How Agentforce is Transforming Businesses in ASEAN with Sujith Abraham
Fresh out of the studio, Sujith Abraham, Senior Vice President and General Manager, ASEAN at Salesforce, joins us to explore how Agentforce 2.0 is transforming enterprise AI. He shares his journey from automotive engineering to tech leadership, reflecting on the rise of AI agents that move beyond chatbots to take real-world actions. Sujith explains how Agentforce 2.0 integrates across Salesforce products like Slack and Tableau, helping businesses automate sales, customer service, and marketing. He highlights how banks, airlines, and telcos in Southeast Asia are leveraging AI for growth, alongside Salesforce’s shift to a pay-as-you-go model for easier adoption. Addressing AI trust and governance, he underscores data privacy and enterprise-grade AI security. Closing the conversation, Sujith shares his vision for AI-driven customer engagement and what great looks like for Salesforce & Agentforce 2.0 in ASEAN.
"What's the point if it's a fast platform, but I still have to go somewhere else? And the last thing is speed. Right now, especially in our region, it's a land grab. When we think about some of the fastest-growing economies in the world—Indonesia, for example, Vietnam, the Philippines—you have hundreds of millions of people here. What we see in every customer I speak to is interest in how they use our platform to move faster, to deploy AI. They don't want to have to build a foundation level of AI, integrating all those elements themselves. They want to deploy it faster. When you think about our history, we have 250 petabytes of data being accessed by 150,000 customers every single day. When you take that set, we had to get this right. We had to because we have thousands of engineers focused on building these platforms so our customers don't have to—so they can deploy innovation and create truly unique and differentiated customer journeys." - Sujith Abraham
Profile: Sujith Abraham, Senior Vice President and General Manager, ASEAN from Salesforce (LinkedIn)
Here is the edited transcript of our conversation:
Bernard Leong: Welcome to Analyse Asia, the premier podcast dedicated to dissecting the pulse of business technology and media in Asia. I am Bernard Leong and agentic AI is about to sweep the globe. What is the future of AI agents? With me today, Sujith Abraham, Senior Vice President and General Manager, ASEAN from Salesforce to tell us about Agentforce 2.0 and what it means for the enterprise customers. Sujith, welcome to the show.
Sujith Abraham: Thank you for having me here, Bernard.
Bernard Leong: I saw Marc Benioff's keynote on Agentforce in the Dreamforce conference last year. It would be great for you to tell me: what is this context specifically to the Southeast Asia customers here? Before that, every time when we have a guest here, we want to hear their origin stories. So how did you start your career and eventually end up in Salesforce?
Sujith Abraham: Thank you for the question. Look, I didn't actually start off in tech. I went to an engineering school that was very tech-based, but I actually started off in the automotive industry.
And if you can believe it, I was designing seatbelts and airbags. The only thing is, I ended up in Michigan. I didn't really like the cold weather, I didn't really like engineering, and I was away from any ocean. Other than that, it was great. So I honestly got on a plane—I still remember it was 96 or 97. I decided to take a chance and went out to California. The industry was growing like crazy, and they were looking for people from the automotive sector. I ended up at Oracle when they were getting into enterprise applications, and it was just an incredible start.
They brought me to Singapore in 2004. I saw Salesforce from its humble beginnings back then and really admired them. We had been talking, but there was never really quite the right role. Then, strangely enough, in 2020, I was on a trip home to visit my parents from Singapore.
I've been in Singapore for almost 20 years now, but I was back home. It had been a while. I got a call from Salesforce, and it was a role that I wanted. They gave me the opportunity to run Southeast Asia, and the rest is history. So, I've been here [with Salesforce] for the past five years.
Bernard Leong: What are the lessons from your career journey that you can share with my audience?
Sujith Abraham: I think for me, what I can say is that it was just as important to figure out what I didn't want to do as it was to figure out what I did want to do. That process of elimination led me to realize that, as much as I valued the study and background in engineering, it wasn’t something I was excited about compared to high tech.
I took a chance. I knew I didn’t want to stay in engineering, but I wasn’t entirely sure about tech either. I made the leap, and I encourage everyone to take that risk. It has served me well, and there have been other moments in my life where taking risks has paid off.
Another key lesson I’ve learned is to follow your passion rather than the money. I’ve seen many people pursue careers solely for financial gain, and while everyone is different, I know that if I don’t enjoy what I’m doing, no amount of money can make up for it. For me, if you love what you do, everything else will fall into place.
