Taiwan AI Subsidies 2026: SBIR, SIIR, CITD Application Guide

1. Why 2026 Is the Critical Year for Taiwan Enterprise AI Subsidies

2026 (Republic of China Year 115) is the final year of the Executive Yuan's "Taiwan AI Action Plan 2.0"^[5]. This four-year national AI strategy, approved in 2023, is organized around five pillars: talent development, technology cultivation and industry growth, operational environment improvement, international engagement, and social and ethical preparedness. Total program spending is in the tens of billions of NT dollars. The FY 2025 technology budget alone reached NT$15.748 billion, a 29.9% increase (roughly NT$3.625 billion) over the previous year^[6].

The final year of the plan has two important implications. First, every ministry faces execution-rate pressure — unspent budget must be committed before the fiscal year ends. Second, the follow-on plan (widely expected to be "AI Action Plan 3.0" or an equivalent) has yet to be formalized, meaning the current subsidy framework could change. For enterprises, the present moment represents the lowest-risk, most resource-rich window for securing AI funding.

The global context makes this window even more valuable. Gartner's January 2026 forecast projects worldwide AI spending of US$2.52 trillion, a 44% year-on-year surge^[8]. IDC's Taiwan-specific analysis projects AI Platform spending will grow from US$134 million in 2025 to US$608 million by 2029^[9]. In the IMD 2025 World Digital Competitiveness Ranking, Taiwan placed 10th globally and 3rd in "Future Readiness"^[10] — but converting those rankings into real industrial competitiveness requires enterprises to accelerate AI adoption.

Yet a sobering reality persists. McKinsey's global survey found that while 88% of companies are now using AI, only 6% achieve "high performance" outcomes^[13]. Deloitte's research similarly shows that although 78% of organizations plan to increase AI spending, 30% or fewer experimental projects manage to scale within 3 to 6 months^[12]. For Taiwan's SMEs, government subsidies are the critical lever that shortens the distance from experimentation to scale — using grants to reduce upfront risk is how resource-constrained companies complete the last mile of AI transformation.

2. SBIR: The Go-To R&D Subsidy for SME AI Projects

SBIR (Small Business Innovation Research) is Taiwan's longest-running and broadest-reaching R&D subsidy program for small and medium enterprises^[1]. Administered by the Small and Medium Enterprise and Startup Administration under the Ministry of Economic Affairs, SBIR continues to accept applications in FY 115 on a rolling basis (not a fixed deadline), giving enterprises maximum scheduling flexibility.

2.1 FY 115 SBIR Funding Structure

SBIR is organized into three phases, and enterprises can choose based on their R&D maturity:

Phase 1 — Feasibility Study (Individual): Up to NT$1 million, with a maximum execution period of 6 months. This is ideal for proof-of-concept (PoC) stage AI projects — for instance, testing whether a machine-vision model can reliably detect defects on a production line, or evaluating the accuracy of an NLP model in a specialized domain. The focus is on demonstrating technical feasibility.

Phase 2 — Research & Development (Individual): Up to NT$10 million, with a maximum execution period of 2 years. This is the primary SBIR track, suited for AI projects that have cleared the PoC stage and are moving into prototype development and system integration. Reviewers evaluate technical innovation, team capability, market commercialization potential, and projected impact^[1].

Phase 2+ — Value-Added Commercialization (Individual): Up to NT$5 million, available to companies that completed Phase 2 with strong results. This funding supports further commercialization and market expansion.

2.2 Application Strategy for AI Projects

Based on SBIR's review criteria, AI-related proposals should address three dimensions:

Technical Innovation: The first question reviewers ask is: "How does your AI solution differ from what already exists?" Simply calling a commercial API (such as routing requests to the ChatGPT API) is generally not considered innovative. You need to articulate what is distinctive about your model architecture, training methodology, data pipeline, or domain-specific adaptation. For example, a model built on a hybrid architecture combining an industry knowledge graph with time-series anomaly detection, trained on proprietary Taiwanese manufacturing data — that kind of technical specificity is what persuades reviewers.

Market and Application Value: Clearly describe the addressable market, target customer profile, projected revenue model, and go-to-market timeline. Letters of intent from potential customers or pilot-user feedback will significantly strengthen credibility.

