Insights

Embracing sustainable Gen AI for all times

Krishna Sudheendra CEO, UST

I spend a lot of my time on planes. Traveling and meeting with our clients regularly is part of my job description. These in-person meetings and conversations enrich our business relationships and enhance our understanding of client needs. Therefore, travel is an indispensable part of my life.

However, as a lover of nature and the environment, I worry that every trip I make adds to global warming. Many of you may be aware that the contrails created by aircraft contribute to our planet’s heat. I was deeply troubled to find out recently that nearly 32% of global warming is due to these contrails. So, how do we ensure business travel without harming the planet? What can I do to leave behind a world where my son and all future generations can continue to travel without harming the environment?

While reflecting on this dilemma, I chanced upon an article about an AI-assisted program developed by the Google research team, in partnership with American Airlines and Breakthrough Energy. The team used AI to predict altitudes that planes can avoid, to cut their contrail emission. Contrail avoidance would now become a solution to bring down emissions and reduce global warming, without affecting air travel. They collected huge amounts of data - like satellite imagery, weather and flight path data - and used AI to develop contrail forecast maps to see if pilots can choose routes that avoid creating contrails.

This is just one of AI's several heartwarming aspects–technology can make a difference and even save the planet. As a member of a technology company, I’ve always believed in the power of AI. And with more studies on how AI can transform the way we live, my belief is becoming stronger by the day.

With Generative AI, there are many meaningful ways to bring about change, create solutions, speed up processes, interactions, and so on. With many large language models (LLM) developments since the birth of Gen AI, the real-time application of this technology has leapfrogged. Whether healthcare, finance, or retail, practically every domain embraces what it offers.

Though this technology is still evolving, and many grey areas need to be demystified, it’s safe to assume that we stand to gain more than lose, with Gen AI. While it’s impossible to look at Gen AI's uses in this article, I would like to illustrate a few examples to urge more companies to research and adapt this technology.

As the name suggests, this branch of AI can generate content and therefore, one of the most common uses is copiloting interactions with external or internal customers - through chatbots. And I like to use the word “copiloting” because Gen AI is about enhancing, not replacing, human skills. Any time we need online customer interaction, we can have these copiloted interactions.

Creating personalized content for customers it generates responses to their inquiries, establishing a relationship right there. However, Gen AI is not just about these interactions.

Content generation is the most common use case for Gen AI. Product and executive summaries can be created using this technology, saving time and resources. But this is not to imply it will replace the human mind. For now, I’d like to think of Gen AI as something that enhances our skills and capabilities: a partnership that can revolutionize how we think and live.

As those of you in the software industry know, Gen AI influences software development as it reduces and fixes bugs in code. Additionally, it reduces the time taken to develop codes, bringing down the need for manual testing. It can also help in software quality assurance, especially in predictive maintenance. Gen AI can predict when the software components are likely to fail. It’s also applicable in RPA, customer service and knowledge management across industry verticals.

Of course, all this is commendable, but for me, one of the most inspiring use cases for Gen AI is in healthcare. While AI has aided many hospitals and payers for the last several years, Gen AI is still nascent among healthcare players. Physicians can provide better care when assisted by this technology. For instance, a simple conversation between a doctor and a patient can be converted into insightful clinician notes with the help of a Gen AI application. The doctor's time making sense of the conversation and developing notes can be better utilized in patient care. Similarly, documentation accuracy can be improved.

As Gen AI works well with large volumes of data, it can leverage structured and unstructured data to craft patient education videos, images, and summaries. As seen in some emerging research, healthcare providers can also use Gen AI in post-discharge care to help summarize discharge information, follow-up needs, connect the dots and produce specialist notes in case the patient needs referrals. Since personalization is one of the key characteristics of Gen AI, it can be used to create custom-made medicine and treatment plans for patients.

Equally fascinating is that Gen AI can be used in medical imaging analysis - understanding images such as CT scans and MRI; detecting and identifying patterns and anomalies. Imagine where this can take us in healthcare technology and improving the overall quality of human life.

Finance and retail also have various use cases and perhaps we would need more articles to discuss Gen AI adaptation comprehensively.

While discussing the potential and benefits of developing Gen AI and using it in our respective fields, we must be cognizant of the risks and pitfalls. Ethics become paramount. Since Gen AI plays around with a lot of data, protecting patient information in the healthcare sector becomes vital. Privacy becomes key. Accuracy is also an issue as the generated content may not be foolproof. There must be frameworks and laws to ensure the risks are covered.

Developing and training Gen AI models may also have an environmental impact. According to a McKinsey report, the development and training of foundation models may lead to detrimental social and environmental consequences, including increased carbon emissions (for example, training one large language model can emit about 315 tons of carbon dioxide).

Gen AI is here to stay. No doubt. And while I’m an ardent enthusiast and would like more organizations to benefit from its potential, we must use it responsibly. Technological advancement cannot happen at the expense of harming privacy, sustainability or the environment.

At the end of the day, we need a planet where we can advance technology and live a better life. What use would Gen AI be if there was no life to enhance it with?