Bernard Leong: That's great. It’s like the privilege of a lifetime is to be yourself.
Sujith Abraham: Yes, that’s right. It’s a gift.
Bernard Leong: You get a chance to do that. So I want to get to the main subject of the day. I want to talk about Agentforce 2.0 and Salesforce in Southeast Asia.
Maybe to start, can you introduce Salesforce, its global mission and vision, and its current footprint in Southeast Asia, or what we call ASEAN?
Sujith Abraham: Yeah, I can definitely do that. Salesforce's mission is to help our customers connect with their customers in a whole new way. Whether that’s through digital channels, messaging, the web, email, or even physical events, we aim to bring all these signals together and provide a 360-degree view of the customer.
We also want to democratize access to this data across sales, service, and marketing departments—all underpinned by AI, data, and CRM. This customer-centric approach is what drives Salesforce forward.
What's particularly exciting today is the third phase of AI evolution, which we call agentic services. We're leading this shift with autonomous AI agents under Agentforce, which operate as always-on virtual assistants that help both customers and employees enhance customer engagement and efficiency.
In Southeast Asia, our journey began in Singapore in 2004. I was watching Salesforce from afar at a competitor, and in 2020, when I joined, we quickly expanded. We launched our second entity in Thailand shortly after, followed by Indonesia in 2023. Around the same time, we introduced Hyperforce, our public cloud version of Salesforce, first in Singapore and then in Indonesia, enabling us to serve regulated industries and bring data closer to home. Our public cloud infrastructure operates on AWS.
Beyond that, Salesforce’s mission is rooted in the belief that business is the greatest platform for change. We are a values-led company, with trust as our number one value, followed by customer success, innovation, sustainability, and equality. These values define our culture, and any Salesforce employee can tell you how deeply they resonate with us.
We’re also proud of our team in Southeast Asia, which has grown into a community of over 1,000 employees. We are fortunate to work with leading enterprises such as Siam Commercial Bank, Singapore Airlines, Converge, Teccom Bank in Vietnam, and Indosat, spanning diverse industries and company sizes. As we continue expanding, we’re excited about the future of digital labor and AI-powered automation in the region.
Bernard Leong: Let’s first establish some key concepts to help my audience understand. How do you define generative AI and AI agents, and how do they intersect with enterprises, particularly in terms of applications?
Sujith Abraham: Sure. Maybe I can start by looking at some of the other forms of AI. Predictive AI analyzes historical data to predict patterns, which is where we began with our own AI back in 2014. Generative AI, on the other hand, takes data and creates something new—whether it’s images, text, or other content—using prompts. A familiar example is ChatGPT, which generates responses by connecting vast streams of data. You might also know Midjourney, which creates images, all powered by AI copilots that allow users to ask questions in natural language and receive responses or visual outputs through prompting.
That was a great start, and it moved us forward. But what was missing was the ability to drive action. It’s helpful to receive an answer, but what if I actually want to take the next step? What if I need to upgrade my plane ticket, get a referral from my healthcare provider, or finalize a loan application—without waiting for a human agent? That’s where AI agents and the Agentforce platform come in. They don’t just provide answers—they execute actions.
Bernard Leong: So essentially, Agentforce invokes actions based on prompts from large language models, turning AI-powered insights into real-world execution.
Sujith Abraham: Correct. Take Remarkable, the digital notepad company as an example, they implemented Agentforce in just three weeks and were suddenly able to manage thousands of customer conversations 24/7. Another great example is Heathrow Airport, where AI agents help guide passengers, provide flight updates, and connect them to airport vendors and merchants. We also have Wiley Books, which leveraged AI-driven case resolution, improving their productivity by 40%. Similarly, Saks Fifth Avenue, a luxury retailer, uses Agentforce to power hyper-personalized customer experiences at scale. These are just a few of the many real-world success stories where AI agents are driving action and enhancing efficiency.
Bernard Leong: Marc Benioff, Salesforce’s global CEO and founder, has been vocal about Agentforce’s importance, particularly during your recent Dreamforce 2024 conference. I’m currently teaching a class for government leaders, business executives, and engineers on how to build their own large language models, and Agentforce has come up in discussions. There’s a lot of curiosity around how to bring AI agents into real-world applications. Could you break down the key ideas behind Agentforce and how it works for businesses on the Salesforce platform?
Sujith Abraham: Sure, I’d be happy to. But let me ask you—how excited are you when you interact with a bot?
