Automating many parts of the development and testing process, for example, gave the company a much better gauge of release readiness. With this better ability to predict performance after rollout, it is experiencing a greater success rate on the releases it brings to the market. Meanwhile, thanks to the collapsing of timelines possible with Agile and DevOps, the company is also bringing new releases to market at eight times the rate it did in the past. Innovation has become so frequent and fluid that managers talk about having an approach that treated adaptation as a constant growth process rather than a disruptive event.
The second important principle revolves around ensuring seamless mess between the existing technology ecosystem and the automation layer. If a company is using ServiceNow, for example, its automation enablement must be in line with its ServiceNow strategy. If it is using an ELK stack (comprises three popular open-source project: Elasticsearch, Logstash, and Kibana) as an open-source software for analytics and AI, it needs to have open-source plug-and-play APIs to which it can connect. It needs to integrate its automation layer using APIs, microservices, and containers, as opposed to pushing one more tool or asset into the existing technology environment. A seamless, accurate data fabric is also critical.