AI in IFRS 16 lease accounting: Why control must come before automation

Summary

AI can help finance teams capture lease data, review contracts and flag exceptions. But in IFRS 16, automation only creates value when the underlying process is controlled. Clean data, clear ownership, human review and audit trails should come before AI is added to lease accounting workflows. 

Introduction

Finance teams are looking at AI in IFRS 16 lease accounting because the workload is repetitive, detailed and document-heavy. Lease contracts, amendments, payment schedules, index clauses and approval notes all contain information that can affect the lease liability, right-of-use asset, depreciation, interest and disclosures.

The opportunity is real. AI can help teams read contracts faster, identify missing fields, compare extracted terms against the lease register and highlight unusual movements before month-end or year-end close. Used well, AI can reduce manual work and improve consistency.

But IFRS 16 is not just a data extraction exercise. The IFRS Foundation describes IFRS 16 as the standard for recognition, measurement, presentation and disclosure of leases. That means finance teams still need judgement, review and evidence. AI can support the process, but it does not replace accountability.

What AI in IFRS 16 means in practice

AI in IFRS 16 lease accounting means using technology to assist with initial contract review, data capture, validation and exception handling. It does not mean letting a system make material accounting judgements or post entries without finance oversight.

Useful examples include identifying potential leases in supplier agreements, extracting commencement dates, lease terms, payment amounts, indexation clauses and renewal options, and flagging gaps or duplicate records. AI can also compare contract data with the lease register and alert finance when something looks inconsistent.

The safest model is assistive AI: the system proposes, finance reviews and an accountable person approves. This keeps professional judgement in the process while reducing manual keying and improving visibility.

Read more: IFRS 16 implementation: Step-by-step guide for lease accounting compliance.

Why control must come before automation

AI should sit on top of a controlled IFRS 16 process, not replace one. If lease data is incomplete, duplicated or stored across local spreadsheets, AI may simply process poor data faster. If ownership is unclear, automation can make it harder to know who approved what.

Karl Oscar Rosli, Lease Accounting Expert & Head of Product Marketing Management at House of Control, has spent the last five years working closely with customers on lease accounting and contract management. In his view, AI readiness starts with control:

“AI can help finance teams move faster, but it cannot compensate for unclear ownership, weak lease data or missing evidence. The teams that will benefit most from AI are the ones that first build a controlled IFRS 16 process.”

— Karl Oscar Rosli, Lease Accounting Expert & Head of Product Marketing Management, House of Control

Before introducing AI, finance teams should have a complete lease register, standard data fields, source documents, approval workflows, access controls and a reliable audit trail. Every material change should be timestamped, attributable and linked back to the contract, calculation and approval.

Read more: The impact of IFRS 16 on 12 different financial ratios.

Where AI can add value with the right controls

AI works best when it supports high-volume tasks where the output can be reviewed against source evidence. Good starting points include:

• Contract review and potential-lease detection.
• Clause extraction with confidence scores.
• Duplicate, missing-data and gap checks.
• Alerts for possible remeasurements or modifications.
• Cross-period consistency checks for disclosures.
• Exception queues for finance review.

These use cases improve control because they help finance teams find what needs attention. They do not remove the need for review. For audit purposes, reviewers should be able to see what the AI extracted, where it found the information and whether the result was approved or corrected.

What to use with caution

Some IFRS 16 areas should remain tightly controlled because they involve judgement or material impact. Lease term assessments, discount rate methodology, modification treatment, impairment indicators and journal postings should not be fully automated without clear rules, review and sign-off.

AI can support these areas by preparing suggestions or highlighting exceptions, but finance, treasury or group reporting should own the final decision. The more material the impact, the stronger the review should be.

This approach is consistent with broader AI governance principles. The OECD AI Principles highlight trustworthy AI, transparency, robustness and accountability. For finance teams, those ideas translate into practical controls: human oversight, documented evidence, clear approval rights and ongoing monitoring of errors and exceptions.

Read more: IFRS 16: Simplifying the calculation and reconciliation of lease liabilities and right-of-use assets.

A practical roadmap for finance teams

A controlled AI roadmap does not need to start with a large transformation. It can begin with five practical steps:

1. Assess lease data completeness and remove duplicates.
2. Standardise the key fields used in calculations and disclosures.
3. Define who reviews and approves judgement-based inputs.
4. Build an audit trail for extracted data, changes and approvals.
5. Pilot AI on one narrow use case before scaling.

A good first pilot might be extracting payment clauses from new contracts or checking for missing lease data in one entity. The scope should be small enough to review properly and measure clearly. Success metrics can include extracted-field accuracy, exception rate, approval time, rework and audit findings.

FAQ

Can AI replace IFRS 16 judgement?

No. AI can support data extraction, validation and review, but accounting judgements should remain owned by finance.

What should finance teams fix before using AI?

Start with the lease register, source documents, data quality, approval workflows and audit trail. AI cannot improve compliance if it is working from outdated, incomplete or poorly controlled information. These foundations make AI outputs easier to review and trust.

Conclusion

AI can make IFRS 16 lease accounting faster and more consistent, but control must come first. The best starting point is not full automation. It is a structured process with reliable data, clear ownership, human-in-the-loop review and evidence that stands up to audit.

When those foundations are in place, AI becomes more than a productivity tool. It becomes a way to strengthen visibility, reduce manual work and help finance teams focus on the exceptions and judgements that matter most.