What lessons can we learn from the first wave of AI?

Artificial intelligence (AI) or cognitive technology is no longer about a machine playing chess. AI is on the streets driving our cars. It is in our call centres talking to customers. AI is drafting and reviewing legal documents with immaculate precision. It is even trading using indices derived from satellite imagery. Sprinkle some AI dust on anything, it seems, and it is reborn.

AI relies on analytical models and digital inputs in the form of vast and continuously flowing streams of data. The models quickly crunch the data and spew out insights with an uncanny human flavour. Some AI frameworks use deep learning and machine learning to create a loop of automated self-learning and unbroken evolution.

Organizations are realizing that AI can be harnessed to create powerful, real-time adaptive enterprises. In other words, we could have organizations that almost intuitively shift in response to the changing environment.

Embracing uncertainty

While it is easy to see that AI has groundbreaking potential, its real significance rests in the fact that organizations need no longer sweat over uncertainties. After centuries of trying to create stable organizations and economies, business leaders need not fear instability. With AI as an ally, they will come to embrace it. They will know that their digital ears, eyes, hands, legs and, we dare say, minds, will maximize each opportunity regardless of how unpredictable the environment may become.

All eyes on the US healthcare sector

A recent global study on AI, carried out by The Economist Intelligence Unit (EIU) on behalf of Wipro, tells us which industries are embracing AI first and why. It shows that the US healthcare sector leads the way with AI applications.

 Artificial Intelligence in the Real World: The Business Case Takes Shape

Artificial Intelligence in the Real World: The Business Case Takes Shape

Image: An Economist Intelligence Unit report sponsored by Wipro

Expressed as an index, the AI implementation score across organizations represented in the survey is 2.40 on a 1-5 scale (where 1=nascent, 2=exploratory, 3=experimental, 4=applied and 5=deployed). This means many organizations that participated in the study are just months away from active experimentation.

The index score is highest in North America (2.61), which reflects the fact that labs and universities in the US have, over the years, already invested in fundamental AI research.

“Ralf Herbrich, Amazon’s director of machine learning, believes the US lead is also partly due to its high excitement levels for consumer technology,” says the EIU report.

“However, he [Herbrich] notes that Europe has taken the lead in areas of AI such as natural language processing for multi-lingual settings. And at least one forecaster predicts that Asia-Pacific will experience faster growth of AI technologies than other regions between 2016 and 2020.”

While current AI initiatives are largely between the exploratory and experimental stages, self-improvement cycles on the back of machine learning will soon take over. Thereafter, we believe things will move at a dizzying pace. Today, if we want to look for early signals of where AI is headed, it is worth turning our attention towards health and life science, retail, manufacturing and financial services.

A diet of data – a precondition to AI

The health sector’s lead, the study points out, comes as no surprise to experts interviewed by the researchers. Jerry Kaplan, a visiting lecturer at Stanford University in California, and James Hendler, director of the Institute for Data Exploration and Applications at Rensselaer Polytechnic Institute in Troy, New York, believe that in the medium term healthcare provision holds brighter prospects for AI application than other fields. Kaplan said that AI’s potential in healthcare is predicated on the enormous volumes of patient data and medical research that the industry has accumulated: “The broad availability of data on outcomes, procedures, clinical trials, expenditures and other areas mean that the use of machine learning techniques can offer unique and useful insights.”

Health and life science, retail, manufacturing and financial services are then the industries that could witness the first wave of AI-driven hyper-transformation. Each one of those industries has one thing in common: they have a favourable history of generating and storing vast amounts of data – the essential building block for AI.

Businesses that do not have a robust data-centric culture to drive AI will be affected. Executives are deeply aware of this: as many as 44% of the executives polled for the report felt that delaying AI implementation will make their business vulnerable to new, disruptive tech start-ups. The only way to counter this is by making data acquisition and management a number one priority. Data is a precondition to deploying AI. Once a comprehensive and future-proof data strategy is in place, an organization can think of which component of AI to build, what to buy, who to partner and which of the existing applications be made more autonomous.

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