How AI governance can strengthen operational resilience

Organizations are investing in AI, but value depends on governance. Strengthen accountability, reduce risk and improve resilience.

In brief

• Organizations that align AI governance with business objectives, risk management and operational execution are better positioned to scale AI confidently.
• Effective governance improves visibility into AI investments, strengthens oversight and helps leaders respond more quickly to emerging risks.
• Trusted data, integrated risk management and clear accountability can strengthen operational resilience while supporting innovation.

Why AI governance is becoming an operational resilience requirement

Operational resilience is the ability to anticipate, withstand, respond to and recover from disruption while continuing to deliver critical business services.

Historically, organizations have focused resilience efforts on technology outages, cybersecurity incidents, supply chain disruptions and regulatory events. Today, AI introduces a new layer of complexity.

AI models are increasingly influencing decisions, automating processes and interacting with critical business data. As a result, organizations must understand not only where AI is deployed but also how AI affects operational workflows, risk management processes, controls, third-party relationships and business outcomes.

Operational resilience is increasingly about understanding how critical business services depend on technology, data, third parties, controls and decision-making processes, including those augmented by AI.

Organizations that fail to establish visibility and accountability across these dependencies may find it more difficult to identify emerging issues, assess potential impacts and respond effectively when disruption occurs.

How rapid AI adoption can create operational and financial risk

Many organizations are moving quickly to deploy AI capabilities across business units, functions and geographies. While this can accelerate innovation, it can also create fragmentation.

Different teams may acquire AI tools independently, implement inconsistent governance practices or rely on data sources that lack sufficient oversight.

The result is often widespread AI adoption that outpaces governance.

Examples may include:

• AI tools embedded in critical workflows without clear ownership
• Duplicate AI investments across business units
• Inconsistent testing, monitoring and human oversight
• Poor-quality data supporting AI-enabled decisions
• Limited visibility into third-party AI dependencies
• Challenges demonstrating controls to auditors, regulators and stakeholders

Left unmanaged, these issues can increase operational risk, create unnecessary cost, reduce confidence in AI outputs and make organizations less resilient during periods of disruption.

Strong governance provides the structure needed to scale AI while maintaining consistency, accountability and visibility.

How AI governance strengthens operational resilience

Many organizations view AI governance primarily through a risk or compliance lens. While oversight remains important, governance can create broader business value when embedded within enterprise operations.

Effective AI governance helps organizations answer critical questions:

• Where is AI being used?
• What data supports AI-enabled decisions?
• Who is accountable for outcomes?
• What controls are in place?
• How are risks monitored and escalated?
• How can leaders respond when issues arise?

The ability to answer these questions is fundamental to operational resilience.

By creating transparency across AI systems, workflows and decision-making processes, governance enables organizations to identify risks earlier, accelerate response efforts and strengthen confidence in AI-enabled operations. Organizations are increasingly looking to integrated workflow and risk management platforms, such as ServiceNow, to help connect governance, risk and operational processes across the enterprise, improving visibility, accountability and decision-making.

Frontier AI raises the resilience stakes

As advanced AI models become more capable, autonomous and embedded across critical business services, organizations may encounter risks that are harder to predict through traditional testing and oversight approaches alone. Effective governance must therefore evolve alongside these capabilities by monitoring changes in model behavior, constraining high-impact actions, assessing concentration and systemic dependencies, and maintaining appropriate human intervention and fallback procedures. These capabilities can help organizations scale AI responsibly while preserving operational resilience in increasingly complex environments.

What CFOs, CIOs and CROs need from AI governance

As AI becomes a more significant enterprise capability, executive leaders have a shared responsibility for ensuring it delivers value while operating within acceptable risk parameters.

Who owns AI governance?

Operational resilience requires shared accountability. While governance responsibilities vary across organizations, successful AI programs often include clearly defined ownership:

• CFOs drive investment discipline, value realization and capital allocation.
• CIOs enable technology architecture, data foundations and operational scalability.
• CROs oversee risk management, controls, monitoring and resilience.
• Business leaders remain accountable for operational outcomes and the responsible use of AI within the functions they oversee.

When responsibilities are clearly defined, organizations can make decisions faster, improve oversight and respond more effectively to emerging risks.

Why trusted data and risk management are critical to AI governance

AI governance is only as effective as the data and risk management practices supporting it.

Many organizations continue to struggle with fragmented data environments, inconsistent reporting structures and disconnected risk management processes. These challenges can undermine confidence in AI-enabled decisions and limit visibility into emerging threats.

Trusted data helps support AI systems operating on information that is accurate, reliable and appropriately governed. Integrated risk management helps organizations connect technology risk, operational risk, third-party risk, compliance requirements and business objectives into a more cohesive framework. Platforms such as ServiceNow can help organizations bring together risk data, workflows and controls to improve visibility and support more effective governance at scale.

Together, these capabilities provide leaders with a more complete view of how AI impacts critical business operations and business performance.

How AI governance creates business value

Effective AI governance should help create business value, not slow innovation. Organizations that successfully connect governance, risk management and operational execution can achieve meaningful outcomes, including:

• Greater visibility into AI investments and value realization
• Reduced duplicate AI spend and technology overlap
• Improved control effectiveness across critical workflows
• Better visibility into third-party AI dependencies
• Faster escalation and remediation of issues
• Increased confidence in AI-enabled decisions
• Stronger regulatory defensibility
• More resilient business operations

The goal is not to introduce additional bureaucracy. The goal is to create a disciplined approach that enables organizations to innovate while reducing avoidable risk.

AI governance example: improving third-party risk management

Consider a global organization using AI to support third-party risk monitoring.

Without integrated governance, leaders may have limited visibility into which models are being used, what data supports them, how decisions are made and who is accountable for outcomes. With connected workflows, trusted data and clear ownership, AI can help identify supplier risk signals earlier, prioritize response actions and improve decision-making across critical operations.

Governance ensures the organization understands how AI supports those decisions, what controls are in place and how issues are escalated when concerns arise. The result is stronger operational resilience, improved accountability and greater confidence in business decisions.

How organizations can scale AI responsibly while strengthening resilience

As organizations continue embedding AI into critical business operations, governance must evolve beyond compliance and oversight. Organizations that connect AI governance with trusted data, integrated risk management and workflow execution are better positioned to scale AI responsibly, direct investment toward the right opportunities and manage risk across critical operations.

In doing so, they can strengthen resilience, improve decision-making and create greater confidence in their ability to respond effectively when disruption occurs.

AI governance is no longer simply about managing risk.

It is increasingly about helping organizations contribute to business objectives, protect critical operations, improve investment discipline, strengthen control effectiveness and build resilience in an AI-powered future.

As AI technologies continue to evolve, organizations that establish strong governance foundations today will be better positioned to capture the benefits of innovation while adapting to emerging risks and changing operational demands.

EY member firms and ServiceNow help organizations establish AI governance capabilities that connect risk management, operational resilience, trusted data and workflow execution. Together, EY and ServiceNow support organizations in scaling AI responsibly while improving visibility, accountability and business outcomes.