How should the first AI use case be chosen?
Rank candidates by data availability, business benefit and controllable risk, not by how new the technology is. A good starting point usually has a clear goal, usable data and an acceptable cost of failure, such as AI quality inspection or predictive maintenance of equipment.
Candidate use cases include predictive maintenance, quality inspection, customer service automation and sales forecasting, with the risk level of each AI application assessed alongside. Set KPI baselines before introduction so the return on AI investment can be tracked.
Buy, co-develop or build in-house: what should a CTO clarify first?
| Model | Suits | Clarify first |
|---|---|---|
| Buy a mature AI solution | Generic needs, fast go-live | Where data is stored, whether the vendor may use your data, whether you can exit later |
| Co-develop with a technology vendor | Special needs, no in-house data science talent | Ownership of model and data rights, acceptance criteria, who operates it after go-live |
| Build in-house capability | AI is a core competence needing long-term iteration | Talent development, data foundations and governance processes |
AI transformation is possible without data scientists; the three models can be combined as needed.
Under governance-first, which mechanisms should be in place before AI is introduced?
- AI ethics principles
- AI system inventory (covering both built and purchased AI use cases)
- Security requirements for AI development and procurement
- Algorithm review process
The risk assessment of each AI application can refer to ISO/IEC 23894 or the NIST AI Risk Management Framework (govern, map, measure, manage); generative AI needs separate treatment. Where staff only use general generative AI tools for everyday documents without confidential or personal data, internal usage rules and training are usually enough.
Does ISO 42001 certification mean EU AI Act compliance?
No. ISO/IEC 42001 is not a harmonised standard under the EU AI Act, and certification itself creates no presumption of conformity. Certification lets you show customers, partners and regulators that AI runs under a responsible governance framework, but high-risk AI systems must still meet legal obligations such as risk management, technical documentation, human oversight and conformity assessment.
The CTO should first determine each AI system's role under the EU AI Act (provider or deployer) and its risk level; high-risk AI requires technical documentation, data governance, accuracy and robustness. The determination in each case is for legal counsel to judge.
What is the CTO responsible for regarding training data and models after go-live?
- Training data: a mechanism for managing AI training data that ensures quality and security; clarity on where data comes from and how personal data complies with Taiwan's Personal Data Protection Act
- Post-deployment monitoring: performance monitoring (model drift detection) and continuous improvement
- Human oversight: clear ownership of human oversight and the decision to stop a system
If AI affects decisions about customers or employees, processes personal data, or the product will enter the EU market, formal governance and risk assessment are needed.
What does Taiwan's AI Basic Act require of corporate R&D?
Taiwan's AI Basic Act was promulgated and took effect on 14 January 2026, with the National Science and Technology Council as the central competent authority. Most provisions govern the government; specific requirements for companies will mainly come through management rules and industry guidelines that each sector regulator sets by risk, which the Executive Yuan requires by January 2028.
The Ministry of Digital Affairs published version 1.0 of its AI risk classification framework on 7 July 2026, which a CTO can use as a reference for inventorying and assessing AI application risks.
How does Winners help a CTO drive AI transformation?
The service page lists four steps, typically 7 to 12 months or more:
- Identifying AI opportunities and assessing risk
- Building the AI governance framework (governance first)
- Introducing and integrating AI systems
- Monitoring, optimisation and EU AI Act compliance
KPI baselines are set before introduction; VP- and director-level consultants lead throughout, never outsourced; after building the mechanism from scratch, Winners' own RegTech platform Ahura continues to support maintenance and operation.
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