“ When an AI model is suitably trained it acquires capability to analyze patterns, relationships and correlations in data with a compute prowess that far exceeds human capabilities to perform these tasks. The compute prowess acquired by AI through machine learning process enables the AI model to detect complex patterns and process big volumes of data. Human brains not equipped to handle this scale of complexity and volume fail to discern and extract the valuable granular information from big data..”

Table Of Content
1.	Introduction
2.	Impact of AI prowess in different sectors
3.	AI in Cyber Security
4.	The present state of AI Adoption in India
5.	The present state of Global AI Adoption
6.	How AI adoption impacts market dynamics

Introduction

When an AI model is suitably trained it acquires capability to analyze patterns, relationships and correlations in data with a compute prowess that far exceeds human capabilities to perform these tasks. The compute prowess acquired by AI through machine learning process enables the AI model to detect complex patterns and process big volumes of data. Human brains not equipped to handle this scale of complexity and volume fail to discern and extract the valuable granular information from big data.

Impact of AI prowess in different sectors

Given below are few specific situations where impact of successful AI adoption can be directly assessed:

• AI machine learning and natural language processing is being used vastly in the medical sectors that trains AI model on medical data, like diseases’ symptoms and medical histories, so as to make the model predict whether any patient has a certain disease.
• In the financial sector, AI bots trained on financial data can analyze huge data volumes within an instant. For example traders can run simulations with the help of AI and introduce random changes in past trading activities to see how the trading outcome can change, so as to be able to predict with high degree of accuracy what the outcome of a future financial event can be. The AI bots can automate trades and carry out trade on behalf of traders much more speedily and accurately than human traders.
• In the IT sector, AI can make an impact on code maintenance. Software engineers spend a large part of their working hours in manually updating old codes and libraries with latest versions, when AI is deployed to undertake the task of code maintenance then updation will be speedy and automatic.
• However, critics of AI have pointed out that if AI gets successfully adopted by cyber fraudsters then the security threats to digital economy would be immense and hence the regulatory bodies of AI domains are defining the protocols.

AI in Cyber security

Cyber security domain is becoming very complex and is being characterized by plethora of technology waves, increase in regulations and subsequently an imperative to adopt AI and automation. The domain is facing scarcity of IT talent as adoption of AI and automation requires more and more IT talent. In traditional setups man hours were deployed to focus on monitoring, if an attack was perceived from any geo location or IP then concerned human officer would have to manually block that port or IP. Under present situations AI and automation tools that can continuously conduct monitoring tirelessly and with precision needs to be adopted, however without dispensing of ultimate human responsibility.
In the cyber security domain AI and ML tools are being increasingly deployed. For one thing AI tools are much cheaper than man-hour-wages spent on certain types of jobs and primarily that matching speed and accuracy parameters of AI tools are way beyond human abilities. AI can analyze data received from several sources in moments and issue alerts whenever applicable. AI bots can be trained with machine learning algorithms to understand normal users’ behavior patterns and detect abnormal activity instantly and as well as categorize threats posed as per the seriousness so as to prioritize needed actions. Security orchestration automation and response (SOAR) software program stacks can collect data about security threats and respond to restore security with little or no human assistance, these software program stacks also automate routine tasks in cyber security centers like patching and vulnerability scanning. Mean time to detect (MTTD) and Mean time to remedy (MTTR) are now the Key Performance Indicators (KPIs) in the cyber security domains and the lower these values the better it is considered and reducing these values require tools like SOAR that can automate monotonous tasks up to 70 % to 80% so as to free the time of analysts for investigations.
Technology evolution like cloud and mobile apps has made several tools available for hackers also. Cyber security officer in charge can find it difficult to know whether a hacker or authorized person is using restricted network access layers, because hackers also have knowledge of similar tools that authorized IT personnel have. Due to the demand for increase in cyber security the cyber units now have to comply with several new compliance standards like PCI DSS, ISO/IEC 27001, HIPAA, GDPR and DevSecOps compliance frameworks and automation and AI makes the implementation of new age compliance standards feasible.

The present state of AI Adoption in India

In India successful adoption of AI may take years to happen, as per Industry analysis reports* around three out of four top companies from sectors of BFSI, Consumer Goods and Industrial Goods are unable to leverage AI to boost profits. Top companies of India would require to train and up skill their personnel for more than around 114 years to transfer AI skills so as to be able to leverage profits. In the forthcoming 3 to 5 years top 500 Indian Companies would need a minimum of 25k-30K Advanced AIML (Artificial Intelligence and Machine Learning) professionals. As per NASSCOM study the demand supply gap in digital technology skills will grow more than 3.5 times by 2026 to 1.4-1.8 million. Talent requirement in core AI like data scientist, data engineer and enterprise architect will be in a shortage by 15% to 20%.
As per experts successful adoption of AI by Indian sectors will be a major determinant of India’s global competitiveness and add up to 1.4% points p.a to the real GDP of India. It is expected that if the major Indian sectors are able to adopt AI successfully then they can add Rs. 1.5-2.5 trillion to their PBT (Profit Before Tax).

The present state of Global AI Adoption

As per the industry analysis reports*, in 2022 Global AI market reached the estimated figure of US $ 450 and growing at 20% rate. AI expenditure reached US $ 665 million in 2018 and is expected to reach US $ 11.78 billion by 2025.

How AI adoption impacts market dynamics

Companies that are adopting AI successfully are likely to report high profitability and earnings per share and hence these factors are likely to positively impact value of their shares. IT sectors mostly participate on global platforms and the successful adoption of AI by the top global companies can soon become a standard or imperative to adopt for top companies of different countries that are operating in open market economies, so as to remain competitive. If AI starts taking over the repetitive tasks that human perform then the challenge for human software professionals would be designing and training of AI models so that these can successfully handle the repetitive tasks with absolute precision. Hence the skills requirement for machine learning programming with high level programming languages like Java or Python is likely to increase.