AI Adoption, Versatility And How Companies Can Confront Skill Gaps

Artificial intelligence has been making waves in the world of technology for the past few years. With its ability to transform and automate a wide range of tasks, AI has become an integral part of many industries. As industries embrace the power of AI and its importance, there is a growing demand for AI professionals who can help design, develop and implement AI systems.

As companies are considering how artificial intelligence (AI) applications can impact their businesses in many different ways, career opportunities are also growing in leaps and bounds.

Estimates predict that?AI will add $967 billion to the Indian economy by 2035 and $450 billion to $500 billion to India's GDP by 2025. AI is projected to account for 10% of India's $5 trillion GDP target.

The Role Of Data Scientist Has Garnered Attention Worldwide

Data is the lifeblood of businesses today. Both data science and data analysis are essential tools for understanding the world around us. Data helps organizations make better decisions, understand customer's needs, and track the progress and performance of any business. Additionally, adding data science can help you minimize or eradicate the risk of fraud and error, increase efficiency, and provide better customer service.

There is a huge gap in supply versus demand—how can organizations cope with onboarding data scientists?

There are 2 ways to confront this problem to an extent.

1. Retrain the existing tech employees. Organizations have to look inside and start to understand the advantages of retraining their staff to become data scientists. This process will enhance the credibility of employees to organizations and vise versa.

2. Cherry pick the interest levels of the existing nontech employees and reimburse their training cost with any recognized institute that can provide data science training. It's always a myth that only tech graduates can take up data science related jobs. Companies can be definitely successful by training the nontech forces also to bridge the gap and address the dearth of data scientists.

Core Components Of AI

Machine Learning (ML)

ML is important for any business because it provides a view of customer behavior and business operational patterns, and supports the development of new products.

Many leading enterprises are now making ML a central part of their businesses as it enhances business scalability and operations.

ML has versatile applications in various industries like banking, finance, travel, insurance, healthcare, e-commerce and more.

Natural Language Processing (NLP)

NLP is among the hottest topics of discussion now. Companies are putting tons of money into research in this field.

NLP's applications are in conversational AI virtual assistants like chatbots, voicebots and videobots.

Deep Learning (DL) And Generative AI

DL is a subset of ML, where algorithms are designed to simulate the human brain's neural networks. DL models are trained on vast amounts of data to recognize patterns and make predictions or decisions. It has several applications in image and speech recognition, NLP, autonomous vehicles and generative AI. Generative AI is a form of machine learning that involves creating AI models capable of generating new data, such as images, videos or even music. Some examples of deep learning with generative AI and NLP are LLMs (large language models) like ChatGPT, LaMDA, BERT, BharatGPT and more.

What Can Companies Do To Catch Up?

According to a report by the?World Economic Forum, by 2025, AI will create 97 million new job opportunities globally, and millions of roles are still vacant. Even though these are speculative figures, gaps in the industry have to be addressed on an immediate basis before the situation takes control of us.

Workforce Transformation By Enterprises?

Instead of looking for talent outside, a better approach for any company would be to look for resources internally. The idea is to transform the competencies of their current IT and functional ability into AI competencies.

Whether doing so in-house or hiring an external training agency to make the workforce AI-ready is a huge deal and one of the ways to bridge the AI gaps in the industry.

Freelancer Approach

Another big and smart move for companies is to welcome freelancers to work on their specific projects. The biggest advantage is, if companies cannot predict future business, they can hire skilled freelancers and work toward project completion. Companies can not only get the smartest and most skilled talents this way but can also save money by eliminating the concept of bench resources.