Scaled Agile – Responsible AI

Major enterprise technology surveys are reporting a dramatic shift in how leaders view Responsible AI: more than 70% of large companies now list AI risk as a material business risk, driven by real deployment issues and increased scrutiny from regulators and customers.

Source: TechRepublic, “AI Adoption Trends in the Enterprise 2026”, published January 6, 2026.

This isn’t just rhetoric. It shows up in spend, governance changes, and organizational structures. Leaders are establishing cross-functional AI councils, formal risk-rating processes, and mechanisms to monitor automated decisions in live business operations. These are not “ethics theater” gestures; they are responses to hard lessons learned when unchecked AI led to biased decisions, miscommunication with customers, or brand harm.

Responsible AI has matured from a philosophical conversation to an operational imperative. The focus has shifted from “What should we intend?” to “How do we ensure our systems behave as expected day after day?” That means robust testing, real-world evaluations, and clear accountability for outcomes.

The companies that embrace this shift are not slowing innovation — they are scaling it with confidence. Responsible AI is no longer a constraint. It’s a risk-mitigation strategy and trust enabler that sets industry leaders apart.