AI seems to be everywhere, or at least discussed everywhere. But, let’s face it, behind the speeches, the mind boggling investment figures, only a very limited number of companies are operationally ready to welcome and use AI to its full potential.
The question them becomes, or should have been all along : how to prepare for AI ? In other words, what needs to change ?
You are probably going to tell me : “it’s all about data !” And that’s absolutely true, data is at the very heart of AI, both in terms of quantity and quality. But there is also a forgotten, yet crucial element : understanding your processes.
AI is not a strategy. It is a tool that acts on your operations. And if those operations are not well understood, documented, and aligned, then AI will not magically fix them. It will simply make them faster, amplify their inconsistencies, and potentially create new problems at scale.
Why process management comes before AI
Think of your business as a network of interconnected workflows: order to cash, procure to pay, claims processing, hiring and onboarding, incident management, sales to order. Each has steps, decision points, hand-offs, and data exchanges. When they work well, your business flows. When they are opaque, ad hoc, or inconsistent, you get friction, rework, and waste.
This is why process management & process mapping come before AI. It’s not bureaucracy; it’s the foundation that makes AI adoption possible at scale.
- Data
Quality is tied to process quality
Most “bad data” is not an IT issue, it’s a process issue. If sales enters orders five different ways, no AI model will fix it. Process mapping clarifies where data originates and how to capture it correctly at the source. - AI needs clear decision paths
AI doesn’t just predict, it recommends. But unless escalation paths and exception handling are defined, insights remain stuck on dashboards. Processes creates the structure for AI to plug into. - Avoiding local optimization
Without an end-to-end view, AI pilots often optimize one step (say, reducing handling time in a single team) while creating new friction elsewhere. Process mapping ensures AI improves the whole flow, not just fragments, to maximize value based on process objectives. - Standardization enables scale
AI can deal with some variability, but scaling across geographies or units is impossible if everyone works differently. Mapping provides the shared backbone AI needs to expand beyond pilots. - Deciding what not to automate
Some tasks require human judgment or empathy. Mapping highlights which steps to automate, which to augment with AI, and which to keep human.
In short: a clear process map shows how work really gets done, aligns stakeholders on “good”, and provides the structured environment in which AI can deliver value.
Let’s say I was able to convince you about the importance of process mapping in your AI transformation. The next question becomes : how do you make it happen ? What are the steps?
From process to AI
Good news : it’s not rocket science, but it requires a clear commitment.
- Process Discovery & Mapping – Understand the current state. Where are the steps, decisions, and loops? Where does work actually happen?
- Process Redesign & Standardization – Remove unnecessary variation, align teams on the future state, and create clear roles and responsibilities.
- Data Instrumentation – Once the process is clear, measure it. Ensure the right data is captured, stored, and cleaned.
- Automation & AI – Now (and only now) are you ready to bring in automation, machine learning, or decision support tools.
When we skip step 1 or 2, we often find that automation is applied to a broken process.
A practical example
Imagine a company with a complex accounts payable process. Standard invoices flow through the ERP smoothly, but exceptions pile up: wrong tax codes, missing purchase orders, manual corrections needed in different systems. The finance team spends hours each week reviewing and fixing these cases.
Management decides to “use AI to optimize accounts payable.” Instead of just deploying a robotic process automation on a vague process, they start with a dedicated process mapping. They clarify the invoice flow, identify where exceptions occur, and standardize how each type of error should be handled. With that foundation in place, they deploy an AI model that classifies exceptions and routes them automatically. For common cases, a chatbot even communicates with vendors to request missing details.
The result? A significant reduction in manual workload, faster vendor payments, and better use of finance team capacity. Without process mapping, the AI would have struggled with inconsistent data and unclear resolution paths. With mapping, the AI becomes a true accelerator.
The double benefit of process mapping
Process mapping is not just preparation for AI — it is value‑creating in itself. The very act of mapping:
- Aligns stakeholders on what “good” looks like.
- Surfaces hidden pain points and redundancies.
- Highlights quick wins for improvement (sometimes you realize you don’t need AI at all!).
In other words, process mapping is crucial on your AI journey while delivering immediate operational gains.
At ngage, our Operational Excellence practice is dedicated to supporting our clients throughout their transformation journeys, including AI transformations. We are specialized in Business Process Management (BPM), encompassing process modeling, process improvement, and automation, leveraging people, organization & technologies.
We would be happy to help you optimize your organization, with or without AI. Although, let’s face it, it will probably be with AI.
Want to know more?
Reach out to Simon to discover how to leverage process management before starting your AI transformation.
Use the button below or via s.peter@ngageconsulting.com.