The increasing use of automated and algorithm-supported processes does not reduce the need for assurance. On the contrary, it raises expectations around oversight, control, and explainability. For boards, the key question is not whether AI is being used, but whether its use is properly governed, controlled, and auditable.
The Board’s Role Has Become More Demanding, Not Less
Boards are ultimately accountable for the integrity of financial reporting, risk management, and internal control. As systems become more complex, accountability cannot be shifted to technology providers, models, or automated logic.
AI and automation introduce new dimensions of risk:
- reliance on complex data flows
- embedded decision rules that are not always visible
- evolving models and assumptions
- increased risk of override or inappropriate reliance on outputs
These are governance issues first, not technology issues, and they require active board oversight.
What Boards and Audit Committees Must Demand
1. Clear Ownership and Accountability
Boards should expect management to clearly articulate who owns AI-influenced processes, decisions, and outputs. Accountability must remain explicit at the management level, with no ambiguity about responsibility for results.
2. Explainability of Outcomes
If a material judgement, estimate, or classification is influenced by automated or AI-supported processes, management must be able to explain:
- how the output was generated
- what data was used
- what assumptions or rules were applied
- why the result is reasonable
If an outcome cannot be explained clearly and consistently, it cannot be relied upon.
3. Strong Governance and Controls
Boards should seek assurance that appropriate governance frameworks exist, including:
- documented approval and oversight of automated processes
- robust access controls and segregation of duties
- controlled change management over models, rules, and configurations
- monitoring mechanisms to identify errors, bias, or unintended outcomes
Weak governance inevitably translates into higher assurance risk.
4. Data Integrity and Traceability
Reliable reporting depends on reliable data. Boards should expect visibility over:
- data sources and interfaces
- key transformations between source systems and reporting outputs
- reconciliations and completeness checks
- controls over data quality
Where data lineage is unclear, confidence in assurance is reduced.
5. Guardrails Against Over-Reliance
Boards must challenge any perception that automated outputs are inherently more reliable than traditional processes. Professional judgement, review, and challenge remain essential, particularly for areas involving estimation, uncertainty, or management bias.
The Role of Audit: Independent Verification, Not Automation
Audit’s role is not to endorse technology, nor to rely on system outputs without challenge. Audit provides independent verification that governance, controls, and evidence are sufficient to support reliance.
As environments become more automated, audit focus shifts toward:
- evaluating system-embedded controls
- testing data integrity and interfaces
- assessing governance over change and access
- validating the consistency and reasonableness of outputs
This does not reduce audit effort, it changes where effort is applied and raises the bar for audit quality.
Why This Matters in the Saudi Context
Saudi Arabia’s regulatory environment continues to mature, with increasing expectations around transparency, accountability, and financial integrity in line with Vision 2030. Boards and audit committees are expected to demonstrate active oversight, particularly where complexity increases.
In this environment, the use of AI and advanced systems heightens rather than diminishes the importance of strong assurance. Regulators, investors, and stakeholders will continue to look to boards for confidence that reporting outcomes are robust, explainable, and properly governed.
