At the core of organisations are subject matter experts that are close to reality on the ground. Supplementing them with automated and intelligent ways of gleaning insights from operational data allows experts to focus on fixing problems, instead of proving why and how it is a problem.

Innovation has transcended the use of data beyond building an understanding and towards guiding improvements – a compass, instead of a map. 

The step change is driven by the maturation of Process Intelligence: technology that uses system data to rapidly build digital twins of operations and identify scenarios leading to process variance and lost productivity. Process mining has existed for several years, but the application of AI/ML towards process optimisation has enabled the relation of process events to its impact on business performance.

Take the Finance function as an example, where early payments to vendors lead to reduced working capital. Traditional methods to understand payments processes are time intensive and complicated – interviews with operators can only cover so many people, and managers may not be aware of all scenarios. But with models correlating working capital to variances in the invoicing process, organisations gain insight into the conditions under which early payments are made and the extent of its impact on working capital. This then forms a basis to design the most effective intervention.

Organisations with business intelligence capabilities can drill down into functional performance to set sights on targeted improvements, answering “what is happening?”. This is followed by the application of analytical engines to process large volumes of operational data and uncover unknown inefficiencies, answering “why is this happening?”. The ensuing intervention should be an investigation into why the inefficiency exists, becoming the subject of process changes – “how do we fix it?”.

For most organisations, the gap to close will be in the sourcing and application of analytical engines – a need filled by widely available Process Intelligence platforms. These platforms ingest event-based system data based on a standard data model and generates visualisations of process flows and interactive analysis to investigate process performance. The vendor landscape is primarily made up of software companies focused on Process Intelligence solutions and established CRM/ERP players adding Process Intelligence as a value-add service, placing a right-sized solution in reach for most organisations.

The competency of leveraging common identifiers across multiple system logs to reconstruct and visualise an E2E view of a business process – the tenet of Process Intelligence – is underpinned by data engineering, enabled by AI/ML, and facilitated by seasoned operations experts. Whilst these skills are rare, at stake is the advantage of making more out of data than competitors, creating a case to invest in these skills and embed them within improvement initiatives.

There are three areas where Process Intelligence creates advantage:

1. Building up-front understanding of systems and processes to guide the intervening play. Rapidly building a digital twin of operations creates an advantage through the speed at which understanding is built (automated system crawlers work faster than workshops and documenting business processes), helping organisations go broader and deeper in understanding operations.
2. Monitoring effectiveness and uptake of interventions. Frequent, targeted corrections can be made while transformation is taking place as live system data is monitored as interventions are rolled out. Shorter feedback cycles de-risk the possibility of lacklustre results following a long, sustained period of effort.
3. Embedding insights into intelligent operating models. In the same way air traffic controllers digitally optimise flight paths, operations leaders can use process intelligence to monitor bottlenecks, simulate changes to operations to model its impact on performance, and make appropriate investments in intervening plays.

An example is in ERP transformations, where stakes are high, and success is reliant on navigating massive organisational complexity to identify core value drivers.

An organisation’s ability to embed Data and AI within operations in a fit-for-purpose way is a new frontier of advantage – and the on-the-grounds, complex nature of operations should not be overlooked. Whilst most organisations look for a map, a compass is needed to find the right path.