Hyperscale automation

A company that wants to be more efficient and adapt to new market conditions needs to onboard its employees in this massive transformation. One immediate step is to prepare employees by giving them the solutions they need to speed up productivity and focus on more valuable tasks. This creates an augmented workforce.

The need for hyperscale and increasing competition have radically transformed the speed of many processes. For instance, subscribing to Netflix gives immediate access to its entire catalog of entertainment. Not only do consumers love Netflix’s speed and simplicity, but having human interaction or controls in the process would represent a non-necessary friction that would slow down adoption. Processes need to be designed or redesigned to be fully automated. This delivers touchless processes.

Delivering touchless processes requires an architecture designed for it, with the key technologies being microservices and application program interfaces (API). Each process must be designed in a way that it is automated end to end and split into small and autonomous sub-processes, each of them built as a microservice. Microservices can deliver complex business rules, are designed to scale up or down on demand, support sudden activity (such as Black Friday), while consuming nothing during periods of low activity (for example, at four o’clock in the morning). These microservices speak to each other with APIs and can run in the cloud, as an autonomous service.

Creating a touchless solution is even more complex when external partners need to be involved. The overall design has to be worked out collaboratively, and a way to secure and automate communication has to be applied. APIs and blockchain can deliver a high level of efficiency and trust by bringing a certified proof of work on both sides.

However, delivering a touchless process is not as easy as simply automating an existing one. The whole process needs to be redesigned as an autonomous operation – using Capgemini’s D-GEM platform – and optimized to run on top of microservices.