All 2026 Conference Sessions

Audit 2030: Key Forces Shaping the Future of Internal Audit

Speakers:

• Stephen Osborne, Sr. Director Analyst, Gartner

Key takeaways

• To stay effective, internal audit must adapt by prioritizing regulatory risk, embedding AI in audit processes and developing multidisciplinary skills for a hybrid human-AI environment.
• Global regulatory fragmentation is increasing compliance costs and driving the need for localized technology and governance.
• AI adoption is reshaping audit, shifting the focus from technology deployment to proving strategic alignment, value realization and trusted outcomes.
• Internal audit teams must prioritize analytical thinking, judgment, problem-solving and professional skepticism over technology credentials alone.
• Audit leaders must embed intelligent automation into core workflows to effectively evaluate increasingly automated enterprise system

No Plan, No Payoff — AI Business Impact Demands a Clear Strategy

Speakers:

•  Eren Fry, Sr. Director Analyst, Gartner

Key takeaways

• A well-articulated AI strategy is essential for translating AI investments into measurable business impact.

• AI plans for audit should start by aligning to how AI can enable achievement of overall department objectives.

• CAEs should define the level of impact they want to see from AI (optimization or transformation) then rigorously prioritize use cases to pilot based on value and feasibility criteria.

• Plans should include a focus on change management and AI skills development to increase adoption rates and increase confidence in AI outputs.

• Ongoing measurement and refinement are necessary to ensure AI initiatives deliver on business objectives and enable ongoing updates to plans.

Enhance ERM Risk Analysis With GenAI

Speakers:

•  Alex Ossington, Director Analyst, Gartner

Key takeaways

• As GenAI plays an ever-growing role as an organization’s “wisdom aggregator” ERM must evolve from aggregating information to generating insights for executive leaders and the board to deliver distinctive value.

• GenAI can support the full risk-analysis process, including risk evaluation, scenario development, interdependency mapping, emerging-risk detection and root-cause analysis.

• ERM teams should use this capability to synthesize information from multiple sources, develop preliminary risk ratings and highlight areas where further evidence or leadership judgment is required.

• To get started, ERM leaders should target one manual bottleneck for GenAI experimentation and use it to develop the technical and cognitive skills required to evaluate and refine AI-generated outputs within their teams, as well as building a prompt library to encourage wider use.

AI in Risk Intelligence Tools: Promising, Powerful But Proceed with Caution

Speakers:

• Nick Sworek, Senior Director Analyst, Gartner

Key takeaways

• For relatively simple activities, proven forms of AI may deliver the desired result more reliably and at lower cost. At the other end of the spectrum, today’s agents may not be ready to manage highly complex, autonomous workflows.
• Agentic AI applications are largely longer-term opportunities for assurance functions. Uses such as autonomous third-party risk management, self-driving GRC orchestration and AI-powered digital twins for risk simulation are promising, but most functions should currently classify them as moonshots rather than immediate priorities.
• Anomaly detection, automated risk scoring, workflow assistance, continuous control monitoring, automated evidence collection and policy and document review are among the more mature AI applications available to assurance leaders today.
• By improving processes, data quality and organizational readiness while adopting more mature AI capabilities, assurance teams can capture near-term value and build the foundations needed for greater autonomy in the future.

Architecting Your Approach to Navigating Ethical AI Dilemmas

Speakers:

• Kelly Larmon, Director Analyst, Gartner

Key takeaways

• Ethical dilemmas from AI use differ from traditional compliance questions because often they require choosing between legitimate priorities.

• For example, an AI pricing tool recommends charging some customers more  
  based on predicted willingness to pay.  Should the employee prioritize revenue over fairness?

• Many organizations have already built a strong ethical foundation by establishing acceptable use policies and adopting responsible AI principles, but without clear guidance, employees are left to interpret how to apply the principles themselves, increasing the risk of inconsistent and poorly supported decisions. 

• CCOEs are well positioned to help employees apply principles to practice because they already manage communication channels, training programs, governance forums and escalation processes.
   

Reimagining the Compliance Operating Model for Speed, Insight and Impact

Speakers:

• April Lawrence, Director Analyst, Gartne

Key takeaways

• The pace of regulatory, technological and business change requires compliance to move beyond static controls and monitoring.

• Compliance teams should adopt continuous learning and adaptation, using data and feedback to refine guidance and controls.

• Shifting from reactive risk data to predictive signals helps organizations get ahead of emerging risks.

• Empowering the business with self-service tools and tailored guidance makes compliance a more valuable partner.

• Demonstrating outcomes and impact, rather than just activity, builds credibility and positions compliance as a strategic function