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Controller

Financial Reporting & Compliance

Enhances◐ 1–3 years

What You Do Today

Prepare GAAP/IFRS-compliant financial statements, footnotes, and regulatory filings. Ensure consistency, accuracy, and compliance with evolving accounting standards.

AI That Applies

AI-assisted disclosure drafting that generates footnotes from underlying data, checks consistency across periods, and flags areas requiring updated disclosure.

Technologies

How It Works

The system ingests underlying data as its primary data source. NLP models process the text input by identifying entities, classifying intent, and extracting the structured information needed for downstream decisions. The output — footnotes from underlying data — surfaces in the existing workflow where the practitioner can review and act on it.

What Changes

Footnote drafts generate from system data. AI identifies inconsistencies between financial statements and disclosures before external review.

What Stays

Accounting policy decisions. How to apply a new standard, when to change an estimate, and what to disclose requires professional judgment.

What To Do Next

This section won't tell you what your numbers should be. It will show you how to find them yourself. Every instruction below produces a real, verifiable result in your organization. No benchmarks, no projections — just the steps to build your own evidence.

1

Establish Your Baseline

Know where you are before you move

Before adopting AI tools for financial reporting & compliance, understand your current state.

Map your current process: Document how financial reporting & compliance works today — who does what, how long it takes, where the bottlenecks are. You need this baseline to measure improvement.
Identify the judgment points: Accounting policy decisions. These are the boundaries AI won't cross.
Assess your data readiness: AI tools for this area need data to work. Check whether your organization has the historical data, integrations, and data quality to support Natural Language Generation tools.

Without a baseline, you can't measure whether AI actually improved anything. You'll adopt tools without knowing if they're working.

2

Define Your Measures

What to track and how to calculate it

Time per cycle

How to calculate

Measure how long financial reporting & compliance takes end-to-end today, then after AI adoption.

Why it matters

The most visible improvement is speed. If AI doesn't save time, question whether it's adding value.

Quality of output

How to calculate

Track error rates, rework frequency, or stakeholder satisfaction scores before and after.

Why it matters

Speed without quality is just faster mistakes. Measure both.

When to check: Check after 30 days of consistent use, then quarterly.
The commitment: Give new tools at least 30 days before judging. The first week is always awkward.
What NOT to measure: Don't measure AI adoption rate as a KPI. Adoption follows value — if the tool helps, people use it.
3

Start These Conversations

Who to talk to and what to ask

your CFO or VP Finance

Which of our current reports are manually assembled, and how much time does that take each cycle?

They're prioritizing which finance processes to automate first

your ERP or finance systems admin

What questions do stakeholders actually ask that our current reporting doesn't answer?

They know what automation capabilities exist in your current stack

your FP&A counterpart at a peer company

Which compliance checks are we doing manually that could be continuous and automated?

They can share what worked and what didn't in their AI rollout

4

Check Your Prerequisites

Confirm readiness before you invest

Check items as you confirm them.