The future of controls

Modern controllership sits at the intersection of two powerful—and sometimes competing—forces.

On one side is the drive for operational excellence. Organizations are leveraging AI and automation to improve speed, accuracy, and scalability. Routine controls—once dependent on human intervention—can now be executed continuously, with far greater consistency. The result is not just efficiency, but also the ability to distribute talent toward higher-value work.

On the other side is an evolving risk landscape. AI introduces new challenges: model risk, data integrity concerns, and governance gaps, to name a few. These risks don’t behave like traditional ones. They are more dynamic, less transparent, and often harder to detect without the right tools.

The opportunity lies in resolving this tension between these sides. Leading organizations are not treating controls as a constraint on innovation. Instead, they are embedding controls directly into transformation initiatives, designing processes where risk mitigation is built in from the start, not bolted on after the fact.

Several trends are accelerating this shift—reshaping the function and redefining what “good” looks like in controls.

GenAI as a catalyst

Generative AI (GenAI) is moving quickly from experimentation to embedded capability. Use cases are already emerging across documentation, control monitoring, and advanced analytics. Tasks that once required hours of manual effort—such as drafting narratives or analyzing anomalies—can now be completed in minutes.

Dynamic risk assessment

Periodic risk assessments are giving way to continuous evaluation. With access to real-time data and AI-driven insights, organizations can identify and respond to risks as they emerge—not weeks or months later.

Continuous monitoring and digitized controls

The traditional model of sample-based testing is being replaced by full-population analysis. Automated controls can now execute and validate themselves continuously, significantly increasing both coverage and assurance.

Platforming and technology enablement

Controls are increasingly integrated across systems and platforms, creating more cohesive control environments. Organizations are also rethinking control architectures—balancing centralized oversight with federated execution.

The future workforce

As controls evolve, so do the people behind them. The skill set is shifting toward data literacy, analytics, and AI fluency. Roles within risk and control teams are being redefined—from operators of controls to designers and interpreters of intelligent systems.

The GenAI trend is harnessing some of the most excitement in the controllership landscape, as well as the most apprehension. And while this interest is widespread, realizing its full value requires a structured approach.

Structure your approach

Optimize the process
The starting point is often straightforward: Target manual, repetitive tasks. Early wins include summarization, documentation, and basic analytics. These use cases are relatively easy to implement and can deliver immediate efficiency gains.

Transform the process
The next step is more ambitious. AI enables organizations to rethink entire processes end to end—eliminating inefficiencies, unlocking value from existing data, and creating more seamless workflows.

Reimagine the function
At the highest level, organizations begin to rethink the role of controllership itself. Controls evolve into a source of real-time insight and decision support, rather than a backward-looking compliance function.

Success requires a balanced portfolio—combining quick wins with longer-term transformation bets and aligning ambition with organizational readiness.

Prioritize the right use cases

Not all AI use cases are created equal. The challenge is knowing where to focus.

A value-versus-complexity lens can help organizations identify high-impact, feasible opportunities. The most promising use cases typically share a few characteristics: high transaction volumes, manual and repetitive processes, and strong data availability.

At the same time, there are common pitfalls to avoid. Over engineering low-value processes can dilute impact, while ignoring underlying data quality issues can undermine even the most sophisticated solutions.

The goal is not to do everything. It’s to do the right things first.

Measuring ROI: Making the business case for AI in controls

Building momentum requires a clear, credible business case.

The value of AI in controls extends beyond simple cost reduction. While efficiency gains are important, organizations are also seeing increased assurance, broader control coverage, and meaningful reductions in error rates and risk exposure.

Implementation considerations matter just as much. Scalability, reusability, and process stability all influence the long-term return on investment.

Perhaps most importantly, leading organizations are expanding the conversation beyond cost savings. The strategic value of better insights, faster decision-making, and stronger governance often proves to be the more compelling driver.

Establishing robust AI governance

As AI becomes more embedded in controls, governance becomes non-negotiable.

Effective governance starts with clear objectives, strong executive sponsorship, and well-defined roles and responsibilities. Ethical frameworks and accountability mechanisms are equally critical.

Controls must also be embedded across the AI life cycle—from design and data through development, implementation, and ongoing monitoring. Both preventive and detective controls play a role at each stage. And governance cannot be static. Continuous monitoring, model validation, and performance tracking are essential, particularly as AI capabilities and the associated risks continue to evolve.

Implications for external audit and assurance

The rise of AI is also reshaping expectations for audit and assurance. Auditors are increasingly focused on understanding how AI is used in financial reporting and control environments. This includes evaluating how AI influences risk assessments and audit procedures.

For management, several key questions should come into focus:

• Where is human oversight maintained?
• How are AI-driven decisions controlled and validated?
• What safeguards exist to prevent unauthorized changes?
  

Looking ahead, audit itself is becoming more technology-enabled, with greater reliance on continuous assurance and increased scrutiny of AI governance and controls.

Practical steps to get started

For organizations looking to move forward, a few practical actions can help build momentum toward AI transformation of the controls landscape:

• Conduct a controls and process maturity assessment to understand the current baseline
• Identify and prioritize high-value AI use cases
• Establish governance frameworks early, rather than retrofitting them later
• Invest in talent and upskilling to build the necessary capabilities
• Pilot, iterate, and scale—treating transformation as a journey, not a one-time initiative

Shifting from compliance to value creation

The future of controls is not just about keeping pace with change. It’s about redefining the role controls play in the organization. It is about shifting from compliance to value creation.

As AI and digital technologies continue to advance, the most successful organizations will likely be those that strike the right balance between innovation and trust. Done right, controls will no longer sit on the sidelines as a compliance function. They will become a core enabler of performance, insight, and competitive advantage. Modernizing controls alongside AI adoption won’t just mitigate risk—it can help position organizations to lead.