As more business processes become digitised, tasks such as monitoring, decision making, and optimisation that would previously have been carried out by human participants are beginning to be augmented or replaced by machine-based algorithms and artificial intelligence.
This is a natural progression in the transition to digital, and we expect to see it advance rapidly in the next three years as enterprise digital initiatives extend further into the organisation and the ability to measure and improve the outcomes of these processes matures.
There is a range of techniques that fall under the artificial intelligence (AI) and cognitive computing banner, including voice and image recognition, natural language processing, machine learning, robotic process automation, and neural networks, but we are now seeing these incorporated into diverse business platforms and applications, in a form that makes them simpler for enterprises to adopt and apply to their processes.
This is no longer the technology of tomorrow, but an array of tools that can form part of any digital initiative.
Common patterns include real-time optimisation of straight-through processes, synthesising knowledge to inform or recommend a next-best action, providing virtual assistance in place of a human operator, identifying anomalous or critical conditions in data streams, and creating predictive insights and inferences through the integration of multiple data sources.
There is a growing appreciation of the role of software in products, services, and processes across all sectors, from loan approval in banking and premium calculations in insurance to improving student outcomes in education and assisted diagnoses in healthcare.
Fuelled by data, we should expect more sophisticated algorithms to become part of these processes, both to augment human decision making and to reduce the level at which direct human supervision or input is required.
We believe that organisations should actively seek opportunities to apply these techniques to their digital processes, by asking some of the following questions:
- Where is manual human intervention still required?
- What techniques are currently used to improve process efficiency?
- Where could greater insight help knowledge workers?
- How could data sources be utilised or combined to analyse process performance?