Bernard Leong: Well, I don’t mind talking to a bot, but if it can’t resolve my issue, I expect it to seamlessly route me to a human agent.
Sujith Abraham: Exactly, that's exactly it. The problem with traditional bots is that they can’t always answer the question. They're based on decision trees, which means they can only provide solutions if the exact query exists in their database. Most of the time, that’s not the case, which leads to a frustrating experience where users keep hitting the "please connect to an agent" button.
The difference with Agentforce is that it engages in natural language conversations. For years, application platforms have stored customer data, but we at Salesforce realized that not all customer data resides within Salesforce. It could be in a bank’s core banking system, an airline’s passenger booking system, or an enterprise's ERP. If we draw an analogy to a doctor’s visit, the power of Agentforce is similar to what a physician provides. A doctor brings comprehensive knowledge of your medical history, and in the same way, Agentforce unifies all customer data, regardless of where it is stored.
The key distinction is that we don’t copy the data—we unify and reference it. If your information is stored in Amazon, Databricks, or a traditional data warehouse, we don’t duplicate it. Instead, our co-pilot accesses and retrieves the necessary insights dynamically, ensuring efficiency with a zero-copy approach.
Beyond data unification, what makes Agentforce truly powerful is its reasoning engine, Atlas. It allows the system to differentiate between different types of queries—whether it’s about a ticket upgrade, a loan application, or an account issue. The system understands who the customer is, what they are eligible for, and what actions should be taken, ensuring more precise interactions.
Then, there’s the trust factor. Can you trust the response provided by the AI? How do we eliminate hallucinations or incorrect answers? More importantly, how do we ensure that when our AI constructs an answer using external large language models (LLMs), a company’s proprietary data isn’t exposed? That’s a critical part of how we’ve designed Agentforce.
Another key aspect is workflow integration. Employees don’t want to switch between multiple applications to find information. If a customer service agent or salesperson is using AI to assist in a conversation, they need it to be seamlessly embedded in their workflow. The goal is to keep everything within the flow of work, reducing friction and making interactions faster.
Speed is also essential. Right now, Southeast Asia is a land grab for digital transformation, with some of the fastest-growing economies like Indonesia, Vietnam, and the Philippines. Every business I speak to wants to deploy AI faster. They don’t want to build foundational AI models from scratch—they want to implement and see value immediately. When you consider that Salesforce handles 250 petabytes of data, accessed by 150,000 customers daily, we had to get this right. Thousands of engineers are working to ensure our customers can deploy AI-powered innovation and create unique customer experiences without the complexity.
Bernard Leong: I would assume that Agentforce also integrates across Salesforce’s suite of products, like Slack?
Sujith Abraham: That’s exactly right. Our AI is embedded across all Salesforce products. It powers Pulse in Tableau, Einstein in Slack, and our sales, service, and marketing agents. We have SDR [sales development representatives] coaching agents, sales agents, service agents, and commerce agents, allowing AI to be present wherever it’s needed in Salesforce’s ecosystem.
Bernard Leong: So if I were to think about it, how does Agentforce actually operate as an AI assistant? One of the key questions is around its different components. For example, in a customer service setting, an AI agent needs to pull from a knowledge base of FAQs, understand customer situations, and even conduct sentiment analysis to provide relevant responses.
Then in sales, SDR calls are completely different. Here, the AI is qualifying leads, prioritizing prospects, and assisting in sales conversations. Similarly, for marketing, the use case might focus on personalization and audience engagement. How are you able to configure these different agents under Agentforce?
Sujith Abraham: The key starting point is ensuring all your data is brought together in one place. This is why we developed Data Cloud, our platform that connects all sources of customer information. Good AI requires good, complete data, and Data Cloud sits at the heart of everything we do.
Data Cloud integrates with Salesforce, AWS, Databricks, Snowflake, ERP systems, and any other customer data sources. Whether your customer data sits in core banking systems, airline booking systems, or e-commerce platforms, we unify it into a single structured view. This includes product information, eligibility rules, and customer history, ensuring that when an agent interacts with a customer, it has all the relevant context.
On top of Data Cloud, we have our co-pilot layer and then the AI agent layer, which leverages our Atlas reasoning engine. This is what enables contextualized responses—it understands who the customer is, their past interactions, and what services they qualify for. These layers ensure responses are accurate, relevant, and tailored to each user’s specific needs.
