Autonomous Digital Enterprise: How They are Changing the Future of Business

What is an Autonomous Digital Enterprise ?

To thrive amid continued transformation, companies must reorient the relationships between their businesses and society. A digital-first business enables differentiation through technology, innovation, and customer intimacy. With this distinctive framework in place, companies will be able to act with clarity, making better decisions and improving their ability to innovate for growth.

An autonomous digital enterprise is a business that relies on automation and machine learning to grow and scale. This new way of doing business is an important shift because it leverages the skills that machines are good at, like repetitive tasks, and makes humans more valuable by leveraging their creativity skills.

5 Levels of Autonomous Digital Enterprise

1. Basic Automation

Basic automations include automation of essential tasks and workflow automation. Companies leverage basic process automation tools like BPM and intelligent workflow automation. In this, there are several human interventions to guide manual steps to automate. The tools help in automating these tasks, which were otherwise manual or time consuming.

2. Advanced Automation

Companies move towards advanced automation when they require advanced features like data integration, analytics and integration with multiple systems such as ERP, CRM etc. This can be achieved through APIs that enable data exchange between systems and applications.

3. Digital Transformation

Companies that have adopted digital transformation are using advanced technologies like AI and IoT to improve their product offerings or provide better customer experiences by integrating multiple systems into one platform or creating a single pane of glass for all operations across different systems. This requires focus on improving business processes through advanced automation so that it can be handled by a single person without any dependence on other resources like IT, finance etc 

4. Fully Autonomous Enterprise 

This, as the name suggests, is the total automation of processes where machines take full control of the processes. Such enterprises leverage AI-powered smart services, self-learning, self-rectifying, and self-securing for total automation of processes.

5. Humans Optional – 

This is probably the most advanced and futuristic level of an autonomous enterprise. Currently, no organization has gained this level of automation maturity. This includes complete autonomous sentience, empowering precision decisions at scale. The necessity of human involvement here is totally optional.

Here are some examples of how autonomous use cases can benefit  various businesses:

Autonomous  Transportation 

Autonomous use cases in the transportation sector include driverless taxis, public transport and car sharing between passengers.

Autonomous use cases for restaurants and hotels include delivery orders, table reservations and room service.

 Autonomous  Healthcare 

The health industry is another area where artificial intelligence can have an impact on the way we work and live. For example, using IBM Watson Health’s AI platform to monitor patients’ health data and deliver targeted care based on their needs. With IBM Watson Health you can build a virtual assistant that provides personalized care right from your smartphone or through SMS messages.

Autonomous Education 

The education sector is another area of artificial intelligence where can have an impact on the way we teach ourselves or our children. For example, using IBM Watson Education’s technology to analyze student performance over time and better understand individual learning styles so that teachers can tailor lessons more effectively.

There are many ways we can use artificial intelligence to make education better. For example, we can use it to analyze student performance over time and better understand individual learning styles so that teachers can tailor lessons more effectively.

The education sector is another area where artificial intelligence can have an impact on the way we teach ourselves or our children. For example, using IBM Watson Education’s technology to analyze student performance over time and better understand individual learning styles so that teachers can tailor lessons more effectively.

We can also use AI to help teachers identify students who may be struggling at school, or those who may need additional support through a program such as Mentoring for Learning or even just one-on-one counseling sessions.

Autonomous Retail: 

The retail industry is one of the largest industries in the world. It has grown to become a key part of many economies and its growth rate is projected to continue. Amazon, Walmart, and other e-commerce companies are leading this trend due to their ability to provide a wide range of products at low prices. More traditional retailers such as Macy’s and Sears are also offering new services such as food delivery, which can help them compete with Amazon and other online retailers. However, this trend poses a risk for these traditional retailers if they do not innovate quickly enough to stay competitive.

Autonomous Database

The autonomous database can be used for automated processes like batch processing, transaction management, or data mining. These systems are used for large-scale data collection and analysis.

A good example of an autonomous database is Google’s Lucene Search Engine which indexes millions of web pages every day. The system runs on multiple servers distributed across the globe and it has no need for human intervention.

Conclusion

Establishing an autonomous enterprise is a journey and not a destination. Organizations must develop a three-to-five-year vision for the autonomous enterprise that points out business outcomes and reflects digital use cases of digital technology in their industry. They should also consider the various automation levels and the autonomous building block components and execute them in steps. It is important to start small, with a proof of concept that can be developed into a minimum viable product. The resulting minimal viable product should be iteratively fine-tuned before it is scaled for enterprise deployment. Finally, it is important to build a business case highlighting the financial benefits of autonomous operations that can save capital and operating expenses, especially in new operations, data centers, and business processes.

In summary, it is vital to start small with a proof of concept before scaling up operations for an entire enterprise. This will allow organizations to test out new processes before investing resources into them. The process of building an autonomous enterprise will take time but companies should make sure they have a clear goal in mind before starting down this path.