From Process Analysis to AI Workflows: Are We Changing the Name or Changing the Discipline?

One of the things that has always fascinated me as a certified Business Analyst is how many concepts originally related to process analysis are popping up in talks about AI workflows.

Much has changed in the technology. The need for understanding how the work creates value has not changed.

Years ago, companies used to invest a lot of resources into process analysis before implementing any new technology. Business analysts would spend a lot of time studying processes, activities, decisions, information flow, dependencies, risks, bottlenecks and potential improvements. Workshops would take place, interviews would happen, processes would be mapped, and requirements would be described.

The aim was not only to build some software. The aim was to understand the business.

Many professionals became experts in understanding how businesses work. They got to know the interaction between different departments, sources of information, decision-making mechanisms and where the problems emerge. Technology was just the last step of a much more complex analytical process.

Today, the new concepts emerged: workflow, AI workflow, automated workflow, agentic workflow and workflow orchestration. The terminology changed dramatically fast.

This leads us to a legitimate question. Is AI workflow substituting process analysis or introducing just a new method of doing pretty much the same things?

Modern workflow platforms allow connecting applications, automate routine processes, transfer information, trigger actions and, increasingly, incorporate AI systems that generate information, recommendations and decisions. Businesses are able to develop such solutions much faster compared to any traditional software development projects.

For managers who are pressured to show the results of AI implementation fast, it is good news. Workflow can be created in days or weeks, while traditional development projects often took months. Speed and understanding are not necessarily the same things.

Traditional process analysis was forcing companies to answer difficult questions before the introduction of technology:

• What problem are we trying to solve?

• Why is this process here?

• Who is the owner of this process?

• Which decisions are made?

• What information is needed?

• How are exceptions managed?

• What risks occur if something changes in the process?

These questions were helping to get organisational knowledge. The analysis itself often had the same value as the solution implemented after it.

Many AI workflow projects start in a different manner. Someone sees that an activity can be automated. An AI tool is connected to the systems in place. Workflow is created. Task is executed faster; productivity increases. The project becomes a success.

But one important question remains unanswered: did we gain understanding or just automation?

This difference may become especially important in the future as the usage of AI expands. There is a practical reason why workflows became popular. Workflow is much easier to understand than process engineering, enterprise analysis or business architecture. It is the language understandable for both technical and business audiences.

Workflows also fit the environment where executives have to produce results fast. The problem happens when the workflow design substitutes the understanding of process.

When looking at many AI projects today, I start wondering whether we see the extinction of business analysis or just its evolution.

• Process map is replaced with workflow.

• Requirements are replaced with prompts, rules and orchestration logic.

• Information flows are replaced with data pipelines.

• Decision trees are replaced with AI driven decision models.

• Governance is still an issue.

The terminology changed. Many of the questions remain the same.

That is why the debate should not be seen as a choice between process analysis and AI workflows. Different situations require different approaches.

Traditional process analysis is still relevant when you are dealing with:

• Regulatory compliance requirements

• Large-scale transformation programs

• Safety-critical activities

• Complex cross-functional processes

In such circumstances, understanding of the process is important before its automation.

In other cases, a workflow-first approach is completely relevant. It works well when:

• The process is known

• The activity is repetitive

• The risk is low

• The aim is to experiment fast

• The goal is validation of AI use case

For many businesses, however, the most practical approach seems to be the hybrid one.

Instead of spending months on analysis before the action or automating something right away without enough understanding, teams should perform enough analysis to understand the process while building workflows in parallel.

Hybrid approach includes:

• Process mapping

• Decision analysis

• Risk assessment

• Workflow design

• Governance

• Human control

• Pilot testing

• Continuous improvement

The aim is to unite understanding with execution, preserve organisational knowledge and benefit from automation.

Perhaps the biggest challenge in such a situation will not be technological but organisational.

For decades process analysts were developing knowledge and experience by studying how businesses work. They were understanding how the value is created, how the dependencies exist and how decisions are made. The process of analysis itself was helping organisations learn.

If the future generations start concentrating on the connection of tools and automation of activities, companies will lose part of the knowledge gradually. They will become more efficient but will know less about how they actually work.

It does not mean to refuse AI workflows. It means to combine them with other disciplines.

Companies should ask process-analysis questions before the implementation of workflow solutions. They should document the objectives, ownership, assumptions, risks and decision logic. The mechanisms of governance should be in place for critical workflows. The human control should be in place for critical decisions.

The most important thing is that there should be people who understand technology and business operations.

Perhaps the real question is not about the substitution of business analysis with AI workflows. Perhaps the question is whether people recognise the need for business analysis under a different name.

AI workflows can automate activities. AI workflows can accelerate execution. AI workflows can connect systems and minimise manual work.

What they cannot do is eliminate the need to understand:

• How the value is created

• How the information flows within business

• What decisions are made

• What risks arise

As a certified Business Analyst, it is the most important lesson that I observe in the current wave of AI implementation.

The technology changed. The need to understand the business has not changed