Jan 06 (The Hawk): Along with the introduction of OpenAI's ChatGPT and DALL-E Google's introduction of Studio Bot, Bard and Gemini with readily available APIs (Application Programming Interface) has created a frenzy among both small and big companies regarding AI. The effort is to integrate AI into their respective systems so that they can get maximum benefits with the help of technology.Can be achieved. These creative AI models are able to generate text, images, sounds and other content with extreme intelligence and have revolutionized areas that demand human intelligence and creativity. From translation to data analysis, report writing to software code creation and even creation of deep fakes, these models are capable of performing complex tasks in the blink of an eye. Does this mean that AI can also create markets for innovation? For your competitive and strategic edgeA. What effect will this have on companies that depend on innovation? Is AI sufficient and necessary to give companies a competitive and strategic edge, or is it just another layer in the information-technology infrastructure? AI models acquire their capabilities through intensive training based on extensive databases. Taking the example of Open AI, it uses the world's largest dataset i.e. the Internet, whose size is about 157 trillion gigabytes. The vastness of the Internet makes it rich and diverse with information and references.that reflect the complexities of the real world. Well-designed AI models trained on Internet data also learn to distinguish between valuable and irrelevant information. Available APIs such as those offered by ChatGPT and Gemini are the democratization of AI. These APIs serve as a user-friendly toolkit and enable the development of contextual and company-based applications on the ground floor. Thanks to this accessibility, artificial intelligence can be integrated with small and medium enterprises.could be achieved and the time and costs associated with AI implementation could be reduced. These implementations undoubtedly created better organizational efficiencies and helped reduce costs in these institutions. But will this provide a competitive edge? Conversely, it can be said that adopting a common underlying model such as ChatGPT or DALL-E can provide uniformity across companies as they can be trained on the same Internet dataset. This may be especially the case with small and medium enterprises where adequate data or any Models may be unavailable. Generative AI can commoditize features that may previously be considered distinctive and provide a competitive edge. For example enabling personalization or local language. When this type of AI becomes the standard for data analysis and reporting, such results can be achieved in the decision-making process as well. In fact, sentiment analysis conducted on social media could also yield similar results because the content of social media was publicly available. AI powered CyberSecurity products will undoubtedly strengthen the cyber security profile but given their availability to everyone, they may not make much of a difference to companies. As organizations move towards ubiquitous AI adoption, the real challenge will be to preserve uniqueness amidst uniformity. In such a situation, some traditional methods of gaining competitive edge may become weak, but despite this, generative AI has the potential to pave new paths. I believe that such competitive advantage There may be factors. The first factor would be the use of context-specific datasets. This will help organizations that have access to large volumes of data gain a strategic edge as these datasets will empower generative AI models, creating better output quality. For example, a car manufacturer armed with data can predict maintenance and gain an edge over competitors. Companies that do not have the data can use data from other producers provided the data is monetized.The framework justifies such transactions of data. A drug research and development company can tie up with many hospitals to collect unlimited patient data and get help in drug development. Such transfer of data creates the need for a strong economic-technological framework. There are also concerns over the economic interests and privacy of all stakeholders. The Government of India's Digital Personal Data Protection Act, 2023 is a step in this direction. The second factor is the ability to achieve creative results from generative AI models.May be associated. For this, efficient prompts will have to be prepared and practical questions will have to be prepared by taking the model. As doctoral students are told, formulating the right research question is half the work of writing a PhD thesis. The third factor for companies' competitive edge will be the power of computers. Companies will invest in advanced dedicated compute capabilities for AI. This trend can be seen as many large companies have adopted advanced tender processors. Has committed to spend billions of dollars for the purchase so that have dedicated AI computing capacity. This will help them train models more quickly and effectively. Along with this, they will also be able to experiment with different models. More computing power will speed up and allow training models with more parameters to be used. The fourth factor will be AI research and development. It is true that Generative AI has made significant progress but it is still not the case that machine capabilities can compete with human capabilities. This gap will reduce with time and companies moving in this direction Will also get a strategic edge over others. The current excitement around the adoption of generative AI resembles the early days of IT adoption. The widespread implementation of IT has led to a kind of standardization and commoditization which is reflected in IT components – hardware, software and networks. It is this commoditization that encouraged journalist Nicholas Carr to ask the question in an article for Harvard Business Review, 'Does IT matter?' He argued that IT had become a commodity and investment in its infrastructure would Competitive advantage is not gained. He later answered this question in a pamphlet titled 'It Does Matter'. Addressing the same question in the context of AI implementation, it appears that mere adoption of AI is not enough.
—Vijay Garg Retired Principal Educational Columnist Malout
