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AI developers need to understand the science behind 'deep learning': Know-how?

AI puts the process of decision-making on a stronger footing by making it an evidence-based determination. Further, AI introduces an element of neutrality and objectivity and minimises the scope of biases creeping into that process.

Edited By: Saumya Nigam @snigam04 New Delhi Updated on: March 17, 2024 10:46 IST
Artificial intelligence, tech news, generativeai
Image Source : FILE AI developers need to understand the science behind 'deep learning': Know-how?

The advent of Artificial Intelligence (AI) has boosted the cause of business in many ways. It has speeded up the process of strategy formulation that derived new strength from a rapid analysis of comprehensive and relevant data which is related to the past and the present.

It has enabled businesses to deal with the new level of competitiveness that exists in today's world, based on an in-depth study of other players in the field as well as of the intricacies of the business environment.

Finally, AI is setting new benchmarks in human resource management in the matter of reconstituting teams to focus on creating new products and services and ensuring timelines of delivery.

In the Age of Information brought in by Information Technology, knowledge-based decision-making became the priority of all business enterprises and towards that objective, arrangements were made to collate and analyse a given set of parameters for defining the future vision of the business entity to be achieved within a timeframe.

The scope of Business Intelligence has been infinitely enlarged by AI which is a tool for quickly examining an unprecedented amount of raw data and collated facts, analysing all risk factors and opportunities and producing a set of reliable-looking predictions. The AI-assisted analytics covers a volume of data that was humanly impossible to handle in one go. This has pushed decision-making to a level of near perfection in a competitive environment.

The potential of AI in strategic decision-making, however, yields the best results when it is used for augmenting human Intelligence with data-driven insights and operational efficiency.

AI puts the process of decision-making on a stronger footing by making it an evidence-based determination. Further, AI introduces an element of neutrality and objectivity and minimises the scope of biases creeping into that process.

AI has opened up the world of start-ups and helped to provide a level playing field to those who can "read the future better" and while pursuing a line of business, find an opportunity that had gone undetected earlier.

Application of AI-aided examination of information available in the public domain leads to a good assessment of the competitor's behaviour as well as the industry dynamics.

Analysis of vast datasets through the application of AI can unearth patterns and trends that throw light on the modus operandi of a rival that was not visible to the human eye and which could be put to good use in a competitive setting.

Even while leveraging historical data, AI can read areas of success of a competitor and determine the scope for improvement there far more accurately thus creating a competitive advantage.

The more comprehensive the data, the better the outcome of the AI application.

It is possible to roll out new GenAI-based products and services to bring more value to investors and customers. AI-aided skills are extensively used now for profile writing, creating engaging headlines and understanding natural language for putting across the work being done by the business enterprise.

The use of AI in critical thinking, problem-solving and effective communication, is now well established.

Automated tasks are another developing area of corporate activity that is being put to good use.

As already mentioned, AI is proving indispensable for predictive analytics. Simulating market conditions and their probable outcomes accrued through advance scenario planning and risk assessment, is becoming a trendsetter.

In the arena of human resource development, work starts with the use of AI to improve the recruitment process itself. AI skills are needed in jobs requiring communication, analysis and sales promotion.

In content creation, it is found that more important than the time spent on writing is the time utilised for interactions and networking for the purpose of enhancing outreach and accessing new knowledge.

Upskilling is required since nearly half of the jobs in India are going to be affected by AI -- fortunately, professionals in India are already using AI skills more than what was the case anywhere else globally.

AI offers a new level of personalisation of customer needs, helps the process of development of new products and facilitates the adoption of entirely new ways of sales promotion. Business enterprises need manpower that is attuned to working with others, finding solutions to the problem at hand and using critical thinking.

The new skills are also put to good use in evolving work-life balance that AI-based tasks could demand. One of the top challenges is to prepare a GenAI-ready workforce. One impediment to this is the shortage of expertise in the emerging tech streams such as cloud computing.

There are limited programmes for GenAI skills in the university curricula. Business corporates have to think of building the capabilities of their workforce in terms of technical skills through a tailored training programme on GenAI. This programme has to be based on enterprise-specific or even team-specific goals.

Professionals across operations, marketing, finance etc require basic familiarisation with GenAI to decide how to effectively leverage GenAI tools to improve 'productivity'.

AI developers need to be nurtured to understand the science behind 'deep learning' and make the choice to pick the right models. Advanced teams may be needed to develop niche expertise in product engineering.

Even at the leadership level specific GenAI programmes have to be devised to enhance the trickle-down effect in creating motivation and ambition across the hierarchies. These are also needed for strategy formulation and possible policy amendments that could be required for better implementation.

Today, an understanding of Learning & Development (L&D) and Large Language Models (LLMs) and their importance in business is needed at the leadership level itself.

The main point of understanding and acceptance by the leadership is that AI's first impact on business was to enable the enterprise to reduce cost and increase the efficiency of its operations so that there was better ROI resulting from the value-add created by AI.

In a nutshell, AI primarily works through data and analytics to help companies to build new products and services and to enhance their customer base.

Machine learning automation is assisting human resource management in screening resumes and scheduling interviews. Screening enables the management to match applicants for positions requiring knowledge, experience and special skills for the job.

Targeted and personalised promotion campaigns are also helped by AI. A simple illustration is 'machine learning' that notes the difference between a customer who scans information for high-end eating places and then searches for clothes online and a customer who is only searching for clothes. In the first case, AI lays out fashion garments while in the other case, it would offer a range covering different categories.

Decision-making is one of the most important areas depending a great deal on AI because the latter can scrutinise large databases on customer preferences, text images and videos that are made for knowledge-based decisions- considered so important for standing against the competition. Supply chain management, security enhancement and customer experience data are among the basic advantages that AI-aided programs could provide.

Above all, India is quite aware of the promises and perils of AI and that is why it has asked tech firms to seek government approval before releasing under trial or unreliable AI tools and to caution the customers that the programme may not be able to answer every query of the user.

AI tools should be used with the basic understanding that they are governed by the input-output principle and that any predictive analytics provided by them rested on the detection of patterns and keywords in an unusually large database. Best results therefore are achieved when human intelligence works in conjunction with data-driven insights.

(The writer is former Director of the Intelligence Bureau. Views are personal)

Report by IANS

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