Bernard Leong: You’ve made a great point about Data Cloud. Many businesses, especially CEOs and business leaders, overlook the importance of integrating data sources before implementing AI. McKinsey reported that only 20-30% of companies are actually data-ready for AI applications. Would you say that’s where Agentforce differentiates itself from other enterprise AI solutions?
Sujith Abraham: Exactly. Many companies attempt to build AI solutions from scratch, but this often leads to delays, complexity, and security concerns. It’s like starting an insurance business without expertise—you’re better off working with an experienced provider who has built a trusted, scalable system.
For enterprises, achieving a standardized level of security, governance, and trust takes significant effort. Our goal is to take the complexity out of AI adoption by providing a unified, trusted data infrastructure. When you use Salesforce, you connect your data sources in a secure and structured way, so you get reliable AI-generated insights quickly, rather than spending years building and validating models internally.
Bernard Leong: I used Salesforce in my corporate career at a tech company, and with Agentforce, I imagine that half of my workload would have been automated.
Sujith Abraham: Absolutely! Our goal is to work alongside users and help them become more productive, more efficient, and more impactful in their roles.
Bernard Leong: A lot of businesses are currently experimenting with AI, but they need to reach full-scale adoption. What are the common challenges companies face, and how does Agentforce help overcome them? I assume scalability, integration, and workflows are key issues, but you also mentioned trust as an important factor.
Sujith Abraham: Trust is absolutely critical. Minimizing AI hallucinations and ensuring accurate responses is a top priority. Take Air Canada’s recent AI mishap, for example—an AI-powered chatbot provided misleading information, which resulted in a lawsuit. This highlights why AI reliability and governance are essential.
In enterprise AI, the assumption is that answers should come from within the company's own data, rather than scraping information from the internet. Consumer AI models like ChatGPT are trained on vast public datasets, but in enterprise settings, you need a private, structured AI stack that surfaces information from verified internal sources.
That’s why Agentforce is built on a layered AI architecture. We have the Atlas reasoning engine, Data Cloud, and the agentic layer, all ensuring accurate, context-aware responses. External large language models (LLMs) like OpenAI’s GPT may be used for answer formulation, but we ensure that sensitive customer data never enters public models.
We also eliminate the need for companies to constantly test, fine-tune, and validate their AI systems. Instead of spending months checking if an AI model is accurate and trustworthy, businesses can simply deploy Agentforce’s pre-built, enterprise-grade AI and focus on building customer experiences rather than coding and maintaining complex AI stacks.
Bernard Leong: It sounds like thinking in terms of solutions rather than components is key. Businesses want a system they can just deploy and use, rather than spending time building their own AI infrastructure.
Sujith Abraham: Exactly. It’s about deployment, not development. Would you build a house from scratch, or hire a contractor who has already built thousands of homes?
Bernard Leong: I’d probably hire a contractor.
Sujith Abraham: Exactly! And that’s the same approach businesses should take with AI.
Bernard Leong: I may not be able to build it myself, but at least I know what I want.
Bernard Leong: So one interesting thing that excited me about Agentforce when it first came out was actually its business model. Traditionally, SaaS operates on a subscription-based pricing model, where customers pay based on users. But with Agentforce, the pricing structure is different—it’s pay-as-you-go, meaning you pay only for the work AI actually does. Can you explain how this new consumption-based model works and how customers are responding to it?
Sujith Abraham: You’re exactly right. We’re shifting to a consumption model, which means instead of paying a fixed monthly fee, customers pay only for what they use. With Agentforce, the pricing is based on conversations—each session lasts 24 hours, during which a user can ask multiple questions. Customers essentially pay per session, ensuring that they’re only charged based on actual usage.
Bernard Leong: Do you find that customers in this region are more open to this kind of business model?
Sujith Abraham: Absolutely. This isn’t a new concept—we’ve seen companies like Amazon successfully operate under a consumption-based model. Customers prefer this approach because it gives them more flexibility and cost control, allowing them to pay only for what they actually use, rather than committing to a fixed recurring fee.
Bernard Leong: I’ve adopted a similar pay-as-you-go model for my customers. It’s a big shift for Salesforce to enter this space. I’m curious—what are some interesting use cases for Agentforce in Southeast Asia, particularly in industries like logistics, finance, and retail?
Sujith Abraham: We’re currently working on multiple proof-of-concepts (POCs), and we’re excited to share them soon. In the banking sector, for example, we’re exploring how AI can transform wealth management by assisting wealth advisors in managing multiple clients more efficiently. In institutional banking, AI is helping streamline customer onboarding, allowing advisors to engage more effectively with their clients.
