In recent years, enterprises in every industry sector have been embarked on a digital transformation journey in one way or another. Business enterprises are taking advantage of the proliferation of digital technologies to define new business models or to improve business productivity with existing models. Key digital propellers such as the Internet (as a ubiquitous reachability platform), applications and open source proficiency (as a skill set platform), cloud (as a pervasive computing and data platform), and, of late, AI/ML (as an insight discovery platform) help enterprise businesses to improve business productivity and customer experiences.
While the pace of digital transformation varies based on the business and the sector it is in, overall, the journey of digital transformation has three stages.
- Task Automation. In this stage, digitalisation leads businesses to turn human-oriented business tasks to various forms of “automation,” which means more applications are introduced or created as part of the business flow. This began with automating well-defined, individual tasks to improve efficiencies. A common example is IVR systems that answer common questions about a product or service but may need to hand off to a human representative. Individual tasks are automated, but not consistently integrated.
- Digital Expansion. As businesses start taking advantage of cloud-native infrastructures and driving automation through their own software development, it leads to a new generation of applications to support the scaling and further expansion of their digital model. The driver behind this phase is business leaders who become involved in application decisions designed to differentiate or provide unique customer engagement. For example, healthcare providers are increasingly integrating patient records and billing with admission, discharge, and scheduling systems. Automated appointment reminders can then eliminate manual processes. Focusing on end-to-end business process improvement are common themes in this phase.
- AI-Assisted Business Augmentation. As businesses further advance on their digital journey and leverage more advanced capabilities in application platforms, business telemetry and data analytics, and ML/AI technologies, businesses will become AI-assisted. This phase opens new areas of business productivity gains that were previously unavailable. For example, a retailer found that 10% to 20% of its failed login attempts were legitimate users struggling with the validation process. Denying access by default represented a potentially significant revenue loss. Behavioural analysis can be used to distinguish legitimate users from bots attempting to gain access. Technology and analytics have enabled AI-assisted identification of those users to let them in, boosting revenue and improving customer retention.
The steady rise in leveraging application, business telemetry and data analytics enables organisations to scale digitally. Adopting an agile development methodology to quickly iterate modifications has shortened the lifecycle of “code to users.” In digital enterprises, the “code” embodies the business flow and the speed of change in “code to users” represents business agility. In this new era of digital economy, applications have become the life blood of the global economy. Every business is becoming an application business and every industry is becoming application-centric industry.
As IT infrastructure automation and application-driven DevOps processes have been largely established across the industry, we envision that a layer of distributed application services that unifies application infrastructure, telemetry, and analytics services is emerging. The scale, agility, and complexity of digital enterprises demands their applications to have self-awareness and the ability to automatically adjust to operating and business conditions. This will breed a new generation of application services to collect, analyse, and act on the telemetry generated by apps and their infrastructure. These capabilities create new business uses. End-to-end instrumentation from code to customer will enable application services to emit that telemetry and act on insights produced through AI-driven analytics. These distributed application services will help application owners to improve application performance, security, operability, and adaptability without significant development effort.
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