Disclaimer: The views expressed in this article are those of the author and do not necessarily reflect the views of the Economic Times – ET Edge Insights, its management, or its members

Business AI

Disruptive new-generation technologies are seen to have dramatically changed the way businesses run. Enterprises are considering realigning their strategies to retain their market position and gain advantages through cutting-edge technologies like artificial intelligence (AI), robotics, and IoT. Companies adopting the new-age exponential technologies earlier have a better chance of staying ahead of the curve and competition. With its potential to augment the capabilities of humans, and help businesses improve productivity, AI has the power to transform businesses across industries and sectors.


In the wake of the fourth industrial revolution, artificial intelligence and automation are the new norm for today’s enterprise. What once was a competitive edge is now becoming a prerequisite for business growth, efficiency, and productivity. It is no longer enough to just implement AI; it is about ensuring that AI is effectively integrated across all business platforms.

The focus needs to move away from what technologies are being offered and instead focus on how these technologies are impacting a company’s specific use-cases and enhancing their outcomes. The goal should be to build an AI-enabled organization and not look at AI as an add-on.

Organizations should look at building a robust AI strategy to imbibe AI in the way they operate. Forrester reports that 40 “insight-driven companies” are going to grab $1.8 trillion by 2021. In this list, we have young companies that are less than 8 years old.

What unifies them? Their obsession with data and AI. There are essentially two types of organizations with respect to AI adoption — first, the “talkers”: there are organizations wetting their feet with AI initiatives taking small risk-averse steps in organizational silos and in some cases getting tangled by bureaucracies; and a minority few, unfortunately, focusing more on press coverage than the actual outcome. Then the “Do-ers”: These are the insights-driven companies, that have integrated or are on a strong path to integrate analytics and AI into their organizational fabric. These organizations have a holistic approach to “AIenabled value chain”.


With AI, every organization will generally make some bad bets. Not only is acceptance of failure key, but failure in AI/ML isn’t binary. Sometimes pilots are technological successes but yield few benefits and vice versa. Building enterprise-scale AI is not easy and there are several obstacles teams face before putting the models in production. Some of the top reasons for failed PoCs include: Vendors failing to prove the concept as originally conceived Concept not delivering the expected outcome in terms of value Fails to satisfy the intended stakeholder’s Results do not add tangible value Enterprises have to perform a series of health checks before putting the models in production.


The role of AI in the near future would be to augment human potential and enable them to perform more strategic tasks.

Artificial intelligence is acting as an “invisible hand” in revolutionizing the healthcare sector. AI-based cancer radiology platforms are helping radiologists and surgeons to accurately and quickly diagnose and size the cancerous tissue to help plan personalized treatment. This is expected to not only dramatically improve the productivity of the small community of dedicated radiologists but also improve clinical outcomes of treatment and reduce patient mortality.

The biggest area where AI is seen making its presence felt is in automating previously manual and time-consuming processes that, while necessary, are a big drag on corporate bottom lines. For companies that have large supply chains with millions of orders or purchases to process, handling invoicing and procurement processes can be a significant drag. Increasingly, enterprises are putting AI in supply chain processes, using tools like computer vision to handle invoices and process automation tools to handle moving information across disparate systems.

These are all a few examples of how AI is already being used to augment our knowledge and our ability to seek and find answers.


The number of robots deployed by Indian firms has witnessed a 200-fold increase since the turn of the 21st century. Though with one of the lowest robot densities in the world, India may have less to worry at the moment compared to other economies.

However, considering the pace of change, India, and its professionals, as well as students, cannot afford to continue living in the 1991 era, where one degree and one-time education is going to last through their entire working career.

Smarter enterprises relentlessly innovate by investing in talent through various learning and development programs, embracing and encouraging diversity, and ensuring all employees are digitally savvy. It is important to recognize that the approach to talent management and retention cannot be the same as it was in the past. The nature of work is rapidly changing. These new digital technologies aren’t just disrupting markets and organizations, they’re creating new roles, augmenting existing ones, and rendering others redundant.

At the moment, India is facing a two forked problem — scores of graduates who need to be skilled in technologies that companies want to hire and millions of professionals with skill sets that are becoming redundant at a rapid pace who need to upskill.


Industry and academia have collaborated in AI research for decades. Most existing arrangements between industry and academia are either “acquihire,” to attract the brightest minds in academia to participate, or “acquire,” which effectively end collaborations by hiring these bright minds away from academia and thus cannibalizing the future pipeline to serve the needs of the present.

A new working model between industry and academia is needed, one in which stable, long-term industry-academic partnerships enable continued AI advancement while preserving our society’s capacity to conduct fundamental research and train future generations of AI experts.


We live in a time of exponential change. Everything we see today will soon be conventional knowledge. AI transformation is expected to be the most strategic subject to be tackled by organizations today. Successful transformations will ensure enterprises go beyond mere automation and cost-cutting strategies and unveil previously unseen business and revenue opportunities. Organizations will have to work towards putting humans in the loop — rethinking work architecture, retraining people, and rearranging the organization to leverage technology to transform into a smart business.


Disclaimer: The views expressed in this article are those of the author and do not necessarily reflect the views of the Economic Times – ET Edge Insights, its management, or its members

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