AI and Open Source: Shaping Enterprise in 2025

Now that most of us have moved past the experimental phase for AI, we see businesses increasingly unlocking its value with this transformational technology. As reported in a McKinsey Global Survey, 65% of respondents shared their organizations are now regularly using AI—double the adoption rate in 2023. 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’s next?

The future of AI lies in open source. As AI expands beyond simple automation into more sophisticated applications like predictive analytics, content generation and even decision-making, it has become essential for enterprises across industries to keep pace. While at varying stages in their AI adoption journey, open source allows businesses to pioneer a new era of innovation. Looking ahead, these are three key trends poised to drive significant changes for enterprises in the APAC region next year.

#1 Discover the open source advantage in AI

Since last year, the number of open source gen AI projects has surged by 98%, with many of these contributions coming from India, Japan, and Singapore. This reflects the importance of collaboration and accessibility when it comes to new technologies like AI and we are likely to see gen AI activity increase globally. Open source AI platforms and tools, as well as open source-licensed models, are already 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 businesses flexibility in navigating constraints like 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, many eyes make all bugs shallow. With vulnerabilities swiftly identified and addressed, businesses will be able to foster greater trust in AI-driven outcomes.

#2 Make hybrid cloud your default

Open hybrid cloud is no longer an afterthought, but a default. In order to thrive in the age of the customer, businesses in APAC 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 flexibility to run AI workloads anywhere, ensuring businesses remain agile and adaptable.

Beyond bolstering system reliability, robust data provenance helps organizations navigate increasingly strict privacy regulations like India’s Digital Personal Data Protection Act (DPDPA). By embracing these practices, businesses not only stay compliant but demonstrate their commitment to ethical AI development. The message is clear: transparency isn’t optional. It’s the foundation of hybrid cloud that works for everyone—a future where innovation thrives on trust, integrity, and shared responsibility.

#3 Plan your AI strategy for sustainable growth

ChatGPT has brought gen AI to the forefront of mainstream consciousness, reshaping how businesses approach workflows and drive efficiencies in uncertain times. We might 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.

In the AI Readiness Barometer: AI landscape study, conducted by Ecosystm on behalf of IBM³, AI maturity was assessed based on four main critical criteria: culture and leadership, skills and people, data foundation, and governance framework. Although AI is a business priority for these ASEAN enterprises surveyed, most lack readiness, including the advanced AI and machine learning expertise needed to harness its full potential. In fact, only 17% said their organizations have extensive expertise and dedicated data science teams. Most organizations are still lagging in AI relevant skills; and are also not prioritizing data governance and compliance enough, potentially exposing them to regulation risk.

To achieve AI maturity, enterprises must 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.

What’s next in 2025

In the new year, we will continue to see AI evolve as a cornerstone of innovation, with a deeper integration of AI and open source shaping the future of technology. This not only broadens the accessibility of new technologies but also enhances the adaptability and efficiency of enterprise solutions across industries. At the heart of these advancements, data is 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.

As we step into 2025, these 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 with open source.