Recommendations
Firms need to establish an AI-first way of working, focusing on data readiness, AI-led learning paths, and implementing a platform model
Responsible AI, including data veracity and privacy, is a concern for Asian countries, although Australia and New Zealand are less concerned about data usability. This is of course a priority for firms around the world as they get their AI systems ready for real-time production.
The enterprise data universe for AI has expanded beyond traditional analytical and transactional data to many other data types (such as user-generated content, synthetic data, machine data, ecosystem, third-party data). To get ahead, firms need to establish an effective data estate, ensuring all data assets are available, accessible, discoverable, and of high quality. In our AI research and client work, four successful data practices stand out:
Organize and identify data — Autonomously discover datasets, identify them for metadata, catalog for holistic understanding of AI initiatives, and ensure data is of high veracity.
Govern through better control — This includes access security; distribution and data rights management; synthetic data versioning; data monitoring; consent management and access control for user generated content; and governance to protect against privacy, legal, and IP concerns.
Regulatory compliance and zero-trust privacy — Continuously track regulations, as well as identify and deploy capabilities that lead to compliance.
Governed consumption — Define user groups to consume model output, and ensure models are current and perform effectively.
This data also needs to be connected, protected, and consumed: All of this is part of the responsible-by-design approach vital for best data practice.
Asian and Australian firms plan to upskill employees but will also recruit new talent and work with partners, similar to North America.
With this in mind, APAC needs to build AI-led learning paths that include both the creator community (data scientists, econometrists, machine learning engineers, etc.) and the consumer community (prompt engineers). These firms should also develop new AI-era roles such as experience designers, digital specialists, and platform engineers.
APAC companies should focus on automation. Human and AI system collaboration will free up significant resources to perform more fulfilling work and increase productivity. According to the annual State of AI report, using Github Copilot led to significant productivity gains for developers. In fact, less experienced users benefit the most, with a productivity gain of 32%.
Our research found that 56% of APAC firms are implementing generative AI or are creating business value, compared with just 46% in North America and 42% in Europe. One way to progress toward building business value is to offer access to self-service generative AI tools from a platform repository. This trending topic enables agile, product-centric teams to build and deploy generative AI applications without having to find or write complex code.
The suite of platforms available to assist includes:
Low-code, no-code tools — Offer visual interfaces and drag-and-drop functionality, allowing users with little to no coding experience to build and deploy simple AI models, like chatbots or data analysis tools.
Prebuilt models and templates — Provide pretrained models for common tasks like image recognition, text classification, or sentiment analysis. Users can adapt these to their needs without building them from scratch, reducing the technical barrier to entry.
Automated and guided workflows — Platforms can guide users through the AI development process step-by-step, offering automatic data preparation, model training, and deployment options. This simplifies the process and reduces the risk of errors for novice users.
Cloud-based infrastructure — Deploying AI models in the cloud removes the need for expensive hardware and software investments, making AI more affordable for individuals and smaller organizations. Additionally, cloud platforms offer easy scaling options, allowing users to adapt their AI resources as their needs evolve.
Collaborative features — Internal development platforms can facilitate collaboration between technical and nontechnical teams by providing secure access to data, models, and results. This enables knowledge sharing and fosters a culture of experimentation and innovation around AI within an organization.
AI-first firms should also build explainable AI tools into their platforms. These reveal how AI models reached their decisions and increase trust, a pillar of any AI-first strategy. This in turn enables agile AI product teams to quickly prove the veracity and thinking behind their work, enabling better decision-making across the whole firm.
Generative AI Radar: APAC
