In 2026, AI adoption will continue to rise, bringing both opportunities and challenges. Organizations face issues such as outdated infrastructure and lack of expertise, leading to concerns about AI agents creating more problems than they solve.
As the August 2026 deadline for the EU AI Act approaches, CX leaders must move from theory to action by ending non-compliant AI projects. Emphasizing transparency and governance is essential as organizations adapt to new regulations and build trust with customers.
New research reveals critical gaps between AI experimentation and associated risks as enterprise AI adoption accelerates across business workflows. The report highlights the need for enhanced visibility and control over data usage to bridge the divide between innovation and trust.
Taiwan has enacted the AI Basic Act to establish a regulatory framework for artificial intelligence, aiming to foster innovation, accountability, transparency, and data governance. The act defines responsibilities for the government and private enterprises while promoting an internationally interoperable AI ecosystem.
Vietnam’s first standalone AI law, enacted on December 10, 2025, promotes innovation while safeguarding human rights and national security through risk-based management and accountability across the AI supply chain.
Most legal teams easily experiment with AI but face challenges operationalizing it. Adoption occurs when consistent, repeatable results enable teams to explain successes and scale AI-driven workflows confidently.
California’s new regulations on automated decision systems (ADS) require AI hiring tools to comply with anti-discrimination laws and reinforce employer accountability. A recent class action against Eightfold AI highlights concerns over transparency and consumer-reporting obligations in AI-driven hiring.
On January 29, 2026, the third AI Policy to Practice Lab in Brussels focused on the critical role of people and skills in AI diffusion. Discussions covered funding, AI literacy, operational governance, and the need for aligned incentives between governments and the private sector to support continuous learning and trust in AI technologies.
As enthusiasm for artificial intelligence (AI) grows, concerns arise over ethical implications including bias, environmental impact, education, and job displacement.
Proposed EU rules for labeling AI deepfakes are causing divisions among experts, with companies required to watermark AI-generated content to prevent deception. The draft favors civil society’s call for broad labeling, while industry voices concerns over excessive labeling and its effect on warnings.
