Strategy in motion: Internal audit’s 2026 execution mandate

Internal audit has arrived at a definitive turning point in 2026, transitioning from a period of technological experimentation to a phase of mandatory, disciplined execution. As evidenced by the insights from the IIA Great Audit Minds (GAM) conference in March 2026, the profession is experiencing its tipping point where advances in agentic artificial intelligence (AI) and autonomous workflows are no longer theoretical possibilities but operational necessities. For boards and executive management, the focus has shifted from "AI intentions" to tangible "AI impact," requiring a fundamental reassessment of how risk is monitored and how assurance is delivered. 

In this volatile landscape, internal audit leaders must navigate a dual reality: the rapid acceleration of technology and a persistent "competency gap" in their teams. Success in this era will not be defined by the volume of tools purchased, but by the ability of the chief audit executive (CAE) to cultivate a culture of innovation while maintaining rigorous human accountability. As a trusted advisor, Baker Tilly’s risk advisory and internal audit practice synthesizes the following key takeaways for leadership to consider as they redefine their assurance strategies. 

1. Moving beyond AI pilots to autonomous execution 

• What we heard: 2025 was a year of experimentation, but 2026 is the year of execution. The profession is shifting from task automation to agentic AI, autonomous software agents that can independently plan and execute complex audit programs with minimal human intervention. 
• Why it matters: Despite high investment, 95% of generative AI projects are currently failing to scale because they lack integration into core workflows. For organizations in highly regulated sectors like financial services, the inability to move from "impressive demos" to "invisible, indispensable tools" represents a significant loss of ROI and competitive edge. 
• What internal audit leaders should consider now: Rather than aiming for broad automation, identify a single, high-volume, rule-based process such as disclosure consistency checks or full-population transaction testing and run a parallel pilot to prove measurable time-savings and error detection rates.