Deriving success from artificial technology is one such bug buzzing continuously in the mind of business leaders. The adoption of the technology across the industry is imminent and its increasing craze is becoming more and more interesting. The success of the company has become directly proportional to the scale at which AI has been employed in the business strategies and transformative ideas. Most of the companies and businesses contribute significantly to the economic growth of a nation which ultimately implies that AI adoption plays a vital role in determining nation competitiveness across the globe.

Talking about India, the revelation made by a study namely Artificial Intelligence Global Executive Study conducted by BCG and MIT Sloan Management Review can be best suited for the country. In the study involving 3,000 industry executives, it was found that 20 percent of the organizations have an adequate understanding of AI and have successfully adopted it to their system. These companies have been termed as 'pioneers' in these genres. Next, in line are 'investigators' who have a significant understanding of AI but fail to adopt it properly. Other clubs of organizations have been titled as 'experimenters' and 'passives' who have slender understanding or adoption of AI. Comparing the leading AI adoption pack among the nations, Chinese companies marking the lead have 32 percent of the companies who have adopted AI whereas the US, France, and Germany have 20 percent of the same.

Although active adoption of AI with Indian companies is less than the top rankers of the list, yet it is not categorized under passive in terms of the technology. Moreover, Indian is among those 20 countries which have standard national AI policy and stands among the top data-rich countries in the world due to significant government initiatives.

Setting the global benchmark, India has successfully encompassed the data and technical infrastructure in context to biometric identifiers (Aadhaar) and digital UPI payment capability. Not too much of disappointment, certain companies thriving in financial services, telecom market, and manufacturing industry can be termed as pioneers.

Loopholes in AI Adoption

•  Some organizations try to gesticulate their AI adoption ignoring any meaningful investment/commitment to AI technology.

•  The ultimate level of AI adoption should not be fantasized as the active participation of robots to greet and welcome visitors.

•  Many genuine adopters are focusing on opening labs and hiring data scientists and first priority and prefer to give a second thought to business use cases of AI.

•  These use cases are not considered at a scalable amount and often seen as an outlying part of the prevailing process.

•  A considerable amount of companies indulges themselves thoroughly with an extensive list of PoCs (Proof of Concepts) which are useful in the construction of excellent talks at AI forums but a few of them are actually meaningfully mounted.

•  Around 75 percent of companies' approach to this is actually useless as they fail to keep up to the scaled-up implementation post-PoC.

How to own successful adoption of AI?

•  In order to succeed in AI adoption, organizations need to embrace the golden rule of 10/20/70 which has been tried and tested by BCG GAMMA a number of times.

•  Around 10 percent of the process involves architecting the algorithm which is also very critical as the algorithm regulates the success of the initiative taken.

•  Another 20 percent of the adoption process involves the practice of algorithm and user interface development.

•  Rest of the 70 percent is comprised of structured support, assistance, and facilitation provided from the business organization's end.

•  This major proportion may also involve redesigning of the work process, designation of teams for maintenance and management of solutions and gauging the adoption rate and outcomes.

Implementation of AI Resources

•  Even after showing excitement for AI algorithm development, some companies simply fail to keep up with the deployment of resources for implementation.

•  The advantages of AI adoption will not be able to justify the implementation cost if business use cases remain peripheral to the process.

•  In the chase to new lustrous PoCs, the team loses the focused prioritization of implementation process.

•  Even after achieving a considerable bar of success, few companies actually dare to disrupt the prevailing work ethics and habits.

•  When companies try to adjust the new solutions to conventional process forcefully, the efficiency of AI-powered projects reduces and ultimate yield poor outcomes leading to disillusionment with AI.

Conclusion

The pioneers of the race have a better understanding in terms of understanding the true mission of AI in business transformation. They are more focused when it comes to leveraging AI and generate new revenue earning methods. It is important for the experienced management team to concentrate on high potential areas including personalization engine, pricing, and supply chain optimization. These areas are considered as the core of business which also aids the different phases of the process.

An unreliable frugal framework like 'wait and watch' tends to enhance the gap between early adopters and late ones. For rewarding adoption of AI, the companies need to be daring enough to have a big vision, take risks and earmark needful sources.