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Operations Manager

Drive process improvement initiative

Enhances✓ Available Now

What You Do Today

Identify the highest-impact process improvement opportunity, lead the project, measure results, and sustain the improvement.

AI That Applies

Process mining — AI maps actual process flows from system data, revealing bottlenecks, rework, and deviations from designed processes.

Technologies

How It Works

The system ingests designed processes as its primary data source. 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 see the real process, not the designed one: '25% of transactions go through 3 unnecessary approval steps that add 2 days of cycle time.'

What Stays

Leading the improvement — getting stakeholder buy-in, managing the change, and sustaining the gains after the initial project.

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 drive process improvement initiative, understand your current state.

Map your current process: Document how drive process improvement initiative works today — who does what, how long it takes, where the bottlenecks are. You need this baseline to measure improvement.
Identify the judgment points: Leading the improvement — getting stakeholder buy-in, managing the change, and sustaining the gains after the initial project. 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 drive process improvement initiative 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 VP Operations or COO

Which steps in this process are fully rule-based with no judgment required?

They're prioritizing which operational processes to automate

your process improvement or lean lead

What's the error rate on the manual version, and what would "good enough" look like from an automated version?

They understand the workflow dependencies that AI tools need to respect

4

Check Your Prerequisites

Confirm readiness before you invest

Check items as you confirm them.