Skip to content

Digital Transformation Leader

Process Redesign & Automation

Enhances✓ Available Now

What You Do Today

You lead the redesign of core business processes — identifying manual bottlenecks, eliminating waste, and implementing automation where it creates real value. You don't just digitize bad processes.

AI That Applies

Process mining tools that analyze system logs and user behavior to map actual process flows, identify deviations, and quantify the cost of manual workarounds.

Technologies

How It Works

The system ingests system logs and user behavior to map actual process flows as its primary data source. Machine learning models identify the patterns in historical data that most strongly predict the target outcome, then apply those patterns to score new inputs. The results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context. The redesign itself.

What Changes

You see how processes actually work, not how they're documented. AI reveals the real workflow — including all the shadow processes, email-based approvals, and spreadsheet bridges people built around broken systems.

What Stays

The redesign itself. Understanding why a workaround exists (regulatory requirement? tribal knowledge? broken system?) and designing a better process requires deep business understanding.

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 process redesign & automation, understand your current state.

Map your current process: Document how process redesign & 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: The redesign itself. 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 Process Mining 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 process redesign & 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 CEO or executive sponsor

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

They set the strategic priority for transformation initiatives

your CTO or CIO

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

They own the technology capability that enables your strategy

4

Check Your Prerequisites

Confirm readiness before you invest

Check items as you confirm them.