As we have written previously, Southeast Asia’s Generative AI (Gen AI) wave will lean more on Gen AI for enterprise application. But how is this trend evolving exactly? What are the specific use cases and drivers of spending on Gen AI solutions?

1. Enterprises in SEA and across the world increase their budget for Gen AI 
2. 2024 witnessed a substantial growth of Application Layer for AI-native enterprise applications. In general, there are 5 key dominant use cases:  (1) Code Copilots (51% adoption rate) ; (2) Support Chatbots (31% enterprise adoption); (3,4) Enterprise Search & Data Transformation: (Adoption rates of 28% and 27%, respectively); (5) Meeting Summarization (24% adoption)
3. Enterprises Focus on Value Over Short-Term Gains in Generative AI Adoption. Organizations are adopting a long-term perspective, favoring solutions that provide measurable value (30%) and align with their unique operational contexts (26%) over those with the lowest price point. These are two key elements Gen AI enterprise applications should have to attract customers.

(1) 40% on Gen AI spending now drawn from permanent budgets

Currently, 60% of enterprise investments in generative AI come from innovation budgets, highlighting the technology’s early adoption phase. However, with 40% of spending now drawn from permanent budgets—58% of which is reallocated from existing resources—organizations are signaling a stronger commitment to integrating AI into their operations.

While foundational model investments continue to dominate, the application layer is expanding at a faster pace, driven by emerging infrastructure-level design patterns. Businesses are leveraging these advancements to streamline workflows across industries, unlocking significant value and fostering innovation.

Gen AI Spend by Category: 2023 vs 2024 (Menlo VC)

There are two key areas shaping enterprise adoption of generative AI:

1. The Application Layer: Where initial breakthroughs are occurring, and untapped opportunities for startups are emerging.
2. The Modern AI Stack: Where the race to develop large language models (LLMs) is transforming competition and standardizing infrastructure patterns.

For Southeast Asia, the competitive landscape will revolve around the application layer.

(2) 8x increase in enterprise investments into Gen AI (from US$600M in 2023 to US$4.6B in 2024) 

In 2024, the application layer emerged as a key focus area, with significant advancements leveraging established architectural design patterns. Companies in this space are harnessing the power of LLMs across various domains to drive efficiencies and introduce new capabilities.

Enterprise buyers have embraced this opportunity, investing $4.6 billion in generative AI applications—a nearly eightfold increase from $600 million in 2023.

This surge in spending reflects not just higher budgets but also a broader vision. On average, organizations have identified 10 potential use cases for generative AI, signaling ambitious plans for the technology. Nearly 24% of these use cases are prioritized for immediate implementation, showcasing strong progress toward real-world application.

However, adoption is still in its early stages. Most companies have only a few use cases in production, while 33% of identified opportunities remain in the prototyping and evaluation phase.

(3) Code Copilots and Support Chatbots remain the most high power use cases of Gen AI, with knowledge management coming in third

As generative AI matures, certain use cases are proving their worth by delivering measurable ROI through improved productivity and operational efficiency:

• Code Copilots: Leading adoption at 51%, developers have become early power users of AI. 
• Support Chatbots: With 31% enterprise adoption, AI-powered support bots provide 24/7, knowledge-driven assistance to employees and customers. 
• Enterprise Search & Data Transformation: Adoption rates of 28% and 27%, respectively, reflect the growing emphasis on unlocking organizational knowledge trapped in siloed data.
• Meeting Summarization: Ranking fifth at 24% adoption, these tools save time by automating note-taking and summarization. 

These use cases highlight AI’s growing influence in reshaping enterprise workflows and delivering real-world value.

Dominant Gen AI use cases (Menlo VC)

(4) Only 1% of surveyed leaders cite cost as a concern for choosing Gen AI tools 

When choosing generative AI tools, enterprises emphasize return on investment (ROI) and industry-specific customization as top priorities. Interestingly, cost is rarely a deciding factor, with only 1% of surveyed leaders citing price as a concern. Instead, organizations are adopting a long-term perspective, favoring solutions that provide measurable value (30%) and align with their unique operational contexts (26%) over those with the lowest price point.

Selection Criteria for Gen AI Tools (Menlo VC)

Despite their focus on ROI and customization, many businesses overlook critical factors in the implementation process. Key considerations such as technical integration, scalability, and ongoing support often emerge as significant challenges too late. This oversight is akin to purchasing a car for its fuel efficiency without considering maintenance or service availability, which prove vital over time.

AI pilot programs often stumble due to unanticipated hurdles in the selection process. High implementation costs, responsible for 26% of failed pilots, frequently take organizations by surprise. Other challenges include data privacy issues (21%), subpar ROI (18%), and technical problems like hallucinations (15%). Addressing these potential obstacles proactively during planning and selection can significantly enhance the chances of a successful deployment.

Learn more about how you can take advantage of Gen AI solutions and tools for your organization in our AI Transformation Playbook.