The low adaptation levels of artificial intelligence have always been a roadblock to realizing its full potential and effectiveness, particularly in the business domain. Particularly, in the Indian business scenarios, low levels of enterprise digitization have resulted in the lack of quality datasets, which has been stated as a major reason for the low levels of AI adaptation. However, with the wave of the pandemic that rewrote rules and changed the rubrics of a multitude of fields, the adaptation gap is being reduced to a significant level helping to realize the full potential of artificial intelligence in business.
In comparison to major economies like the UK, US, and Japan, AI adoption has been the highest in India amidst the pandemic. By effectively blending AI and business, remarkable results can be achieved that can often prove to be game-changing.
Enterprise AI in India Today
At present in India, AI applications are limited to functions like sales, marketing, and customer support. Though a number of digitally efficient companies have automated their L1 support completely, like HDFC Bank’s chatbot EVA, it is highly unlikely that an AI system will be trusted enough by the bank to handle functions that are core to the operations. However, if implemented in an effective way, the results can be transformative.
Implementing AI: Things to Keep in Mind
There are three significant things to be kept in mind before implementing AI in business platforms for enhancing efficiency and ensuring optimization.
1) Define the unique set of AI use cases
Artificial intelligence is a technology that holds a wide spectrum of possibilities and immense scope. This apparent limitless feature can be tempting and businesses might feel like experimenting with it arbitrarily. However, this entails a threat of getting lost in a haze of possibilities and losing sight of the exact objective of the business. Therefore, in order to ensure that the AI application brings the maximum value to the business, it is important to be aware of the crucial areas of AI that can be useful and in sync with the business.
It is important to keep in mind that starting out with simple problems concerning business can prove to be more effective than using complex deep learning algorithms in the initial stage without being prudent about the amount of data the information systems are capable of generating.
2)Training and Validating the AI Model
Continuous feedback mechanisms can help the AI algorithms in learning faster and producing relevant outcomes. One example of this is human-in-the-loop(HITL) machine learning concepts wherein the decision accuracy of the AI is assessed by human experts, who can also provide guidance regarding the best outcome prediction. Real-time shadowing is also an effective technique to train the AI model.
3)Privacy and Ethical Responsibility
A major concern that comes in the wake of artificial intelligence applications despite their game-changing characteristics is the privacy and safety concerns. Though AI is capable of making powerful predictions, it can also erode data boundaries and intrude on user privacy. This underscores the necessity of implementing data anonymization tools so that the datasets are stripped off their PII. Timely updating of the privacy policies is also crucial to privacy protection.
If the gap concerning the shortage of talent is addressed through upskilling initiatives and prudent investments are made, artificial intelligence in business can prove to be an absolute game-changer.
