The Asia-Pacific (APAC) region is now a hotbed for generative artificial intelligence (generative AI) and is expected to become a global leader in its adoption, with a projected economic impact of US$4.5 trillion by 2050, according to research by Accenture.
By 2026, more than 80% of enterprises are expected to use generative AI models or integrate AI-powered tools into their production environments. Additionally, many will adopt application programming interfaces (APIs) that enable generative AI capabilities. Countries like Singapore are positioning themselves as hubs for AI innovation. Business leaders in the region are now looking beyond simple generative AI add-ons for existing software.
Common uses of generative AI today involve text generation, such as customer-facing chatbots or tools for summarising content. But what’s the next step? When and how will generative AI transcend being a mere feature or chatbot?
The true potential of generative AI lies deep within the software and IT systems underpinning modern organisations, powering development on the other side of the screen. Microsoft’s GitHub Copilot, a code completion tool that predates ChatGPT, helped programmers write cleaner code faster. Fast forward to today, and we have “Devin,” the first fully autonomous software engineer developed by Cognition AI, which can execute complex engineering tasks, learn over time, and fix mistakes. IT professionals are already witnessing the transformative power of AI across areas beyond code development, including cybersecurity, system resilience, and information discovery, highlighting the technology’s immense potential.
AI will impact both customer engagement and enterprise work processes. A recent Elastic report surveying over 3,000 IT professionals from the US, Europe, and APJ found that over 50% of respondents strongly believed in generative AI’s potential to improve customer experience and engagement, while 57% saw internal opportunities for generative AI to boost operational efficiency and individual productivity. In both cases, the focus shifted from merely receiving search results, alerts, and notifications to obtaining precise answers to their challenges. For generative AI to drive meaningful change with data it wasn’t directly trained on, it needs contextual information. The key to this? Powerful search capabilities.
Gain access to the most insightful information to bridge knowledge gaps
Large language models, such as GPT-4 and Gemini, are data-hungry. They learn from vast public datasets, but their true potential lies in accessing an organisation’s internal data, such as customer interactions and product catalogues. When combined with search, these documents bring together the information and context needed to bridge the gap between public and private data, allowing generative AI to provide accurate responses to queries.
Imagine a customer calling for support. A generative AI tool would need more than general knowledge; it would need access to the customer’s call logs, order history, and other relevant internal data to provide precise, detailed answers. Robust search capabilities can feed generative AI with the necessary information, bypassing the time-consuming task of manually correlating context and general knowledge. This creates insights that transform customer support from impersonal searches into one-on-one conversations.
Generative AI is a force multiplier for cybersecurity
Beyond customer-facing applications, generative AI combined with search can also offer greater visibility into an organisation’s IT infrastructure. With multiple applications running on the cloud—or even across multiple cloud services — it’s crucial for IT teams to have data-driven insights at their fingertips to identify behavioural trends in their systems. In addition to helping them allocate resources for busier periods, these insights enable IT teams to anticipate abnormal behaviour caused by errors, outages, or even security threats.
By consolidating insights from an organisation’s entire IT stack, AI can help teams detect potential critical errors or pinpoint anomalous activity that could signal security threats. This capability enables IT teams to avoid worst-case scenarios and focus on innovation and skills development. The same Elastic survey found that over 50% of organisations worldwide plan to leverage AI for automated threat detection, and AI is well-positioned to fulfil this task.
Generative AI isn’t just about prompting a large language model to create new lyrics in the style of the Beatles, nor is it solely about responding quickly to customers or developers. Beyond chatbots and simple prompts, generative AI holds immense potential to improve productivity, customer engagement, and resilience.