Team Execution Capability: Detail the core team's AI-related education, professional background, and relevant accomplishments. International publications, AI competition awards, or prior successfully-completed government projects should be highlighted prominently.

3. SIIR: Driving AI-Enabled Service Innovation

SIIR (Service Industry Innovation Research) is administered by the Department of Commerce under the Ministry of Economic Affairs^[2] and is dedicated to supporting innovative R&D in the service sector. FY 115 applications are now being accepted, with the following funding tiers:

3.1 FY 115 SIIR Funding Structure

Individual Application (Innovation R&D): Up to NT$1.5 million, for a single service-sector company developing AI-driven service innovations — such as an AI scheduling engine, an AI-powered customer service chatbot, or a generative AI marketing content platform.

Consortium Application (Alliance): Up to NT$5 million, for multiple enterprises jointly building an AI service platform or shared AI infrastructure serving an entire industry segment.

International Cooperation: Between NT$5 million and NT$10 million, encouraging service-sector companies to co-develop AI applications with international partners^[2].

3.2 How SIIR Differs from SBIR

The biggest difference lies in what the reviewers are looking for. SBIR emphasizes technical innovation, whereas SIIR focuses more on service innovation and business model transformation. Your AI solution does not necessarily need an algorithmic breakthrough; it needs to demonstrate how it creates a fundamentally new customer experience, reshapes service delivery, or unlocks a new revenue stream. For example, a restaurant chain using AI to analyze POS data alongside weather forecasts to dynamically adjust menus and prep quantities — technically straightforward, but with clear value in service-model innovation and operational efficiency, which is exactly the profile SIIR rewards.

4. CITD: Accelerating AI Adoption in Traditional Manufacturing

CITD (Conventional Industry Technology Development) is administered by the Industrial Development Administration under the Ministry of Economic Affairs^[3] and is specifically designed to help traditional industries — textiles, metalworking, food processing, plastics, machinery, and similar sectors — upgrade their technology. FY 115 applications are now open.

4.1 FY 115 CITD Funding Structure

Product Development (Individual): Up to NT$2 million, covering no more than 50% of total project costs. This suits a single company developing and deploying AI applications such as defect detection, intelligent scheduling, or predictive maintenance.

R&D Consortium (Joint Application): Up to NT$10 million, proposed jointly by three or more companies — or a combination of companies, research institutes, and academic institutions. Ideal for supply-chain partners building a shared AI application platform^[3].

CITD reviewers pay close attention to industry relevance — your AI solution must directly address a concrete pain point in a traditional manufacturing process. Tool-wear prediction in metalworking, intelligent color-deviation detection in textiles, quality-consistency monitoring in food processing — these deeply industry-specific AI applications are exactly the kind of proposals CITD most wants to fund.

5. Smart Enterprise Program: The Most Accessible AI Adoption Subsidy

The "Program for Enhancing SME Smart Business Efficiency," administered by the Small and Medium Enterprise and Startup Administration^[4], is currently the most direct and lowest-barrier subsidy channel for Taiwan's SMEs looking to adopt AI and digital tools. The FY 115 budget totals NT$310 million, with a submission deadline of March 10, 2026.

Unlike R&D-oriented programs such as SBIR, this program focuses on deploying mature, commercially available AI and digital tools rather than requiring companies to develop proprietary technology. In other words, if your company wants to implement an AI customer-service system, AI-powered inventory management, or an AI analytics dashboard — solutions that already exist as proven products — this program is the best fit.

Applicants must work with a pre-approved "AI Technology Service Provider" or select an approved solution from the SME AI platform. The provider then assists with diagnosis, planning, and implementation. This mechanism is designed to ensure subsidy funds are spent effectively, rather than lost to internal trial-and-error.

6. Taiwan AI Action Plan 2.0: The Policy Landscape and Five Strategic Pillars

All the individual subsidy programs described above ultimately draw their funding from one overarching national framework — the Executive Yuan's "Taiwan AI Action Plan 2.0 (2023-2026)," approved in 2023^[5]. Understanding this top-level architecture helps enterprises anticipate future policy direction and plan accordingly.

6.1 The Five Strategic Pillars

AI Action Plan 2.0 is coordinated by the National Science and Technology Council (NSTC) and spans five pillars:

Pillar 1: AI Talent Development — The previous plan cycle cultivated over 33,000 AI application professionals and 4,300 advanced technical specialists, with 62 universities offering AI curricula (8,000 students enrolled). The 2.0 plan continues to expand these programs^[5].

