Over the past year, adoption of generative AI has grown significantly across industry domains and functions, such has customer operations, marketing and sales, software engineering, and research and development. In a world where 50 percent of business or organizational decisions could be taken by AI, companies must work hard to build trust.

Generative AI is pervading organizations. According to our latest research, 97 percent of global organizations allow employees to use generative AI in some capacity. While large language models and agentic AI systems shows incredible potential, questions arise about bias in their training data and the robustness of safety constraints.

The rise of foundation models comes with associated trust issues. And neglecting to address them leads to financial losses and business risks.

Incoherent execution of evaluation and tests, lack of content monitoring, or no consistent benchmarks can lead to untrustworthy solutions.

To be able to run GenAI at scale effectively, organizations need to envision guardrails from an operational, rather than solution-specific, perspective while ensuring setting a specific framework to business context and domain.

“Trust is bigger than one question, it’s a multi-dimensional problem, so you need to think about trust in a specific context.”