Taiwan’s 2025 AI Watchlist: 9 Priorities for Marketers and Engineers

The screening question is deliberately practical: which topics keep appearing in Traditional Chinese enterprise AI coverage, which have moved beyond experimentation, and which affect marketing, product, engineering and IT operations at the same time?

The 9-topic watchlist at a glance

For marketers: turn generators into governed workflows

1. Generative AI content workflows

For marketers, generative AI is still the easiest starting point. But the research focus has moved beyond clever prompts.

iThome’s service-sector data says Taiwan’s service industry is among the most active adopters of generative AI, with 16% of service-sector companies already using it in formal production environments. That matters because marketing, retail, customer service and content operations often sit close to service-sector workflows.

The useful question is not just: can AI write a post? It is: can the team turn product descriptions, customer-service replies, social posts, EDMs, campaign briefs and internal knowledge summaries into repeatable, reviewable and traceable workflows?

One-off speed is valuable. But in a business setting, the bigger prize is consistency: clear inputs, approved tone, factual checks, review steps and ownership.

2. AI-agent marketing automation

AI agents are a shared topic for marketing, product and engineering teams. INSIDE’s 2025 white paper says companies are moving past the stage of simply chatting with AI and are asking AI to do work. It describes AI agents as digital collaborators with capabilities such as perception, planning, action and reflection.

For a marketing team, that suggests a shift from asking AI to draft one piece of copy toward asking AI to read campaign data, plan a task, draft content, trigger a workflow and hand off to a human reviewer at the right point.

Technically, AI agents can also improve how they obtain and process information by connecting to tools such as knowledge graphs, RAG and API queries.

3. Multimodal content and creative operations

CIO Taiwan, citing IDC, identifies multimodal GenAI as a major 2025 enterprise trend, with companies expected to prefer models that can process images, video and text together.

For marketers, this is a direct challenge to text-only thinking. Product pages, ad visuals, short-video scripts, support knowledge, social images and campaign briefs may increasingly belong in the same AI-assisted planning, production, checking and reuse cycle.

The operational question is therefore broader than which image model is best. It is whether the content team can manage creative assets as a connected system: brief, copy, visual, video, approval, compliance and performance feedback.

4. ROI, budget and hallucination governance

As AI moves closer to live business use, performance measurement and risk control become unavoidable. INSIDE’s white paper points to two linked problems: a 70.9% budget fog and a trust crisis around AI hallucinations.

For marketers, that means AI adoption should be studied alongside governance. Useful metrics include whether AI shortens production time, whether outputs remain on-brand and factually correct, and whether costs can be traced to a campaign, channel or workflow.

These questions decide whether AI stays a short-term experiment or becomes part of daily marketing operations.

For engineers: move from model calls to production systems

5. AI-agent and agentic system design

For engineers, the core challenge of AI agents is not a single impressive answer. It is whether a system can complete tasks reliably inside real constraints.

iThome says agentic AI adoption increased sharply from last year, and INSIDE frames AI agents as part of the move from AI that chats to AI that acts.

That makes agent design a systems problem. Engineers should study tool calling, API integration, task planning, state management, error recovery, permission control, observability and human-in-the-loop escalation. Those design choices determine whether an agent is a demo or a dependable part of an enterprise workflow.

6. RAG, or retrieval-augmented generation

RAG remains one of the most important foundation topics for engineers. iThome lists RAG as an emerging GenAI-related technology with notable adoption growth, showing that companies remain focused on connecting models to internal data and evidence.

Key research questions include how to prepare data sources, rank retrieval results, attach supporting evidence, evaluate answer accuracy, and handle outdated or contradictory knowledge.

If AI is going to become an enterprise knowledge entry point rather than a general chatbot, RAG is usually one of the first architecture questions engineering teams have to solve.

7. AI-augmented software engineering

iThome explicitly names AI-augmented software engineering as a growing adoption topic, including AI support for development, debugging and testing.

That means engineers should not treat AI coding tools only as autocomplete. The more strategic research area is how AI can participate in test-case generation, bug analysis, refactoring suggestions, documentation updates, code review and the preservation of internal engineering knowledge.

