How AI is disrupting Business Ecosystem?

How AI is disrupting Business Ecosystem?
Image credit: OnPassive

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Founder & CEO, ONPASSIVE
3 min read
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Though Artificial Intelligence is around for more than 50 years, Deep Blue, a chess-playing computer developed by IBM, defeating the reigning world champion gave the technology its much deserved attention. Two dozen years and billions of savings later, AI is as inevitable as ever to businesses that are in pursuit of profits. BFSI (Banking, Financial Services and Insurance) and Retail have reaped the early harvest as they foresaw AI as the new engine of growth sooner than many other sectors.

EARLY ADOPTION

Amazon is one of the early adopters of AI technology, started working on a Recommender System that understands and predicts user interest and behaviors, and makes recommendations based on these insights. By 2012, Amazon integrated its recommendation system across all stages of the purchasing process - product discovery to checkout. McKinsey & Company estimated that recommendation systems drive 35 percent of purchases at Amazon. Another early and heavy adopter of AI-powered, data-driven technology is IBM. Its CEO, Ginni Rometty, predicted that Cognitive Computing (Machine Learning powered speech recognition, sentiment analysis, face detection, risk assessment, and fraud detection etc.) would become a $2 trillion market by 2025.

APPLICATIONS

In a research conducted by the world-renowned Economist magazine’s Intelligence Unit in 2020, Predictive Analytics is the leading AI application adopted by many sectors, followed by Virtual Assistants (Chatbots etc), Image Analytics and Robotic Process Automation. Predictive and prescriptive analytics transform data and information into data-driven actionable insights that will fuel the growth engine in ways that are hardly imaginable few years ago. Streamlining the inventory management and supply-chains alone saved billions of dollars in inventory costs for retail giants like Walmart and Amazon. Telecom and other service providers are using customer-segmentation and churn analysis to provide better customer satisfaction. Data-driven digital marketing using targeted service/product marketing saw huge jumps in conversions that resulted in revenue growth.

MEASURES OF SUCCESS

Though measures of success for AI adoption and Return on Investment vary from sector to sector, reduced operational costs is the first and most common tangible benefit across industries and sectors. Higher customer/stakeholder satisfaction, detection and prevention of fraudulent transactions and ability to develop new products/services/markets are the long-term returns on the AI investments organizations are making today.

CHALLENGES

For many organizations, greater and deeper AI adoption means a paradigm-shift for its management and a culture-shift to its workforce. Unskilled and under-skilled employees feel threatened as automation involves decreased manual labor. This automation anxiety can be assuaged by upskilling the workforce to make ready for their new roles and responsibilities. Another hurdle for small and medium-sized organizations is the investment as AI-adoption demands big investments upfront. Cash-rich industry leader had a head start thereby widening the gap between them and their smaller competitors. Companies that are shackled with huge technological baggage and legacy systems had no option, but gradual AI adoption to absorb investment over a period.

CONCLUSION

With more than 25 billion IoT devices gathering data and more mobile phones than people on earth today, traditional businesses are forced to evolve in tune with the ever-changing technological landscape, making the AI adoption inevitable.

(The Author is the CEO & Founder of ONPASSIVE)

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