The dashboard has found the delay. Now comes the interesting part.
Why are orders getting stuck? Which checks are doing something useful? What would happen if you changed them? And can the systems support the improvement everyone has just agreed?
These are the questions that bring the capability behind transformation into focus.
A process-mining specialist can uncover patterns in the data. Understanding what those patterns mean takes knowledge of the operation. Making the improvement work can bring process engineering, enterprise systems, automation and change expertise into play.
As organisations introduce AI agents into those processes, the decisions become more demanding. Someone needs to understand what an agent should do, what it needs to know and when it should bring a person into the conversation.
For businesses planning that work, there is an important question: what experience do we need around the table to make this work?
Knowing what the data is telling you
An order can become several deliveries. An invoice can cover multiple items. A payment can arrive while someone is still chasing it.
Understanding those relationships matters when you are investigating performance across a business. The analysis needs to connect orders, deliveries and payments in a way that reflects how the operation actually works.
What counts as a completed order? Which timestamp marks the actual handover? Are two systems describing the same event differently?
Answering these questions takes people who understand both the data and the business. They need to validate the connections and recognise when an apparent delay reflects how an event was recorded, rather than how the work was done.
There is also the work that system records do not fully explain: the spreadsheet used to resolve an exception, the clarification obtained by phone, the manual step that keeps everything moving. Task mining and conversations with operational teams can help fill those gaps.
Those workarounds deserve attention. Some are unnecessary effort. Others are holding the process together.
Making the right improvement
Take orders waiting on credit approval.
Shortening that wait sounds sensible. But the team needs to understand why the holds exist. Are customer records out of date? Are approval rules too broad? Or is the control doing exactly what it should?
This is where process engineering, analytical judgement and operational experience come together. The team needs to investigate the cause, design the change and test its effect against the wider outcome.
Putting that design into practice brings the enterprise systems and the people using them into the picture.
SAP, Oracle and automation specialists help implement the change across the relevant systems. Change practitioners help operational teams understand the new approach, handle exceptions and use it confidently. Together, they take the improvement from recommendation to everyday operation.
Giving AI a useful job to do
The same process could also be a candidate for AI. Consider an agent that investigates held orders and recommends the next action.
It needs reliable information about the account, recent payments and the relevant rules. It also needs clear boundaries. Preparing a recommendation, releasing an order and changing a credit limit carry different consequences.
The process design must therefore specify how the agent works with people and existing systems, including when responsibility passes between them.
What happens when records disagree? Who handles an exception? How does the system recover if an update fails halfway through?
Answering those questions takes process expertise alongside AI engineering, integration and testing. Operational teams have an essential contribution to make: they know the situations that a tidy demonstration may never encounter.
Finding the capability behind the job title
For leaders building a delivery team, the experience behind a role matters as much as the title.
Someone who builds process analyses may bring different strengths from someone who designs the underlying data model or implements changes across enterprise systems. A programme may need all three.
Useful conversations get specific about the processes and systems a consultant has worked with, how they validated their findings and what they helped change in live operation. The results matter, along with how those results were measured.
Sometimes one experienced specialist brings the breadth needed. Sometimes the work calls for complementary expertise across a team.
BeInspired brings together the expertise the work needs: a process-mining consultant who understands SAP data, a process engineer with supply-chain experience, or an automation specialist who can put the proposed changes into practice.
The capability that makes ambition work in practice
The thread running through this work is the ability to connect insight with action: understanding what the data reveals, deciding what should change and making that change work across the business.
Across Technology, Transformation and Growth, we mobilise specialist consultants and multidisciplinary teams around what you need to achieve.
Tell us what you need to transform. Let’s talk about the expertise that will help you get there.
Sources
Celonis: What is object-centric process mining?
Background on connecting orders, deliveries and other business records in process analysis.
SAP Signavio: Task mining
Explains how task mining can reveal manual activity that system event logs do not fully capture.
Celonis: Platform innovations for AI-driven operations
Describes using process context to coordinate AI agents, people and existing systems.