Ramesh (E): Thank you for having me, it's a pleasure to be here. Introducing AI into an organisation requires careful planning. The first step is to identify where AI can add real value, rather than using it just because it's trendy. Focus on tasks that could benefit from automation or better data analysis. It's also important to have the right infrastructure, good quality data, and skilled people in place. Additionally, creating a culture of innovation, gaining leadership support, and providing staff with proper training are key to successfully adopting AI.

Interviewer (I):  It seems like one of the biggest challenges when working with AI is ensuring the dataset is unbiased and representative. What steps should companies take to avoid biassed datasets?

Ramesh (E): That’s a critical issue in AI development. Ensuring that your dataset is representative requires diverse data collection from various sources, so all perspectives are included. Regular audits and bias checks should be built into the process, from the data collection phase to the final model deployment. It’s also essential to involve a diverse team in the development process to spot potential biases. Ultimately, transparency in how the data is gathered and curated, and being open about any limitations, will go a long way in minimising harmful biases.

Interviewer (I): Productivity is something everyone is looking to boost, and AI is often talked about as a solution. From what you’ve seen, how does AI improve productivity in the workplace?

Ramesh (E): AI and machine learning boost productivity by automating repetitive tasks, freeing teams to focus on more complex work. From data preprocessing to real-time analytics, AI speeds up processes and enhances decision-making. By streamlining tasks like model tuning and performance monitoring, AI/ML allows teams to focus on innovation and solving more challenging problems, ultimately driving productivity.

Interviewer (I): A lot of organisations are striving to improve operational efficiency, and AI seems to be a promising solution. How do you think adopting AI-driven decisions can actually help streamline operations?

Ramesh (E): AI is incredibly effective at streamlining operations by taking over repetitive, manual tasks that humans typically handle. For example, AI can automate data entry, forecast demand in supply chains, and even manage customer interactions through intelligent chatbots. The speed at which AI can process data and make decisions allows companies to save time, reduce costs, and minimise human error. The key is aligning AI implementation with your specific operational goals, ensuring it improves decision-making and process efficiency.

Interviewer (I): AI is evolving so fast, and with that, there seem to be a lot of misconceptions floating around, especially in the industry. Based on your experience, what are some common myths you’ve encountered about AI?

Ramesh (E): There are quite a few misconceptions. One big one is that AI will completely replace human workers, when in fact, it’s more about enhancing human abilities rather than replacing them. Another common myth is that AI can work without much data or that it can just be applied as a "plug-and-play" solution. In reality, AI needs quality data and continuous monitoring. Many people also believe that AI is infallible, but just like any system, it’s only as good as the data it’s trained on and the design behind it. AI and explainability is also an important and related subject. Given that many AI systems make decisions or recommendations that can be life-changing for those impacted, it is important that those decisions be explainable, interpretable, and easily communicated to those who are thus affected.

Interviewer (I): There’s always some anxiety around AI taking over jobs or becoming competition for humans. At what point, if ever, do you think AI becomes a competitor, and should we be concerned about it ?

Ramesh (E): That’s a valid concern for many, but I believe AI should be seen as a tool, not a competitor. AI excels at automating repetitive, data-heavy tasks, but there are many areas where human skills—like creativity, empathy, and complex problem-solving—are irreplaceable. Instead of worrying about AI taking over, we should focus on how to adapt and learn new skills that complement AI. That said, it’s true that certain jobs may be displaced, especially those that are highly repetitive. The key is staying adaptable and upskilling for future opportunities.Humans generate, design, develop, distribute, and monitor AI systems. Human decisions are impactful throughout the AI development life cycle, and those decisions, reflecting the developers’ values, impact the performance of AI systems in a significant way.

Interviewer (I): With all the advancements happening in AI, there’s a lot of talk about how legislation will have to adapt. Do you see any major changes coming in terms of laws around AI?

Ramesh (E): Absolutely. As AI becomes more integrated into society, legislation will need to catch up. I foresee more regulations around data privacy and algorithmic transparency to ensure AI systems are fair and ethical. Governments will likely require companies to disclose when AI is involved in decision-making processes, especially in sensitive areas like finance and employment. Additionally, there will likely be stricter rules to prevent harmful biases in AI and to hold companies accountable for how their AI systems impact individuals and society.

Interviewer (I): AI is making waves in almost every industry right now. Do you see any industries where particularly exciting work is happening with AI?

Ramesh (E): There are some fascinating advancements across multiple industries. In healthcare, AI is being used for early diagnosis and personalised treatment plans, which is really groundbreaking. Autonomous vehicles are also seeing rapid progress, especially with AI helping improve safety and navigation systems. Retail is using AI to enhance customer experience through personalised shopping and better inventory management. Financial institutions are leveraging AI for fraud detection and credit risk management. It’s exciting to see how AI is being used in both traditional and emerging industries to solve complex problems.

Interviewer (I): AI and machine learning are clearly transformative, boosting productivity by automating tasks and enabling teams to focus on innovation. It’s inspiring to hear how AI is enhancing decision-making and tackling complex challenges. Thank you for sharing your insights today; it’s been a pleasure speaking with you, and I’m sure our audience will value your perspective on AI’s role in shaping the future of work.

Ramesh chitor