Organizations in the Asia Pacific region need three predictions to come true and rein-in the rapidly developing empowerment technology
Now that most of us have moved past the experimental phase for AI, we see businesses increasingly unlocking its value with this transformational technology. But with more entrants in the enterprise AI landscape, the challenge lies in creating true competitive differentiation, rather than just keeping up with the status quo.
So how can enterprises prepare for what is coming next? As AI expands beyond simple automation into more sophisticated applications such as predictive analytics, content generation and even decision-making, it has become essential for enterprises across industries to keep pace.
At the heart of these advancements, data will be the backbone of meaningful, reliable insights. We will see more organizations place greater attention on data provenance, where they have an overview of the origin, integrity, and authenticity of their data, growing deeper trust in an increasingly AI-driven world.
AI trends predictions
As we step into 2025, such interconnected trends will shape a more inclusive AI landscape, setting the stage for a future where businesses of all sizes can unlock the full potential of data and technology. Here are our predictions…
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The rise of open source in GenAI development
Since last year, GitHub’s data indicated a 98% surge in the number of open source gen AI projects in India, Japan, and Singapore. This reflects the importance of collaboration and accessibility when it comes to new technologies such as AI, and we are likely to see GenAI activity increase globally.
Open source AI platforms and tools, as well as open source-licensed models, are democratizing innovation by ensuring that its benefits, such as versatile frameworks and tools, are no longer confined to a select few. By making these benefits accessible to organizations of all sizes, the playing field is leveled, allowing even smaller enterprises to discover open source and innovate on a global scale.
Open source solutions also offer flexibility in navigating constraints such as cost, data sovereignty, and skill gaps. With a collaborative open source community, enterprises can tailor these solutions to their specific needs while retaining control over sensitive data. Moreover, with greater collaborations, vulnerabilities can be identified and addressed quicker, leading to the fostering of greater trust in AI-driven outcomes.
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Hybrid cloud could become the default priority
In order to thrive in the “age of the customer,” businesses in the region have three main priorities: speed, flexibility, and innovation. Simplifying the integration of AI into daily business operations is critical to achieving these goals. This also enables operational consistency across teams and the flexibility to run AI workloads anywhere.
In our Singapore market, we see the financial services industry leading the charge, with both local and regional banks leveraging hybrid cloud for AI workloads.
To fully capitalize on these advancements, APAC organizations will collaborate with reliable providers that offer the expertise and infrastructure to leapfrog ahead without the need for extensive scaling.
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AI maturity will require multi-dimensional preparation
In 2024, we may start to see some enterprises that are overly fixated on immediate returns reign in their efforts on AI-driven transformations prematurely. However, to truly unlock AI’s full potential, enterprises need to take a long-term view.
When gauging AI maturity on four main critical criteria: culture and leadership, skills and people, data foundation, and governance framework, organizations in the region need to plan for attracting advanced AI and machine learning expertise, dedicated data science teams, AI-relevant workforces, and prioritization of data governance and compliance to mitigate regulation risks.
To achieve AI maturity, the region will need to adopt a more strategic and patient approach, particularly in more complex areas where AI can drive significant value.
Beyond investing in enterprise data and technology to enhance data readiness, organizations need to be prepared at every level. This involves fostering a culture of innovation, upskilling employees to embrace new technologies, and aligning long-term processes with strategic business goals.
