The IBM Institute for Business Value’s “Top 5 Trends for 2025” points out that global enterprises are accelerating the large-scale adoption of AI. Nearly half of the surveyed companies indicate that in 2025 they plan to use AI to optimize processes or to accelerate innovation in products and business models. However, according to McKinsey, although 88% of companies have initiated AI-driven transformations, only 10% are able to achieve scaled deployment across multiple departments and applications. While enterprises recognize the enormous potential value of AI, the path to realizing that value is quite challenging. What risks are companies actually encountering, and what are the keys to achieving AI at scale?
Risks accompanying the path to large-scale AI adoption
According to key points presented by Deloitte at the 2025 Taiwan Artificial Intelligence Conference, implementing AI is not a one-off project. If AI adoption remains at the level of individual projects, results tend to be fragmented and hard to accumulate. Only by deeply integrating AI into organizational structures and workflows, moving toward modularization, can enterprises achieve true large-scale deployment and build a robust transformation framework. The risks and challenges of scaling AI applications can be categorized as follows:
- Increasing regulatory pressure:As AI rapidly advances, global attention on its risks and impacts is growing. Both government agencies and technology developers are calling for clear and stringent regulatory frameworks to ensure AI is used safely, controllably, and responsibly.
- Cyber risks and data security issues:The rise of generative AI has made advanced cyberattacks easier to execute, while also giving rise to new forms of malicious behavior and threat vectors.
- Data protection and privacy challenges:AI relies on massive amounts of data for training and operation, which often conflicts with personal data protection principles. Organizations must rethink how data is used to ensure intellectual property rights, security, and regulatory compliance.
- User expectations of AI:Users have increasingly high expectations for AI’s security, reliability, and ethical use. Companies are accelerating their response by embedding “trust” and “transparency” as core principles in AI development and deployment.
The key to scaling AI lies in modernizing the data infrastructure and ensuring robust data protection.
In addition to sufficient resources and AI talent and skills, the data infrastructure and protection are critical enablers. This includes a modernized data platform, comprehensive data governance, and robust protection measures to ensure safe and scalable AI operations. If data is fragmented or lacks secure risk controls, scaling AI applications becomes unfeasible. Enterprises should treat data as an accelerator, not a barrier, in the AI adoption process. The three key strategies for enterprise AI data are as follows:
- Focus on data management: establish a scalable and governable data foundation.
- Clear data access and permission controls.
- Layered data management strategy.
- Support data flow across diverse sources.
- Enhance data security: ensuring trust and compliance while scaling AI.
- Implement a zero-trust framework, verifying every access.
- Classify and encrypt sensitive data to ensure it is not leaked.
- Monitor data usage behaviors to detect potential threats.
- Ensure data quality: guarantee the accuracy and interpretability of model decisions.
- Clarify which data is valuable and how it should be stored.
- Establish automated data cleansing and validation processes.
- Regularly verify the integrity, accuracy, and timeliness of the data.
OmniStor AI, a zero-trust management system for the AI era, accelerates AI deployment at scale while ensuring security.
ASUS Cloud’s data security team has long focused on enterprise data protection, dedicated to building a zero-trust file management system for the AI era. Acting as a central hub for AI data security, it ensures strict access control, compliance with audit requirements, and integrates AI computing resources and tools to accelerate AI deployment while maintaining security. The four key highlights of OmniStor AI are as follows:
- Real-time endpoint data synchronization to a centralized data pool, accelerating data sharing and AI training.
- Centralized data integration, enabling seamless connection to computing resources and analytics tools.
- AI data access control to prevent sensitive information leakage.
- Comprehensive monitoring and auditing mechanisms to enforce AI governance and manage risks.
The scale-up of AI is not only the ultimate goal of enterprise transformation but also a core pillar for long-term operational resilience and innovation capability, with data management and data security playing a critical role. As a trusted partner in digital transformation, ASUS Cloud provides a high-security, highly compliant data management platform, helping enterprises advance their AI journey while ensuring data protection and accelerating AI application development.
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Reference sources:
- iThome: https://www.ithome.com.tw/pr/171218
- https://www.find.org.tw/indus_trend/browse/5c0e03073ccc40f0f439e51c294a851d
- 2025 Taiwan Artificial Intelligence Conference: “Agentic AI Driving Enterprises Toward Scalable AI: Strategies, Implementation Methods, and Key Challenges”