What is generative AI? The essential guide for businesses in ASEAN

Last update: March 4, 2026

Are you struggling to scale your customer support across multiple languages and time zones in ASEAN? You might feel like your team is stretched thin trying to maintain a personal touch in a rapidly growing digital landscape.

Generative AI is no longer just a buzzword; it is the engine helping businesses like yours transform complex data into meaningful customer connections. By automating the "heavy lifting" of content creation, you can focus on what truly matters: building trust with your local community.

But what exactly is generative AI? Put simply, generative AI is technology that takes a set of data and uses it to create something new – like poetry, a physics explainer, an email to a client, an image, or new music – when prompted by a human.

Unlike traditional AI models, generative AI “doesn’t just classify or predict, but creates content of its own […] and, it does so with a human-like command of language,” explained Salesforce Chief Scientist, Silvio Savarese .

Of course, the ability to classify and predict data accurately is a critical element to successful generative AI: The product is only as good as the data it has to work with.

In this article, you will learn how to move beyond the hype and implement a generative AI strategy that respects regional nuances. We’ll show you how to increase productivity while ensuring your brand remains authentic to the ASEAN spirit.

The rise of generative AI is not just a global phenomenon; it represents a significant economic opportunity for the ASEAN region. Projections estimate that AI could contribute up to $1 trillion to the region's GDP by 2030 . This growth is being driven by rapid adoption across various sectors, demonstrating the immense potential for businesses in Southeast Asia to leverage generative AI for innovation, efficiency, and competitive advantage.

There are several approaches to developing generative AI models, but one that is gaining significant traction is using pre-trained, large-language models (LLMs) to create novel content from text-based prompts. Generative AI is already helping people create everything from resumes and business plans to lines of code and digital art. But the technology’s potential at Salesforce and for enterprise businesses goes beyond making images of polar bears playing bass guitar .

The user gives the tool direction on what to produce, and then, based on the LLMs it has to work with, the AI generates something — be it words, code, or when thinking even bigger, things like novel proteins .

Eventually, Savarese predicts , these AI tools will “assist us in many parts of our lives, taking on the role of superpowered collaborators.”  For enterprises, it is especially important to include a human in the loop approach when developing and using generative AI technologies. By doing so, businesses can validate and test automated workflows with human oversight and intervention before unleashing fully autonomous systems. This can help prevent potential risks and ensure that the technology is being used in a responsible and ethical manner. Moreover, having a human in the loop can help build trust and confidence in the technology among stakeholders and customers.

Digging deeper, it typically does this using one of two types of deep learning models: generative adversarial networks (GANs) or transformers.

• GANs are made up of two neural networks: a generator and a discriminator. The two networks compete with each other, with the generator creating an output based on some input and the discriminator trying to determine if the output is real or fake. The generator then fine-tunes its output based on the discriminator’s feedback, and the cycle continues until it stumps the discriminator.
• Transformer models, like ChatGPT, (which stands for Chat Generative Pretrained Transformer), create outputs based on sequential data (like sentences or paragraphs) rather than individual data points. This approach helps the model efficiently process context and is why it’s used to generate or translate text.
• While GANS and transformers are among the most popular generative AI models, several other techniques are used as well, such as variational autoencoders (VAEs), which also rely on two neural networks to generate new data based on sample data, and neural radiance fields (NeRFs), which is being used to create 2D and 3D images.

Generative AI models like ChatGPT, StableDiffusion, and Midjourney have captured the imagination of business leaders around the world.

In fact, a new Salesforce survey found that two-thirds (67%) of IT leaders are prioritising generative AI for their business within the next 18 months, with one-third (33%) claiming it as a top priority. 

The technology is “open and extensible – supporting public and private AI models purpose-built for CRM – and trained on trusted, real-time data.”

Salesforce has been exploring how to develop and deploy generative AI to support customer needs for years. For example, the company introduced CodeGen, which democratises software engineering by helping users turn simple English prompts into executable code. Another project, LAVIS (short for LAnguage-VISion), helps make AI language-vision capabilities accessible to a wide audience of researchers and practitioners.

Salesforce’s ProGen project revealed that by creating language models based around amino acids instead of letters and words, generative AI was able to produce proteins that have not been found in nature, and in many cases, are more functional. With further research, the idea is that these proteins can be used to develop medicines, vaccines, and treatments for diseases.

While the potential of generative AI is enormous, it “is not without risks,” according to Paula Goldman, Salesforce Chief Ethical and Humane Use Officer and Kathy Baxter, Principal Architect for Salesforce’s Ethical AI practice.

In a coauthored article, the pair pointed out that it’s “not enough to deliver the technological capabilities of generative AI. We must prioritise responsible innovation to help guide how this transformative technology can and should be used — and ensure that our employees, partners, and customers have the tools they need to develop and use these technologies safely, accurately, and ethically.”

In an interview with Silicon , Goldman shared, “Accuracy is the most important thing when applying AI in a business context because you have to make sure that if the AI is making a recommendation for a prompt, for a customer chat or a sales-focused email, that it’s not making up facts.” Ensuring data is accurate and trustworthy is foundational to any AI application.

The authoritative feel of ChatGPT responses is itself something to be mindful of, said Savarese, who warned it could lead to what he deems “confident failure.”

“The poised, often professional tone these models exude when answering questions and fulfilling prompts make their hits all the more impressive, but it makes their misses downright dangerous,” Saverese said . “Even experts are routinely caught off guard by their powers of persuasion.”

Scale the reliance on tools like ChatGPT up to the enterprise level and it’s easy to see how high the stakes could get. But IT leaders are on guard: Nearly six in 10 (59%) said they think generative AI outputs are inaccurate.

Then there’s the question of how to use generative AI ethically, inclusively, and responsibly.

That’s why Salesforce is building trusted AI capabilities with embedded guardrails and guidance to help catch potential problems before they happen. If the world is going to realise the potential of generative AI, it will need good reasons to trust these models at every level.

Responsible AI also means sustainable AI. AI consumes significantly more power than traditional workloads and 71% of IT leaders agree generative AI would increase their carbon footprint through increased IT energy use.

Despite the need to explore generative AI inclusively and with intention, the technology holds vast potential for the future of CRM.

AI has long been integral to the Salesforce Platform. AI has long been integral to the Salesforce Platform. Today, we are moving beyond simple assistants and entering the third wave of AI—autonomous agents. With Agentforce, CRM evolves from a system of record into a system of action, featuring autonomous agents that can independently take action across every sales, service, marketing, commerce, and IT interaction.

For CRM, this means a shift from reactive tools to proactive digital labour. Agentforce is the only enterprise agentic AI solution that elevates every experience by bringing together humans, applications, AI agents, and data. Now any company can safely deploy agents that work for their customers, suppliers, and employees 24/7. Whether it is qualifying a lead in Agentforce Sales or resolving a complex case in Agentforce Service, these agents do not just suggest a response—they execute the necessary business processes autonomously.

The power of this new era lies in its precision. Teams can manage the complete agent development lifecycle with a robust set of tools to build, test, deploy, manage, and orchestrate AI agents at scale. By grounding these autonomous agents in data from Data 360 combined with public data, Agentforce ensures every output is relevant, trusted, and deeply integrated into existing business workflows. This ensures that generative AI is no longer a generic content creator, but a specialised expert tailored to your specific business needs.

Read about Agentforce and what it means for the future of business.