The practical goal is not to replace the software development lifecycle. It is to decide where AI can safely reduce friction without lowering quality, security or maintainability.

8. AIOps and AI-assisted operations

iThome says that, driven by the generative AI wave, more Taiwanese companies are looking at AIOps to optimize IT operations.

The value of AIOps is not limited to automated alerts. It can also connect logs, monitoring data, incident records and operations knowledge to help summarize incidents, detect abnormal patterns, suggest likely causes and accelerate troubleshooting.

For engineering and SRE teams, AIOps is where AI moves from the development side into the operations side of the software lifecycle.

9. SLMs, small language models and multi-model deployment

CIO Taiwan, citing IDC, says not every company needs a large language model. Enterprises are expected to use small language models according to scenario needs, while multi-model AI becomes a normal pattern for enterprise AI development.

For engineers, that shifts the research agenda from model leaderboards to deployment strategy. Which tasks need a large model? Which can run on a smaller model? When is model routing useful? How should the team evaluate cost, latency, privacy and quality?

Teams working closer to hardware, endpoints or infrastructure may also want to track edge AI. MIC says AI PCs and AI phones will accelerate penetration in 2025, and that as AI moves to the edge, AI chips will become more diversified.

Traditional Chinese keywords worth tracking

If your reading list is primarily Traditional Chinese, these search terms map closely to the themes that recur in Taiwan CIO surveys, ICT trend coverage and AI-agent white papers.

• 生成式 AI, GenAI, AI content workflow
• AI Agent, 人工智慧代理人, 代理式 AI, agentic AI
• RAG, 檢索增強生成, enterprise knowledge-base Q&A
• AI 增強軟體工程, AI-assisted development, AI testing
• AIOps, AI 維運, IT operations automation
• 多模態 AI, multimodal AI, text-image-video models
• SLM, 小語言模型, multi-model AI, model deployment selection
• AI governance, AI hallucination, ROI, budget control

A sensible learning order

For marketers, start by standardizing generative AI content workflows. Then study how AI agents can connect tasks and tools. After that, bring multimodal assets and governance into the same operating model. That sequence matches the evidence of service-sector GenAI adoption, enterprise interest in multimodal models, and the shift from chatting with AI to asking AI to do work.

For engineers, start with RAG and AI-assisted development. Then move into agentic system design, AIOps and multi-model deployment. That path lines up with iThome’s adoption-growth themes and IDC’s view, reported by CIO Taiwan, that SLM and multi-model use will become more common.

For product owners or AI implementation leads, the most useful first question is not which model is strongest. It is which workflow can be measured: what the input is, what AI should do, who reviews it, what success looks like, and how the process rolls back when it fails.

That is exactly where INSIDE’s concerns about budget uncertainty and hallucination risk become operationally important.

FAQ

Is there an official Taiwan ranking of AI topics for marketers?

Based on the public sources used here, no. A more reliable approach is to compare Taiwan CIO survey findings, service-sector adoption data, ICT trend coverage and AI Agent white paper analysis, then identify the topics that appear repeatedly and are closest to real implementation.

Why do AI agents, RAG and multimodal AI deserve priority?

AI agents represent the shift from chatting with AI to having AI perform tasks. RAG addresses how models connect to searchable enterprise knowledge and evidence. Multimodal AI reflects the move toward handling text, images and video in the same AI-assisted process.

Is prompt engineering enough for engineers?

No. Prompting remains useful, but the stronger engineering signals in Taiwan enterprise coverage are RAG, AI-augmented software engineering, AIOps, agent architecture and SLM or multi-model deployment strategy.

Bottom line

Taiwan’s 2025 AI agenda is moving from single-use generation to connected workflows. Marketers should study content workflows, AI-agent automation, multimodal operations and governance. Engineers should study agents, RAG, AI-assisted software development, AIOps and model deployment strategy.

Together, those topics form the capability stack companies need if they want AI to move from trial use into repeatable, governed business operations.