AI is likely to become one of the most important technologies of our era.
In this context, McKenzie’s report that analyses the potential impact of AI is a worthy consideration.
What is Artificial Intelligence (AI)?
Artificial Intelligence is an advanced stage of automation, where machines become capable of some form of decision making and cognitive functions.
By virtue of analytical techniques, some form of preliminary automation has been existent since the 1970s.
But performance of traditional analytics tends to plateau as the data set become considerably large, which was a major impediment.
Contrarily, the evolving “Machine Learning Techniques” perform better with larger data sets, and their data requirements are also more massive.
Machine learning methods are particularly valuable in extracting patterns from complex, unstructured data, including audio, speech, images and video.
However, if a threshold of data volume is not reached, robust AI that could add value to the traditional analytics techniques, can’t be built.
AI has the potential to play a major role in three important business functions namely - process automation, cognitive analytics, and people engagement.
How is AI development progressing?
Over the last few years, the necessary ingredients have come together to propel AI beyond research labs and into the marketplace.
Among them are – Powerful but inexpensive computer technologies; huge amounts of data; and advanced algorithms including machine learning.
Nonetheless, it is still early stages, and only leading-edge technology companies are presently in procession of advanced AI systems.
But considering the rapidity in the way AI is progressing, it is pertinent for us to ideate now on - AI’s economic potential, its limitations and challenges etc...
In this context, McKinsey recently published a paper on the marketplace potential of AI, which is a worthy read.
What does Mckinsey’s paper state?
The paper is focused on machine learning and based its study on more than 400 use-cases across 19 industries and 9 business functions.
Applications - Two-thirds of the opportunities to use AI are in improving the performance of existing analytical tools, and reducing human intervention.
This implies that, AI majorly being applied successfully to tasks that not long ago were viewed as the exclusive domain of humans.
Only 15% of the use cases studied by McKinsey are green-field cases, in which only machine learning techniques can be used.
In the remaining 15% of cases, machine learning provided limited additional performance over existing analytical methods.
Economics - It has been estimated that the potential value that AI would add to the global economy ranged between $3.5 trillion and $5.8 trillion annually.
This is about 40% of the overall value for all analytical techniques.
The most probable areas where AI’s potential could be reaped are retail, transport, logistics, and travel.
What is the way ahead?
In line with the current trend,companies are likely to adopt AI by incrementally leveraging and ramp up their existing analytics capabilities.
For this, they need to make sure that they have access to the necessary data for the envisioned up-gradation.
Such a pragmatic approach to getting on the AI learning curve is more sensible than attempting to tackle advanced, green-field AI problems.
Notably, the latter requires the kinds of skills and data that are generally only available to tech giants.
