Competitiveness and Governance in the Post-Generative AI Era

1. The Current State of AI Evolution and Corporate Utilization

The pace of AI's evolution in recent years has been nothing short of transformative. With the rise of generative AI and large language models (LLMs), systems are no longer confined to narrow tasks. They now generate natural dialogue, summarize and translate at scale, create images and audio, and even produce program code - capabilities once thought to be the exclusive domain of humans.

Some white-collar tasks that previously relied on human labor are now being streamlined and automated by AI, rapidly raising expectations for improved productivity and business transformation within companies.

A 2024 Japanese domestic survey found that while only 9.9% of companies had deployed generative AI in 2023, that number leapt to 25.8% just one year later (*2). Another survey showed that nearly nine out of ten companies using AI are already seeing measurable benefits (*3). Together, these findings mark a tipping point: AI has moved from pilot projects to full-scale deployment across industries.

Still, adoption is far from seamless. Accuracy fluctuations, algorithmic bias, and the risk of information leakage continue to hold companies back, especially in critical sectors such as healthcare and infrastructure, where errors carry severe societal consequences. The risks became tangible in 2023, when Samsung banned employee use of ChatGPT after an internal code leak raised concerns about sensitive data being exposed to public models (*4). Around the same time, Italy's data protection regulator temporarily blocked ChatGPT over GDPR violations, citing transparency and privacy concerns (*5). These instances showed just how quickly enthusiasm for generative AI could collide with legal, ethical, and security risks.

In response, organizations are beginning to recalibrate, seeking a balance between opportunity and control. By 2024, more than half of companies had already started to establish AI utilization policies and risk management frameworks, introducing clearer checkpoints and audit mechanisms to govern business applications (*3).

In this new phase, decision-making is less about "whether" to adopt AI, and more about portfolio strategy: which AI systems to deploy, under what governance structures, and with what safeguards in place.

2. Business Innovation Driven by the Democratization of Technology

The rise of AI has accelerated the democratization of technology. It has put powerful capabilities, once reserved for specialists, into the hands of a much broader workforce. What used to require advanced training or coding expertise is now accessible through conversational AI interfaces. Employees in sales, marketing, or planning roles can run data analyses or generate summaries in a matter of minutes. These were tasks that once demanded dedicated technical staff. The result is a wave of business innovation that cuts across departmental and role boundaries.

In practice, the impact is already visible. In software development, AI is automating code generation and testing. On the factory floor, it is spotting anomalies and enabling predictive maintenance. In the back office, it is streamlining white-collar processes through automation that goes far beyond traditional RPA. By lowering the technical barrier to entry, AI ensures more equitable access to knowledge and skills. This not only boosts individual productivity and sparks new ideas but also decentralizes AI use across the organization, making it easier for insights from the front lines to shape business strategy.

3. Major Risks in AI Utilization and the Need for Control

However, democratization comes with risks. Because powerful AI tools can be used without deep IT knowledge, the margin for error grows. Without proper guardrails, mistakes can scale just as quickly as successes. Put simply: if anyone can use AI, anyone can misuse it. That makes governance more crucial than ever.

Recognizing and managing these risks is the foundation for safe and sustainable adoption. Five of the most pressing challenges include:

1. Hallucination
   - AI systems can generate content that appears convincing but is factually incorrect, leading to poor decisions or the spread of misinformation.
2. Information Leakage
   - Sensitive corporate or personal data may inadvertently be exposed if it is entered into AI tools without safeguards.
3. Prompt Injection
   - Malicious prompts or hidden instructions can manipulate models into disclosing confidential information or behaving unpredictably.
4. Lack of User Literacy
   - Employees who use AI without understanding its limitations risk misinterpreting outputs or overestimating accuracy.
5. Copyright and Privacy Infringement
   - AI may reproduce copyrighted material or personal data without consent, raising legal and ethical concerns.

The severity of these risks grows with the scope of deployment. While an error in an individual productivity tool may be inconvenient, failures in enterprise systems or customer-facing services can trigger financial, reputational, or even regulatory crises.

Figure 1: Relationship Between AI Implementation Level and Risk

As shown above, risk factors are diverse, but they can be mitigated through proper control. Yet the answer is not to halt adoption. Instead, companies must develop mechanisms to keep risks within acceptable bounds. Practical measures include human review of AI outputs, clear policies prohibiting the input of sensitive data, and a whitelist of vetted AI tools approved for internal use.

