Generative AI creates new content like text, code, and images. But for businesses in Asia, it is only the key to scaling operations when this intelligence operates inside trusted workflows. By turning generative AI into accountable enterprise action, organisations can hyper-personalise customer experiences across a diverse, digital-first market while ensuring humans and agents work together securely.
By Silvio Savarese, Executive Vice President and Chief Scientist, Salesforce AI Research
Last updated: 11 May 2026
Editor’s Note: AI Cloud, Einstein GPT, and other cloud GPT products are now Einstein. For the latest news on Artificial Intelligence, go here.
Are you struggling to maintain a personal touch with your customers while scaling across different Asian markets and languages? The pressure to innovate is rising, but your team only has so many hours in the day. It feels like you are constantly playing catch-up with rapidly evolving digital expectations.
Generative AI is no longer just a futuristic concept; it is the engine powering the next wave of productivity for businesses from Singapore to Jakarta. It empowers your team to create, code, and communicate with unprecedented speed.
Generative artificial intelligence (AI) exploded on the scene in late 2022, sending people and businesses into a frenzy of curiosity and questions over its potential.
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.
“AI is only as good as the data you give it and you have to make sure that the datasets are representative.”
Paula Goldman, Salesforce Chief Ethical and Humane Use Officer
This guide explains how you can harness this technology to solve regional operational challenges. You will learn how to move beyond the hype and implement a strategy that delivers measurable results in the Asian business landscape.
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 résumés 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 amongst stakeholders and customers.
Digging deeper, it typically does this using one of two types of deep learning models: generative adversarial networks (GANs) or transformers.
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.
As recent Einstein GPT news from Salesforce highlights, 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.
More recently, 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.
Ketan Karkhanis, Salesforce's Executive Vice President and General Manager of Sales Cloud, said that while the technology may be a boon for large businesses, it's helpful for small- and medium-sized businesses (SMBs) too.
“Capabilities like automated, AI-generated proposals and customer communications, along with predictive sales modelling, will give SMBs even more powerful tools to help them provide great customer experiences, manage operating expenses, and achieve sustainable growth,” Karkhanis said.
Clara Shih, the CEO of Salesforce AI, believes that generative AI “will completely reshape the field of customer service.”
“With generative AI layered onto Agentforce for Service and Einstein 1, we'll have the ability to automatically generate personalised responses for agents to quickly email or message to customers … freeing human agents to spend more time deeply engaging on complex issues and building long-term customer relationships,” Shih said .
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 Agentforce 360 Platform. For example, Einstein AI technologies deliver more than 200 billion daily predictions across Einstein 1, helping businesses close deals faster, provide AI-powered human-like conversations for frequently asked questions, and better understand customer behaviour .
Recently, Salesforce announced Einstein GPT , the world’s first generative AI for CRM. From personalised sales emails to auto-generated code, Einstein GPT will deliver AI-created content across every sales, service, marketing, commerce, and IT interaction, at hyperscale. And, it’s built for customers in a way that’s relevant to them — Einstein GPT uses data from Data 360 combined with public data to create content across Einstein 1.
And, it will do so with the same foundation of inclusivity, responsibility, and sustainability at the core of any Salesforce product. Read about generative CRM and what it means for businesses.
Learn more about Einstein GPT and how it marks the next big milestone in Salesforce’s AI journey .
Editor’s Note: AI Cloud, Einstein GPT, and other cloud GPT products are now Salesforce Einstein. For the latest on Salesforce Einstein, go here.
Generative AI refers to artificial intelligence models capable of creating new, original content, such as text, images, audio, video, or code, that resembles content created by humans.
Generative AI models learn patterns, structures, and relationships from vast datasets during training, enabling them to generate novel outputs that align with the learnt characteristics of the data.
Common types include Large Language Models (LLMs) for text, Generative Adversarial Networks (GANs) for images, and diffusion models for image and video generation.
Applications include automating content creation (marketing copy, reports), generating personalised recommendations, designing new products, and accelerating software development.
It acts as a creative partner, providing new ideas, generating variations, and automating tedious parts of the creative process, allowing humans to focus on refinement and conceptualisation.
Ethical concerns include potential for misinformation (hallucinations), copyright issues for generated content, misuse for malicious purposes, and ensuring transparency about AI-generated material.
Prompt engineering is the art and science of crafting effective inputs (prompts) for Generative AI models to guide them towards producing desired, high-quality, and relevant outputs.
While generative AI is powerful for creating new content like text and code, businesses in Asia need it to execute reliably across complex, multi-lingual environments. Salesforce solves this by allowing generative and deterministic approaches to coexist within governed enterprise constraints. This ensures that when generating localised content, AI agents operate with explicit accountability and persistent shared context, elevating your local teams to drive regional success together.
To operate effectively across the diverse digital landscape of Asia, generative AI requires a unified, governed data foundation. Instead of assembling context task-by-task, Salesforce uses Data 360 to provide persistent shared understanding for both humans and agents. This preserves governance and gives AI agents the context required to act on generative insights reliably and accountably, avoiding hallucinations and ensuring trusted production.
