AI – Not Just Another Tech : HR Vista

Himanshu Thakur, IOCL

hthakur@indianoil.in

Author is a technology enthusiast who strongly believes that the true power of any technology is in its practical and business application. He has more than a decade of experience in implementing core technologies and 3+ years of experience in Human Resources. As a tech evangelist, Himanshu brings a different perspective towards bridging the gap between people and technology.

The current wave of AI is disrupting every government, organisation and individual. With the idea of understanding the present AI environment, this article delves into the brief parallels between technologies of the past and AI. The discussion touches upon various facets of AI and its adoption in organisations and workplaces.

Main Content

Sir Tim Berners-Lee invented the World Wide Web which changed the face of every industry and led to the rise of many. The Internet was the talk of the town in the mid-1990s, and if a business didn’t have a .com, they were old school. Every organisation was rushing to technology companies to put their profile on a webpage. Thanks again to Sir Lee, HTML and HTTP were making things possible. Tech companies were rising – everything on web.com to buy-this-or-that.com. The excitement about the Internet was huge among businesses, and the investments, as well as the stock prices of Internet companies, were skyrocketing. Conventional businesses were also trying to showcase their ability to leverage the internet and build and promote their websites. Though the dot-com crash in 2000 led to the closure and bankruptcy of many companies, Web 2.0 rejuvenated the internet with dynamic and interactive content. Social media and e-commerce disrupted the industry, and the leaders were again on the spot to make tough decisions.

Fast forward 20 years, a young American entrepreneur surprised the world with an interactive platform that has answers to anything or everything. ChatGPT was born. And suddenly, AI became a common lingo. Though AI is not a new buzzword, companies have been leveraging AI and Machine Learning (ML) capabilities to drive value for more than a decade. Netflix recommender system to OCR readers, ML algorithms have been improving and learning with time. Deep learning systems based on neural networks are conditioning the social behaviours of citizens and have been capitalised for major global events ranging from US elections to Brexit. Fortune 500 companies have also unleashed the potential of ML algorithms to build smart supply chains, improve operational efficiencies, enhance customer experience, etc. If AI-ML has been an intrinsic part of all conventional businesses for almost a decade, why have all the governments and businesses started showing concerns and excitement at the same time for AI? It is the Generative flavour of AI that answers almost all queries through its LLMs and generates content ranging from images to videos. The AI tech that was earlier implemented as a recommender system or a document reader project is now available in the hands of every person in the form of ChatGPT, and Dall-E (image generation.

AI – Not Just Another Tech

GenAI, Google Gemini etc. Every country or organisation is trying to put a leash on the usage and adoption of AI through new policies and guidelines. AI is mostly seen as a technology problem, rather, it is not only a technology but also a people and process problem. AI as an independent tech cannot solve business problems, it needs the support of the tacit knowledge of people and robust processes to solve business problems and generate value. AI implementation is not just about upgrading the existing Office tools or migrating data to the cloud. Modern AI systems are part and parcel of the business transactions at every stage. The previous disruptive technologies, such as cloud, robotics, and IoT were mostly adopted as standalone projects executed by the IT teams in collaboration with LOBs. However, AI as a technology requires the participation of all stakeholders from different verticals to ensure tangible results with the least trouble. In order to ensure seamless navigation towards AI adoption, business leaders and managers would have to address challenges related to data, skills and the AI ecosystem in the most meticulous way.

Data Challenge

Any AI system is as good as the underlying data and knowledge. For instance, an AI agent capable of answering queries related to policy matters would be able to handle user queries effectively if the domain knowledge is available in an organised and structured way. Any AI Assistant with limited domain knowledge would hallucinate and is extremely dangerous if adopted for mainline operations. AI, unlike previous disruptive technologies like cloud, and mobile apps, won’t just augment the existing business processes but rather transform the way humans interact with machines. The control of humans on IT systems would translate to co-existence i.e. both AI and Humans would be equally part of decision-making, with the former doing the bulk of the heavy lifting. Humans in the loop would be confident of AI decisions only if the underlying information is accurate and meaningful in the context. Additionally, AI feeds on information, new and novel to be precise. Any AI not trained continuously would be obsolete in no time. Hence, managers should ensure that continuous clean data pipelines are in place to ensure consistent growth and maturity of AI models.

Skill Challenge

AI skill is the prime topic of discussion in almost all forums, irrespective of domain or industry. HR across organisations are juggling the available skill repository and its relevance with the ongoing AI storm. “AI Generalists” is one acceptable solution to the present situation of firms. Every organisation can neither afford to build their dedicated AI teams nor skill their existing workforce to learn and implement every facet of AI technology. AI is more of a tool than a technology. Employees must be skilled and empowered in such a way that they can leverage the AI capabilities in their day-to-day tasks in a similar way to the collaborative tools/technologies such as Office, Slack etc. The workforce shall be trained to learn the nuances of capabilities such as Prompt Engineering, Data policies, Systems Thinking etc. Every skill or competency mentioned in a subject on its own, though these competencies shall be incorporated among the workforce to ensure that the organisation can embrace their AI journey.

AI – Not Just Another Tech

AI Ecosystem

GoI announced India’s AI mission in the 2025 Budget with a commitment of around Rs 10K crores (approx. 1.1 billion USD). Interestingly, DeepSeek (a Chinese Large Language Model) spent almost 1.3 billion USD on hardware and training its LLM. Major tech giants have committed budgets to the tune of 80 billion USD towards AI infrastructure. Clearly, India’s investment is not sufficient to build its own LLMs or similar Generative AI Models. Having said that, the organisation shall also avoid the AI trap of investments towards the development of AI models from scratch. Judicious usage of existing AI platforms and agents shall be promoted to derive maximum value from the present (or limited in most cases) AI budgets. This leads to the next two eminent problems – The choice of AI Agents and Data Security. AI agents are everywhere, from the most popular ChatGPT to niche ones like Elicit AI (for academic research). AI agent choice and data security go hand in hand. For instance, there is a website – There is an AI for That that can help users browse through the recent and available AI agents for different categories of tasks and activities. The privacy policies, terms of use, etc. shall be thoroughly read and understood before using these agents, and all efforts shall be made to ensure sensitive information is not uploaded to unknown sources. The organisations shall build processes to ensure that AI agents’ selection and usage are monitored judiciously in a way that not only empowers the employees but also ensures data privacy in line with the organisation and government guidelines and policies.

To conclude, AI would stay and would prosper and grow even at a more rapid pace. The AI race among tech giants and nations would get even more intense. Organisations may have to redefine their business models to stay relevant. There are still many queries that are unanswered. Since, at the end of the day, any AI agent is a black box and not even its creators can predict its next course of action. The leaders and managers would have to act mindfully to ride the AI roller coaster and approach without myopic lenses. In the end, the answer to all the burning questions can only be answered by the AIs of the present and the future.

And if you’re unsure where to start, maybe just ask ChatGPT1.

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