By Felicia Oyedara

Every transformation programme starts with a process map, swimlane diagrams, documented workflows, and a shared belief that the organisation understands how its own work moves. That belief may not always fully reflect operational realities, and the cost of discovering gaps mid-migration is rarely small.

In April 2018, TSB Bank migrated customer records from a legacy platform to a new system built by its parent, Banco Sabadell. Following the migration, many customers experienced difficulties accessing their accounts, and some services were disrupted for a period.

A subsequent review found the migration had proceeded without adequate understanding of the interdependencies between existing processes and the new system’s architecture.

The problem has a name, and it is more precise than many organisations appreciate.

What is Operational Intelligence?

Operational intelligence derives a data-accurate picture of how work actually moves across processes, systems, decisions, and people, from the systems that already exist. It answers questions that stakeholder interviews or workshops may not fully resolve: What paths does work actually take? Where do delays accumulate, and why? What decisions are being made, at what points, by whom, and with what consistency?

Without it, three things can happen. Teams may automate undefined workflows. They may digitise bottlenecks. They may scale inefficiencies across platforms.

The Data Layer

The raw material of operational intelligence is event data. Every enterprise system: ERP, CRM, case management, order management writes event logs. Every status change, approval trigger, and record update leaves a timestamp. Those timestamps, linked by a case identifier, contain a record of operational behaviour. The data already exists in systems organisations already run. What often does not exist is the practice of treating it as a primary input to transformation strategy rather than a compliance byproduct.

From a clean event log, process mining tools such as Celonis, UiPath Process Mining, and SAP Signavio reconstruct process paths, show where delays concentrate, and compare behaviour against documented models. The gap between what the process map shows and what the event log shows can be significant. Approvals that look automatic in a diagram can involve additional manual steps outside formal systems.

When DHL applied process mining to its customs clearance operations, it identified delays linked to both processing time and the timing of decisions.

That distinction matters. Most transformation programmes focus on process visibility: where things are in the pipeline. Decision visibility goes further. The decision point is where the workflow branches, and overlaying case attributes on those forks can reveal what factors influence path selection and whether that governance is consistent across teams and over time. It can indicate which exception paths consume disproportionate capacity and how workflows behave compared to documented expectations.

Operational Intelligence and AI Readiness

This is where the stakes are highest. A machine learning model trained to automate routing decisions will perform well if the training data accurately reflects the decision logic that should govern those decisions.

In many organisations, historical decision data reflects formal rules alongside informal workarounds, individual discretion, and exceptions handled outside the system. A model trained on that data may learn patterns of observed behaviour rather than intended logic. Deployed at scale, it can reproduce those patterns consistently.

Establishing clear decision logic before training, and building a dataset that reflects intended rather than observed behaviour, is not a minor step. It can influence whether an AI system supports good decisions or amplifies existing inefficiencies.

Sequence Before Selection

Operational intelligence is not a workstream that runs in parallel with implementation. It is a prerequisite that supports more informed implementation decisions. Before any platform is selected or automation brief is written, three questions need answers from data: What paths does work actually take, and how frequently does each variant occur? Where do delays accumulate, and what attributes predict them? At what decision points does the workflow branch, what governs those branches in practice, and how consistent is that governance across teams and over time?

Felicia Oyedara is a UK-based Data Analyst specialising in digital operations, process optimisation, and people analytics across fintech, banking, and consulting environments. She focuses on translating operational and workforce data into clear, actionable insights that improve performance, streamline processes, and support better decision-making.