For airlines, consider what happened a few months ago when there was a major outage—customers were desperate for assistance but couldn’t reach an agent. With Agentforce, airlines can instantly deploy unlimited digital agents, ensuring every customer receives personalized support in real-time. Imagine the impact on customer satisfaction and brand loyalty if every airline could provide seamless assistance during crises.
We’re also seeing strong adoption across telcos, healthcare providers, government agencies, and educational institutions, all using AI-powered natural language interactions to improve customer and citizen engagement. Even broadband companies are leveraging AI to provide faster, more personalized service, helping them capture market share in this highly competitive region.
Bernard Leong: You’ve touched on governance and trust earlier. Let’s dive deeper—what safeguards does Salesforce have in place to ensure transparency and accountability when AI agents make decisions? How do you ensure that there’s auditability for businesses using Agentforce?
Sujith Abraham: Trust is our number one value, and we take AI governance very seriously. We’ve been extremely vocal about ensuring data privacy, accuracy, and security, and we’re committed to keeping customer data protected.
To prevent AI hallucinations and misinformation, we use synthetic data testing and continuously monitor AI performance. Our Office of Ethical and Humane AI sets strict guidelines to ensure responsible AI deployment. In fact, Salesforce’s first AI research center outside the U.S. is based here in Singapore, and it plays a key role in developing AI safeguards and filing patents that enhance our platform’s reliability.
We work closely with our customers, fine-tuning AI accuracy in real-world scenarios. For instance, at Heathrow Airport, our AI-powered customer service platform has achieved over 95% accuracy in handling passenger queries. This kind of continuous refinement ensures that Agentforce delivers reliable, high-quality AI interactions.
Bernard Leong: Many CEOs and business leaders ask me about AI adoption, particularly when implementing large language models (LLMs). How does Agentforce decide which foundational LLM to use?
Sujith Abraham: We use a hybrid approach. While customers can bring their own model, the majority of AI processing happens through Salesforce’s own AI models. Our Atlas reasoning engine is powered by XLAM models, designed specifically for enterprise-grade reasoning and decision-making.
We also have smaller, specialized models optimized for different functions—whether it’s sales, marketing, or customer service. While we do integrate external LLMs like OpenAI and Google, their role is primarily to structure and articulate responses. However, we never expose customer data to these external models, ensuring data security and compliance at all times.
Bernard Leong: Salesforce was one of the early pioneers in large language models, with the development of Einstein AI. Given your experience, what advice would you give to business leaders and government executives considering AI adoption?
Sujith Abraham: That’s a great question. First, focus on use cases—ask yourself, what problem are you solving? McKinsey found that 75% of AI’s business value lies in front-office operations, meaning AI should be customer-focused. Whether you’re enhancing customer service, sales, or marketing, defining a clear objective is critical.
Second, ensure your customer data is unified. AI isn’t just about the data in your CRM [Customer Relationship Management] —you need a system that can seamlessly connect and integrate multiple data sources. Without high-quality, connected data, AI will struggle to generate meaningful insights.
Third, prioritize trust and speed. Many companies attempt to build their own AI solutions, but this approach is often slow, expensive, and complex. Businesses are quickly realizing that DIY [Do it Yourself] AI projects don’t scale well, whereas Salesforce provides a ready-to-deploy, enterprise-grade AI solution that allows companies to implement AI faster and more effectively.
Right now, it’s a land grab for AI adoption, and the companies that deploy AI the fastest will gain a significant competitive advantage. Based on the number of POCs [proof of concepts] we’re running, it’s clear that speed matters more than ever.
Bernard Leong: So, what’s one thing about Salesforce and AgentForce in ASEAN that very few people know?
Sujith Abraham: One of the things we’re really proud of—especially given where we’re sitting—is that Salesforce’s first AI research center outside the U.S. was established in Singapore. It has since become a leading hub for AI innovation, filing more patents than any other department.
Our Singapore AI team plays a critical role in developing new AI-driven solutions and ensuring that Salesforce continues to lead in trust, governance, and cutting-edge AI applications. It’s an incredible team, and I’m lucky to have them here.
Bernard Leong: Wow. I wanted to follow up on that. How do you see Agentforce and Salesforce’s innovation center contributing to broader AI initiatives shaping the ASEAN ecosystem?
Sujith Abraham: One of the key aspects of Salesforce is our community. We have what we call Trailblazers—individuals and heroes within organizations who take our platform and build incredible things with it. They innovate in ways we couldn’t even imagine, and that’s why we see Salesforce as a platform rather than just a product.
