Organizations across all industry sectors in Asia Pacific are facing challenges in translating GenAI innovations into practical applications.
According to Qlik and IDC’s latest report, The AI Pivot: Accelerating GenAI Adoption and Unlocking Data-Driven Business Value, nearly 35% of organizations cite poor-quality or poorly labeled datasets hindering their ability to scale GenAI initiatives.
DigiconAsia discussed some of the findings from the report with Dr Chris Marshall, Vice President, Data, Analytics, AI, Sustainability, and Industry Research, IDC, and Maurizio Garavello, Senior Vice President, Asia Pacific & Japan, Qlik.
AI-related investments in the region are projected to outpace overall digital technology spending, with GenAI expected to have an estimated regional economic impact of US$1.6 trillion by 2027. Who stands to gain and who stands to lose?
Dr Marshall: The surge in AI investments across the region presents both significant opportunities and challenges for organizations and sectors. Organizations that quickly embrace AI, particularly in data-rich industries with complex operational processes like finance, manufacturing, and retail, stand to gain through innovation, cost efficiencies, and enhanced customer experiences.
However, those slow to adopt AI or unable to adapt their business models with new technology risk being left behind. Sectors reliant on traditional, labor-intensive processes may find themselves at a disadvantage, while businesses that lack the necessary skills or fail to invest in upskilling their workforce may struggle to leverage AI’s full potential.
AI adoption is like a high-speed train. The doors are open now, but they won’t stay open for long. Companies that board early gain momentum while those still debating whether to get on risk watching opportunities accelerate away. Success hinges on the speed and effectiveness of integration into the core business strategy.
Your research finds that 80% of APAC organizations are rethinking data management as poor data quality, bias, and complex engineering cause one in five GenAI projects to fail. This aligns with another study reporting that 80% of APAC leaders agree their organizations are investing in GenAI projects at the expense of more valuable data and analytics initiatives. What should be done to address this misalignment of priorities?
Garavello: Firstly, businesses need to rethink how they approach AI investments. Companies rushing to embed AI just for a quick stock bump or a headline-grabbing announcement are missing the point. Those who succeed won’t be those with the flashiest AI news; they’ll be the ones quietly using AI to simplify processes, improve customer experiences, and drive actual growth. Anyone can plug in a trendy chatbot, but true successors reshape workflows and operations to ensure AI makes a difference every single day. If AI isn’t making a business smarter and faster, it’s just noise. This means shifting the focus from AI hype to real-world impact, ensuring that technology is implemented in ways that create measurable business value.
Secondly, AI’s effectiveness hinges on the quality of the data it’s built upon. Prioritize data quality by establishing robust frameworks that ensure data is accurate, unbiased, and well-structured. Without a strong data foundation, even the most advanced AI models will struggle to deliver meaningful results.
Lastly, an outcome-driven approach is key — businesses should focus on AI projects that generate tangible business or societal benefits. For example, initiatives that improve decision-making and drive efficiency such as predictive maintenance in manufacturing or automated risk assessment in financial services.
By shifting focus from hype to real-world applications, organizations can maximize the value of AI while ensuring data investments remain a top priority.
