Every time a new technology knocks on our doors, it always comes with force, and as always, the concerns are on whether this new technology will make humans redundant. In this era of generative AI, similar concerns have surfaced, capturing the attention of the general public. But automation has been a part of our evolution for years, according to  Amit Chadha, chief executive and managing director at L&T Technologies Services (LTTS).

“I understand the concerns about AI and automation potentially replacing jobs, particularly in coding and other entry-level roles. However, it is important to note that automation has been a part of our evolution for years. We have seen transitions from fax machines to WhatsApp communications, the shift from manual to automated testing, and now the rise of AI-driven automation,” he told AIM.

In fact, the issue of machines replacing human labour has been discussed since at least Aristotle’s time. To be more specific, Chadha said that he doesn’t foresee automation completely eradicating entry-level coding positions. Instead, he believes it will reshape the nature of the work, and introduce new kinds of jobs. In this changing environment, coders proficient in generative AI will find themselves highly sought after. 

Reskilling is key 

As our economy becomes more digital, new roles will surface. While older roles may become redundant, they will be replaced by new ones with digital skill sets. The key lies in adaptability, upskilling and continuous learning. Currently, many organisations are splashing a good amount of money to upskill their workforce on generative AI, including the Indian IT giants like Wipro, TCS and Infosys. 

Chadha says L&T Technology also remains dedicated to upskilling its employees to navigate new technologies and roles. He believes upskilling of engineering talent will be a defining factor in remaining relevant in the global market. “L&T Technology has already taken a key step in this direction with its Global Engineering Academy (GEA).

“The academy is responsible for the onboarding training of all fresher hires across our locations and conducted over 450 sessions in the last fiscal. A first-of-its-kind initiative, the academy has helped retrain and enhance the skill sets of over 4,500 of our engineers to enhance our delivery capabilities across evolving global customer requirements, including at our Krakow, Poland centre.”

Leveraging generative AI 

L&T Technology has recognised the transformative potential of Large Language Models (LLMs) in today’s technological arena. “In fact, we were among the early adopters of models like BERT, RoBERTa, and ALBERT, trained on 350 million parameters, setting the benchmark in 2021. These models form the basis of our foundational starting point,” Chadha said.

Besides the models mentioned above, the Vadodara-headquartered firm is also leveraging newer models like OpenAI’s GPT series as well as LLaMA from Meta. While they are not looking to build an LLM from scratch, they remain dedicated to enhancing existing models to craft solutions that resonate with their clients and the sectors they cater to. “We channel significant resources into refining them to meet the distinct needs of various sectors, from healthcare to SDV and industrial equipment. For example, we’ve employed specialised models like DenseNet for tasks involving X-ray analysis.”

“Moreover, our commitment isn’t limited to our clients – we also utilise AI internally to further our own digital transformation across teams as diverse as HR, finance and operations. The in-house application of AI not only deepens our understanding of the technology but also equips us to tackle the challenges of deploying AI solutions for our clients,” Chadha added.

Challenges in deploying generative AI 

While organisations across multiple industries today are integrating generative AI to improve their offering, the technology also comes with its own set of challenges such as security risks and hallucinations. Chadha, while addressing these challenges, notes that one of the primary challenges L&T Technology has encountered while leveraging generative AI is data.

“Securing the right kind of data in the necessary volume often poses difficulties. In the absence of data from OEMs or clients, there’s a need to generate synthetic data, which might not always be production-ready. This remains an industry-wide challenge,” Chadha said. Moreover, the intricacy of the technology and its associated hardware is another concern for L&T Technology. 

“Even though hardware costs have decreased, its complexity persists, demanding specialised expertise. We have grappled with issues related to model optimisation, especially crucial when handling expansive models. Additionally, generative AI is most effective for intricate use-cases, which can be elusive within certain sectors. Identifying the right business scenarios that could benefit from AI necessitated extensive research, involving close collaboration with clients and domain experts.”

L&T’s AI endeavour began six years ago 

L&T Technology’s endeavour with AI began nearly six years ago. Over the years, the company has delved into various facets of AI, including Natural Language Processing (NLP), image processing, and ML. In the last few years, the company has crafted several AI-enabled platforms that are currently in active production. “We’ve crafted a number of AI-powered platforms, backed by close to 50 patents. These platforms cater to diverse departments and sectors, encompassing engineering, quality management, and regulatory assurance.”

One notable platform is AiKno, which centres on AI solutions for industrial applications, especially in the Oil & Gas sectors. “AiKno harnesses extensive models of NLP and has been effectively implemented over time, enhancing efficiencies, and addressing intricate challenges for our clients in these sectors.”

Another is an AI-driven quality assurance tool compliant with medical standards and an AI-facilitated annotation tool designed for Advanced Driver-Assistance Systems (ADAS). “These platforms have moved beyond the experimental phase, with tangible customer references and active implementations. Our internal adoption of AI has not only bolstered our R&D endeavours but has also been instrumental in addressing tangible business challenges,” he concluded.