Our developer community is also vital. They build intellectual property on top of our platform, creating new customer journeys that can be commercialized or made available to a wider audience. Similarly, our customers themselves take what we offer out of the box and customize it to suit their needs—whether it’s for an airline, a logistics company, or a bank—differentiating themselves with unique capabilities.
Governments are another important sector. For example, how can we enhance digital citizen experiences, such as accessing CPF services in Singapore? How do we bring similar innovations to other parts of the world? We’re fortunate to work with a forward-thinking government, and we’re excited to empower public sector groups to serve their citizens even better.
Bernard Leong: What is one question you wish more people would ask you about Salesforce or Agentforce?
Sujith Abraham: I wish people would ask, "Why Salesforce?"
Bernard Leong: I could just ask you that now, right?
Sujith Abraham: Yes, you can! And it’s a fair question. First and foremost, we’ve thought through these challenges deeply because we’ve had to. We manage 250 petabytes of data across 150,000 customers, and we had to get it right.
The ability to unify all that data and leverage customers’ existing investments in data warehouses or ERPs is a massive differentiator. Think of it like visiting a doctor—having a comprehensive and intelligent reasoning system, like our Atlas reasoning engine, enables better decision-making.
Trust is also foundational. Customers need to be confident in the information our AI delivers, ensuring their data and intellectual property don’t end up in the public domain.
Another key factor is workflow integration. Employees shouldn’t have to "swivel-chair" between applications. AI should be seamlessly embedded into their flow of work, enabling them to operate faster and more efficiently. One of our customers mentioned that onboarding new employees is a major challenge because they struggle to access knowledge quickly. Having an AI agent to guide them through complex processes reduces stress and improves employee retention.
And lastly, speed. We designed Agentforce with scalability in mind so that companies can deploy AI rapidly. As I mentioned before, it’s a land grab. High-growth economies and large enterprises in ASEAN recognize this, and they are prioritizing deployment over development.
Bernard Leong: My traditional closing question—what does great look like for Salesforce or Agentforce from your perspective?
Sujith Abraham: For us, greatness is seeing our products enable incredible new journeys for people—whether it’s citizens accessing government services, patients receiving better healthcare, travelers navigating an airport more smoothly, or customers having seamless banking experiences.
When we create AI-driven solutions that make everyday interactions faster, easier, and more accessible, we know we’re making a difference. Many people are busy and can’t always wait on hold for customer service after work. If we can improve our customers’ customer experiences, we feel like we’re truly driving change.
Bernard Leong: I’m looking forward to seeing that future. Sujith, thanks for coming on the show and for hosting me at your studio for this conversation. I have two quick closing questions. First, any recommendations that have inspired you recently?
Sujith Abraham: I’ll give you a non-technical one! I encourage everyone to experiment with AI, no matter what industry they’re in. I’ve been using Midjourney and Runway to create visual storytelling for our team.
We wanted to tell stories faster, so we’ve been building AI-generated imagery with Midjourney, then animating them with Runway. Years ago, this would have required hiring a studio and actors, but now we can create prototypes and take them to professionals for refinement. It’s an incredible way to democratize content creation.
Bernard Leong: That makes total sense. You can quickly generate a concept and then have experts polish it.
Sujith Abraham: Exactly. And this democratization of AI tools applies to many areas. Not everyone enjoys writing, but AI can help generate drafts. Need content in another language? AI can assist with translations. My advice is simple—go out and experiment!
Bernard Leong: I’ll add to that—I often use ChatGPT in combination with Gamma to generate presentation outlines. My students love it because it makes business school classes much more interactive.
So, how can my audience find you?
Sujith Abraham: LinkedIn is the best place. Feel free to reach out and connect with me there.
Bernard Leong: You can find us on YouTube, Spotify, and now even on video on Spotify. Let us know your thoughts on this episode, and most importantly, tell us who you are.
Sujith, many thanks for coming on the show. We should definitely do this again sometime.
Sujith Abraham: Absolutely, Bernard. Thanks for having me. I really appreciate it.
Podcast Information: Bernard Leong (@bernardleong, Linkedin) hosts and produces the show. Proper credits for the intro and end music: "Energetic Sports Drive" and the episode is mixed & edited in both video and audio format by G. Thomas Craig (@gthomascraig, LinkedIn). Here are the links to watch or listen to our podcast.