Pillar 2: Technology Cultivation & Industry Growth — This covers AI chip development (targeting top-3 global positions in domain-specific chips), targets of NT$250 billion or more in incremental AI hardware and software output, and startup incubation. The Ministry of Digital Affairs' "NT$10 Billion AI Startup Fund" (backed by the National Development Fund over 10 years) falls under this pillar^[11].

Pillar 3: Operational Environment Improvement — Building computing infrastructure, opening government datasets, and publishing the "Reference Guidelines for Government Use of Generative AI." These guidelines, officially released on October 3, 2023 and comprising 10 articles, provide a risk-management framework for public-sector adoption of generative AI^[7].

Pillar 4: International Engagement — Promoting international cooperation and export of Taiwanese AI technology.

Pillar 5: Social & Ethical Preparedness — Addressing the ethical, regulatory, and societal impacts of AI. The Executive Yuan has approved a draft "Artificial Intelligence Basic Act" and submitted it to the Legislative Yuan for review.

6.2 What the Budget Numbers Tell Us

In FY 2023 (ROC 112), the AI Action Plan 2.0 had an allocated budget of NT$13.114 billion and actual expenditures of NT$13.048 billion — an execution rate of 99.5%. The FY 2024 budget was NT$12.123 billion, and FY 2025 jumped to NT$15.748 billion^[6]. Year-over-year budget growth combined with near-perfect execution rates signals that the government is not merely talking about AI — it is spending. As the plan's closing year, 2026 will almost certainly see ministries accelerating the drawdown of any remaining unspent funds.

7. Subsidy Comparison: Amounts, Eligibility & Program Features at a Glance

The following table summarizes key details of the main AI-related government subsidy programs available in FY 115 (2026)^[1][2][3][4]:

In addition, enterprises should consider the R&D Investment Tax Credit under Article 10-1 of the Statute for Industrial Innovation^[14]. Companies conducting AI-related R&D may deduct qualifying expenditures from their business income tax liability — either 15% of the current year's R&D spending (credited against that year's tax), or 10% of the amount by which current-year R&D spending exceeds the three-year average (credited over three years). This tax benefit does not conflict with grant funding. Enterprises can use both simultaneously: subsidies lower the upfront R&D cost, and tax credits reduce the remaining tax burden, creating a powerful double-leverage effect on the financials of an AI project.

8. Application Strategy: A Three-Stage Path from Assessment to Scale

Faced with so many subsidy channels, most companies struggle not with "Can we apply?" but with "Which one should we apply for?" Here is a three-stage approach based on practical experience:

8.1 Stage 1: Low-Barrier Quick Start

Prioritize the Smart Enterprise Program^[4] or SBIR Phase 1. The former suits companies that want to deploy proven, off-the-shelf AI tools (e.g., AI customer service, AI reporting dashboards, AI scheduling systems). The latter suits companies with in-house R&D ambitions. Both have relatively low entry barriers and can yield initial results within 3 to 6 months.

8.2 Stage 2: Leverage Early Results for Larger Funding

Once Stage 1 is complete, use the quantifiable outcomes (e.g., "After deploying AI quality inspection, our defect rate dropped by 18%") as supporting evidence to apply for SBIR Phase 2 (up to NT$10 million) or a CITD R&D Consortium grant (up to NT$10 million). The key at this stage is to let your initial results speak for themselves — concrete data makes a proposal dramatically more persuasive.

8.3 Stage 3: Industry-Wide Scaling and International Cooperation

Once your AI application has been validated internally, consider SIIR International Cooperation (up to NT$10 million) or NSTC industry-academia mechanisms to take your AI solution to a broader market. SBIR Phase 2+'s NT$5 million value-added funding is also relevant at this stage.

9. Proposal Writing: Practical Tips and Common Rejection Reasons

A strong AI R&D subsidy proposal needs more than solid technical content — it needs to tell a compelling story from the reviewer's perspective.

9.1 Problem Definition: Lead with the Pain Point, Not the Technology

Reviewers have read too many "AI for AI's sake" proposals. The right approach is: start by clearly describing the specific business problem (ideally with quantified data), then explain why AI is the best solution for that problem, and only then unfold the technical design. For example: "Our company handles 12,000 customer complaints per month, of which 68% are repetitive issues consuming 73% of our support team's working hours. This project will develop an NLP-based intelligent customer service system expected to automatically resolve 80% of repetitive complaints."

