industry experts about the potential impact of ML in business analytics.
It turns out that business analytics is the number one area where data scientists are playing a key role.
As trained professionals, Data scientists can sift through a goldmine of data and identify risk or fraud. They can create statistical and big data methodologies for predictive fraud propensity models, using the results to let out an alert that can help prevent or mitigate damages.
To conclude, turning insights into action will improve the decision-making process of any company and if utilised accurately, data science is guaranteed to ultimately scale-up business profitability.