Governments and industry bodies are also working to standardize these practices. In the United States, for example, the AI Risk Management Framework (AI RMF) was developed through collaboration between public and private sectors, encouraging organizations to adopt voluntary but robust safeguards.

Ultimately, AI risk management is not a technical afterthought but a core management responsibility. Effective governance is the prerequisite for safe, secure, and value-driven AI utilization.

4. Global Trends in AI Regulation and Governance

Figure 2: International Comparison of Major Legal Frameworks Related to AI

Across the globe, governments are racing to shape the rules of AI. Nowhere is this more evident than in Europe, where the AI Act, passed in 2024, introduces one of the most comprehensive regulatory frameworks to date. Beginning in 2025, it will be phased in across member states.

At the heart of the Act is a risk-based classification system (see Figure 3). AI applications are divided into four categories: unacceptable risk, high risk, limited risk, and minimal risk. Systems deemed to carry "unacceptable risk", for example, those that threaten human rights, are banned outright. High-risk systems face strict obligations around testing, transparency, and human oversight.

Figure 3: Four Risk Classifications of AI Systems in the AI Act (A Risk-Based Approach)
- Source: European Commission Website "Shaping Europe's Digital Future"

Generative AI is not left out. Providers must ensure outputs are clearly labelled as machine-generated, and disclose whether copyrighted works were used in training or results. The enforcement teeth are sharp: companies that fail to comply face fines of up to 7% of global turnover or €35 million (*6). Even businesses headquartered outside the EU fall under these rules if they sell AI systems into the European market.

The EU's approach sets a high bar, combining sweeping ambition with extraterritorial reach. For global companies, it represents a signal that AI governance will increasingly shape competitiveness.

5. The Role of Management and Key Principles for Building an AI Governance Framework

Safe and effective AI use begins with leadership. In the era of democratized AI, and especially with generative AI, implementation has become a core management decision. Without a clear strategy from the top, uncertainty spreads through the organization. Teams hesitate, opportunities are lost, and productivity gains are left unrealized.

The responsibility of management is therefore twofold: to understand both the potential and risks of AI, and to define what level of risk is acceptable. This sets the foundation for clear usage policies and rules that must cascade through the organization. Policies might prohibit the input of sensitive information, establish review processes for AI-generated outputs, or define approved lists of tools. To embed these practices, some companies are already creating cross-functional units such as AI Governance Offices or Centers of Excellence (CoE) to provide continuous oversight.

However, governance should not be seen purely as defense. Management must also frame AI as an offensive strategy - a lever to enhance innovation, competitiveness, and growth. Building such a framework requires coordination across risk management, legal, security, and business strategy teams.

6. Essentials for Establishing Responsible AI Utilization

As AI becomes central to corporate competitiveness, responsible utilization grounded in ethics will determine which companies win trust from markets, regulators, and society. The following seven essentials (see Figure 4) provide a foundation for building that trust and ensuring sustainable growth.

Figure 4: Seven Essentials That Companies and Organizations Should Implement for Responsible AI Utilization

The companies that succeed will not be those that hold back out of fear, but those that move forward with governance and ethics as the backbone of their AI strategies. By investing in robust governance frameworks, organizations can capture medium- to long-term growth while minimizing exposure to major risks. In return, they strengthen their credibility with stakeholders across the market, society, and regulatory authorities.

When risks are managed effectively, AI becomes becomes a strategic weapon. In the post-generative AI era, the role of management is clear: to balance offense (innovation and competitiveness) with defense (risk control and accountability). Companies that achieve this balance will not only sharpen their competitive edge but also fulfil their broader social responsibility.

7. Conclusion

Global trends in AI governance highlight both the extraordinary potential of AI to drive productivity and the serious risks it carries in terms of security and ethics. For management, the task is not to treat these risks as constraints, but to harness them as catalysts - springboards for stronger governance and more confident adoption.

The pace of technological progress and regulatory change leaves no room for complacency. Now is the moment to reassess your company's AI governance framework and take decisive steps to strengthen it.

In the post-generative AI era, the real winners will be those organizations that adapt quickly, calibrate risk with clarity, and embrace bold innovation underpinned by trust. They are the companies that will set the standard for responsible leadership in the age of AI.