9.2 Common Budget Pitfalls

Unrealistic personnel costs: If you list an AI engineer's monthly salary at NT$150,000 but your company has only 10 employees and is not a technology firm, reviewers will question the credibility. Reference the Ministry of Labor's salary survey data and set figures within a defensible range.

Vague cloud computing costs: Specify the exact cloud service plans you intend to use (e.g., which AWS instance type, how many GPU hours), and attach official pricing screenshots or vendor quotations.

Unmeasurable KPIs: Phrases like "improve efficiency" or "enhance quality" are red flags. Reviewers want concrete metrics — on the technical side, model accuracy, F1 score, inference latency; on the business side, cost savings in dollar terms, revenue growth rate, customer satisfaction improvement in percentage points.

9.3 Five Common Reasons for Rejection

(1) Ineligibility: A non-SME applying to SBIR, a non-service company applying to SIIR, or a non-traditional-industry company applying to CITD. Each program defines its eligible applicants precisely — verify your eligibility line by line before submitting.

(2) Insufficient innovation: "Deploying an off-the-shelf AI tool" is not the same as "AI innovation R&D." If your project only involves purchasing commercial AI software and installing it, that qualifies as "technology adoption," not "R&D," and does not meet SBIR or CITD criteria. (For that kind of need, apply to the Smart Enterprise Program instead.)

(3) Duplicate applications: The same R&D content cannot be submitted to two or more government subsidy programs simultaneously. Review agencies share data, and duplicates are flagged. The legitimate approach is to split a larger initiative into distinct phases or sub-projects, each applied to a different program.

(4) Weak market analysis: Citing only international case studies or outdated data is a common shortcoming. Use the latest Taiwan-market data from IDC^[9], Gartner^[8], and domestic industry association statistics.

(5) High closeout risk: Unrealistic execution timelines, especially insufficient time allocated for data collection and labeling. We recommend establishing rigorous project documentation from day one — every expenditure with complete receipts, every technical meeting with minutes, and every milestone with specific deliverables.

10. Choosing the Right AI Technology Service Provider

For most SMEs, applying for AI subsidies is an unfamiliar and time-consuming process. From identifying the right program and writing the proposal to preparing the budget and rehearsing the oral defense, every step requires specialized knowledge. Choosing a capable AI service provider is often the decisive factor in whether a grant application succeeds.

When evaluating AI service providers, consider the following:

Technical depth: Does the provider possess genuine AI engineering capability, or are they simply repackaging third-party solutions? Look for indicators such as published research, technical blogs, or open-source contributions.

Industry experience: Do they have actual AI deployment track records in your industry? Only about 5% of enterprise AI pilots succeed at scale — relevant industry experience is critical for reducing failure risk.

Subsidy application expertise: Have they successfully guided enterprises through government subsidy reviews before? Providers who understand reviewer expectations and scoring criteria can materially improve proposal quality.

End-to-end service capability: Can they support the full lifecycle — from technical solution design and proposal writing, through oral defense preparation, to project execution management and final closeout reporting?

11. Conclusion: Seize the Policy Window to Accelerate Enterprise AI Transformation

2026 is the final year of the "Taiwan AI Action Plan 2.0"^[5]. That means a well-defined policy window is open — the current subsidy framework is in place, budgets have been allocated, and government agencies face spend-down pressure. While post-2027 policies will almost certainly continue to support AI, specific program names, grant amounts, and eligibility criteria may shift.

Against a backdrop of surging global AI investment (Gartner projects US$2.52 trillion in 2026^[8]), the question facing Taiwanese enterprises is no longer whether to adopt AI, but how quickly they can do it. Government subsidies are the most effective accelerator — enabling companies to launch AI projects at 50% or lower self-funding ratios that would otherwise be impossible on constrained budgets.

Meta Intelligence's team combines deep AI technical expertise with hands-on government subsidy experience, providing end-to-end services from program selection and technical solution design through proposal writing and project execution management. Our goal is not simply to help you "win the grant" — it is to ensure that subsidy funds are channeled into AI R&D directions that create lasting competitive value for your enterprise.