Autonomous Digital Enterprise is the future of business (Part II) - Its Principles, Levels, and Characteristics

What are the principles, levels, and characteristics of Autonomous Digital Enterprise?

Organizations had to redesign their IT teams and infrastructure management in response to rising demand for virtual communication and collaboration. These changes in workplace culture and norms have expedited company transformation. To support the change to digital primary rather than digital secondary, enterprises must reimagine their operations lifecycle and standard operating norms. Now, the technology has evolved from a supporting infrastructure to a main driving force in enterprises' ability to sustain and progress towards success. This is how companies become Autonomous Digital Enterprises (ADE).

An Autonomous Digital Enterprise (ADE) is a growth-oriented enterprise that provides value in solutions while increasing operational agility and flexibility. As such, an ADE prioritises the views and opinions of its customers in order to incorporate any useful information they may have provided.

In our previous article in this three-part series, we discussed the concept of an Autonomous Digital Enterprise, what exactly it means and how it will change your business significantly.

This article explores other facets of ADE, including its principles, levels, and characteristics.

Autonomous Digital Enterprise Principles

According to a Harvard Business Review Analytic Services study article, autonomous firms follow and are led by three important principles: context, insights, and actions. The terms "Context" and "Insights" refer to the insights gained by applying Artificial Intelligence/ Machine Learning (AI/ML) to operational data, and "Actions" refer to the application of advanced automation technologies such as Intelligent Automation or Hyperautomation to proactive, preventive, and reactive scenarios.

Following these principles, businesses might strive for autonomy. However, if companies want to be really autonomous, they must have intelligent, networked, technology-enabled, value-creating systems in place, as well as a rock-solid data foundation. They should also use AI/ML and hyperautomation or intelligent automation technology to all of their business processes and associated activities, allowing for smooth interactions with stakeholders and their partner ecosystem.

5 Autonomous Digital Enterprise Levels

Autonomous Digital Enterprises go through several stages of change.

1. Basic Automation

Automation of important tasks and process automation are examples of basic automations. Businesses use fundamental process automation solutions such as BPM and intelligent workflow automation. There are various human interventions in this to guide manual steps to automate.

2. Human-Initiated

Emerging technologies such as RPA, intelligent automation, process mining tools, machine learning, and natural language processing, among others, are being used to enable human-directed automation of business processes.

3. Machine intervention in automation

These are high-level automations that leverage cognitive applications, neural networks, GANS models, and contextual choices, among other aspects, to deliver automations with occasional machine interventions. Humans are on standby but do not need to intervene for extended periods of time.

4. Enterprise with Complete Autonomy

This, as the name implies, is total process automation in which machines take complete control of the processes. For total process automation, such businesses use AI-driven smart services, self-learning, self-rectifying, and self-securing.

5 Humans are optional.

This is most likely the most advanced and forward-thinking phase of an autonomous enterprise. At the moment, no firm has reached this level of automation maturity. This includes fully independent sentience, which enables precise decisions at scale. Human engagement is entirely optional in this case.

Characteristics of an Autonomous Digital Enterprise Operating Model

Self-driving firms that use artificial intelligence, intelligent automation, and governance to streamline operations and management are known as autonomous digital enterprises. A fully autonomous company can configure, monitor, and maintain itself on its own. It can also learn and adapt to change. There are various operational model features that firms must consider when applying them to processes and operations across the enterprise in order for the organisation to become an autonomous enterprise.

1. Technology-enabled work transformation based on efficiency

Work transformation projects that harness technology for cooperation across digitally enabled functions are spearheaded by modern autonomous companies. This leads to increased company efficiencies, increased productivity, and improved employee engagement and satisfaction.

2. Be aware of the internal and external states

Companies seeking to become autonomous digital enterprises investigate, interrogate, deduce, measure, and monitor data to observe and comprehend the internal and external state of processes, systems, and operations.

3. Data gathering and mining

Data should be integrated and shared between sections, departments, or practises in enterprises. This data should be able to be aggregated by organisations in order to analyse the structural and behavioural elements of processes, systems, and operations.

4. Intelligent Automation Throughout

Organizations that are well on their way to becoming self-sufficient digital firms automate business operations, particularly through the use of Intelligent Automation. Intelligent Automation (IA) integrates technologies such as Structured Data Interaction (SDI), Robotic Process Automation (RPA), Machine Learning (ML), and Natural Language Processing (NLP). IA also interfaces with supporting applications or tools such as optical character recognition (OCR), artificial intelligence (AI), business process management (BPM), and others. Customer interactions and activities that are automated result in lower costs and errors, considerably faster execution, fewer repetitive monotonous work for personnel, and, ultimately, better customer experiences.

5. Data-driven enterprise

Advanced data and analytics are used by autonomous organisations to recognise patterns, construct normal behaviour models, get important insights, and proactively identify dangers. In addition to traditional data sources, data can be collected from IoT, social media, and consumer engagement platforms.

6. Improved communication systems

Autonomous enterprises enable collaboration, intelligent automation, and other communication tools.

7. IT centralised

Autonomous organisations have centralised IT responsibilities for digital transformation via Centers of Excellence (CoEs) that supply technology to support enterprise-wide transformation activities.

Final Thoughts

Significant gains in efficiency may be possible through the automation of critical back-end operations and front-line tasks. Planning and a clear roadmap to leverage emerging technologies like intelligent automation, hyperautomation, AI/ML capabilities, advanced analytics, and others are necessary to evolve, mature, and reinvent progressively to become more technology-enabled across the enterprise, on your way to becoming an autonomous digital enterprise. Making it possible for people, processes, technologies, data, devices, and expanding networks to converge will be crucial. Successful autonomous digital enterprises will place a premium on agility and customer-centricity, and leverage data-driven insights to increase their scalability, resilience, and dependability.

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Watch this space for our next article on the ADE series covering the components of an Autonomous Digital Enterprise.