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Finance Manager

Improve finance processes and drive automation

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What You Do Today

Identify manual, repetitive processes in your team's workflow and work with IT/operations to automate them. Build the case, manage the implementation, and measure results.

AI That Applies

Process mining for finance — AI maps how work actually flows, identifies bottlenecks and manual steps that are candidates for automation.

Technologies

How It Works

For improve finance processes and drive automation, the system identifies bottlenecks and manual steps that are candidates for automat. The processing layer applies the appropriate analytical models to the structured data, generating scored outputs that surface the most actionable insights. The results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context.

What Changes

You prioritize automation by impact: 'Automating intercompany reconciliation saves 80 hours/month. Automating expense accruals saves 20 hours/month. Start with intercompany.'

What Stays

Building the case for investment, managing the change, and ensuring the automation works correctly — technology is the tool, you're the driver.

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 improve finance processes and drive automation, understand your current state.

Map your current process: Document how improve finance processes and drive automation works today — who does what, how long it takes, where the bottlenecks are. You need this baseline to measure improvement.
Identify the judgment points: Building the case for investment, managing the change, and ensuring the automation works correctly — technology is the tool, you're the driver. 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 Celonis 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 improve finance processes and drive automation 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

What's our current capability gap in improve finance processes and drive automation — and is it a people problem, a tools problem, or a process problem?

They're prioritizing which finance processes to automate first

your ERP or finance systems admin

What's the biggest bottleneck in improve finance processes and drive automation today — and would AI address the bottleneck or just speed up something that's already fast enough?

They know what automation capabilities exist in your current stack

4

Check Your Prerequisites

Confirm readiness before you invest

Check items as you confirm them.