The accelerated march of artificial intelligence (AI)—comprising generative, agentic, and advanced automation—has shifted from an experimental frontier to foundational infrastructure in enterprises across Southeast Asia.
By 2025/2026, AI will no longer be a luxury but a necessity for organisations seeking a competitive advantage, resilience, and innovation. This transformation requires not only the adoption of technology but also the continuous, challenging work of integrating AI deeply and responsibly into business processes.
SAP Southeast Asia's president and managing director, Liher Urbizu, shares valuable perspectives on why AI integration is indispensable, how enterprises can navigate the challenges, and what the future holds for CIOs and business leaders in the region.
Why embed AI and automation?
When asked about the long-term strategy for embedding AI and automation into enterprise software, Urbizu underscores their strategic imperative, emphasising that the business reality of today is that AI and advanced automation are not optional.
"They have become fundamental pillars in enterprise software that drives business success. Our customers want agility, resilience, and innovation—all of which AI enables by automating routine tasks, accelerating decision-making, and powering new content and workflow models," he elaborated.
This acknowledgement reflects the regional trend: Southeast Asian enterprises are moving past the "if" of AI adoption and are now focused on the "how." AI is no longer experimental but has become ingrained in operational DNA.
Generative AI tools support content creation and decision analytics, while agentic AI automates complex workflows, significantly reducing the need for manual interventions.
Industry research supports this shift. Gartner predicts that by 2026, 70% of new enterprise applications will include generative AI capabilities, fundamentally redefining application design and user experience. For CIOs, this means prioritising AI-driven software as a non-negotiable foundation of digital infrastructure.
The hard work behind the integration
Embedding AI deeply within enterprise software and business processes is a complex task. Urbizu highlights the layers of effort required, starting with the statement: "It's not simply about deploying AI tools."
"The hard work involves operationalising AI responsibly, building robust data pipelines, modernising legacy applications, upskilling teams, and integrating security and compliance by design. This requires coordinated effort across functions and sustained leadership commitment." Liher Urbizu
The integration involves modernising legacy systems that were not designed for AI workloads, developing clean and reliable data pipelines for training and inference, and integrating AI governance into the software development lifecycle. Additionally, workforce upskilling is critical as AI changes job roles and skill requirements.
CIOs in Southeast Asia must bridge technology, talent, and compliance gaps while moving at pace—a careful balance between innovation speed and risk mitigation.
According to a 2025 IDC survey, over 60% of CIOs in the region cite talent shortages and integration complexity as primary challenges in scaling AI.
Security challenges to AI adoption
AI integration brings heightened cybersecurity risks. Urbizu expresses caution: "AI workloads, especially in the cloud, face new vulnerabilities that traditional IT hasn't encountered before."
He reminds us that advanced threats increasingly target misconfigurations and privilege escalations in the deployment of AI models. Security cannot be an afterthought—it must be embedded from the outset.
This concern aligns with reports from cybersecurity firms warning of AI-specific attack vectors—including data poisoning, model extraction, and adversarial inputs. Cloud deployments amplify risks due to the expanded attack surface and complex identity and access controls.
Enterprise security teams require AI-tailored governance frameworks, real-time threat detection, and incident response protocols that align with regional regulations to ensure the trustworthiness and continuity of AI-driven operations.
Navigating evolving regulations
Keeping pace with regulatory frameworks is another vital dimension. Urbizu notes the regulatory landscape in Southeast Asia: "We see an encouraging convergence in regional and national AI guidelines."
"ASEAN's 2025 AI governance framework emphasises transparency, accountability, and data protection, while countries like Singapore and Thailand are harmonising laws to strike the right balance between fostering innovation and ensuring compliance and trust." Liher Urbizu
Governments across ASEAN are actively updating regulations, recognising generative AI risks such as misinformation and bias. Singapore's Cybersecurity Act revisions and Thailand's Supported AI Law demonstrate evolving legal frameworks that CIOs must incorporate into operational strategies.
Industry analysts highlight that compliance with such evolving regulations will become a non-negotiable requirement, necessitating advanced monitoring and audit capabilities embedded within AI systems.
Achieving business objectives with AI
Ultimately, the integration of AI and automation drives tangible business outcomes. Urbizu posits that embedding AI helps enterprises achieve agility—quickly adapting to market changes; resilience—better anticipating and responding to disruptions; and innovation—opening new product and service opportunities. This is the competitive edge in a digital-first world.
By automating routine workflows, enhancing decision-support systems, and powering new customer engagement models, AI significantly improves efficiency and differentiation. CIOs and CTOs are now measured not just on cost savings but on their ability to embed AI-driven value across the enterprise.
AI as the backbone of future enterprises
The journey to making AI the core of competitive infrastructure is challenging. It demands persistence, comprehensive transformation, and careful risk management. Yet, as SAP's Urbizu emphasizes, the rewards are significant—and essential for future-proofing the enterprise.
Charlie Dai, VP and principal analyst at Forrester, highlights a key opportunity for APAC firms: “Rapidly operationalising AI enables organisations here to leapfrog legacy constraints, much like what leading companies in Western markets are doing.”
However, Dai also cautions that early adopters face pressures, especially around “vendor monetisation, as agentic AI systems require deep integration and workflow redesign.”
To successfully balance innovation, cost, and agility, he advises CIOs to adopt integrated AI platforms designed with open, modular architectures.
He warns against over-dependence on a single vendor and underscores that long-term control over AI investments depends on negotiating transparent pricing and accelerating localization strategies aligned with regional needs.
Summing up the imperative, Urbizu states:
“The future of enterprise software is inseparable from AI and automation. Leading businesses in Southeast Asia are approaching AI not as a one-off project but as a strategic priority to secure sustainable competitive advantage in an increasingly digital economy.”
