Author: Tim Jennings, Chief Research Officer, Ovum
As the momentum of migrating enterprise workloads to the cloud increases, there is a growing trend of innovation as a major business driver for cloud application deployments. Historically, innovation has not figured highly in the business case for these projects, particularly for the ERP systems that run an organization’s core business processes; the rationale has been much more about operational efficiency, and modernization of back-office functions – something particularly prevalent in HR and finance in recent years.
Now organizations are looking at their core applications as a platform for innovation, particularly as their digital transformation projects extend deeper into the core. Capabilities such as AI and machine learning, blockchain, and IoT are all seen as opportunities to innovate around products, services, and business models. Implementation approaches are changing to match these ambitions, with a move away from lift and shift, and replicating existing customizations in the cloud, to making a fresh start on a modern platform with a phased agile implementation and a regular and frequent cadence of new functionality.
Updating a major back-office application should also be a trigger – and an opportunity – to reassess the business processes that it supports and the value that it delivers to the enterprise. The preferred approach is to utilize standard processes as widely as possible, using configuration within the application, rather than customization, to meet the needs of the business. Custom software development should be restricted to must-have features that cannot otherwise be supported by the application or where a bespoke approach can provide the business with a profitable differentiated proposition.
Though some organizations will seek to develop new capabilities using emerging technologies such as AI, IoT, and blockchain, much of the work will be done by the application vendors: the real value derived from these technologies is in your organization’s data, not in the software code. Every application vendor, for example, is now incorporating AI for relevant business use cases such as next best action in sales and customer service, or best fit candidates in recruitment. When combined with IoT data from the physical world, it also enables scenarios such as predictive maintenance schedules in engineering.
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