Business AI strategy ‘must focus on strategic implementation’

Businesses navigating the complexities of Artificial Intelligence (AI) should shift from experimental AI projects to strategic implementations focused on delivering measurable business outcomes, a top Amazon Web Services (AWS) executive said.

He emphasised that the technology's true potential lies in its ability to drive tangible value and transform operations.

In a keynote address at Krunthep Turakij's “AI Revolution 2025” seminar on Thursday, Joel Garcia, AWS Head of Technology for ASEAN, said that while generative AI is transforming how we live, work, and connect, companies must not get caught up in the "fear of missing out" (FOMO) and the rapid pace of technological change.  

He highlighted a shift from focusing on Proof of Concepts (POCs) to demonstrating Proof of Value (POV), with businesses increasingly seeking a return on investment (ROI) from their AI initiatives.  

“It's not going to be led by technology. It should be led by the business,” Garcia stated, emphasising the importance of aligning AI projects with key performance indicators (KPIs) such as customer lifetime value, productivity gains (eg, reduced time to market, average handling time in contact centers), and enhanced decision-making.

Garcia illustrated this with examples, citing how Amazon Q, AWS's generative AI assistant, had saved the company an estimated 4,500 man-years and $260 million in Java upgrades.  
 

He highlighted AWS’s work with Krungsri Bank in Thailand to drive financial inclusion through a comprehensive data platform providing 360-degree customer views and optimised ATM cash delivery, and how e-commerce companies are using AI-powered insight tools to analyse vast datasets and improve customer experience.    

Looking ahead, Garcia identified key trends shaping the future of AI:

The rise of reasoning models and Chain of Thought: The significance of AI's ability to mimic human cognitive functions by reasoning through problems before providing a response, will enhance the depth and quality of AI-driven insights.

The shift from AI as a tool to AI as a team member through the development of AI agents: These autonomous entities will be able to proactively engage with users, understand context, and perform tasks, further integrating AI into everyday workflows.

The importance of multi-modality: AI systems will be able to process and understand various data types beyond text, including audio, video, and tabular data, enabling more comprehensive and contextually rich AI applications.

The need for model choice: Recognising that different AI models excel at different tasks, businesses will need access to a variety of models to address complex challenges effectively.

Garcia stressed that while AI offers immense potential, a strong data foundation remains critical for successful generative AI implementation.  

He also highlighted the importance of people, process, and technology, adding that new skills and roles will be crucial in the AI-driven future.

Garcia reiterated that generative AI is a powerful innovation accelerator, but its adoption must be strategic, focusing on delivering business value and impact. 

He emphasised that data will continue to be a key differentiator, and a transformation-focused mindset is essential for organisations to thrive in the age of AI.