How To Implement Intelligent Automation?

Intelligent Automation (IA) refers to the smart technologies that enable business process automation (BPA) by orienting artificial intelligence into functional workflows and end-to-end operations.

Intelligent automation is different from traditional automation techniques in two ways:

Intelligent automation is concentrated on the whole procedure workflow end-to-end and is not limited to automating individual redundant tasks in the methodology chain.

Intelligent Automation Technologies speculate on the prospects and make data-driven findings regarding automating a procedure.

Let's take a glance at intelligent automation, including instances, components, and stages for introducing IA in the enterprise.

intelligent automation example

Consider an easy-case example. Suppose that your IT network is equipped with an Identity and Access Management (IAM) system that reviews and organises network access demands as authorised or unauthorised.

One day, the IAM system observed a disproportionately enormous amount of access requests from globally allocated IP address locations. While all seem to be valid login details and should be allowed, the pattern also recognises potential cyberattack attempts.

While the traditional method of an automation system would let all network connection demands through as long as the key certificates were validated, the intelligent automation solution would study the way of recommendations and historical network key trends. The Intelligent Automation solution would quickly identify the anomalous behaviour as a possible Distributed Denial of Service (DDoS) attack or a loophole in network structures that is routing all traffic through a single access terminal.

Components of Intelligent Automation

To deliver intelligence across the end-to-end decision-making procedure, a good Intelligent Automation (IA) solution integrates:

Artificial Intelligence

Intelligent automation is driven by data that has hidden insights about the wider industry approach. AI algorithms analyse extensive volumes of structured and unstructured data to determine anomalies proactively. The data streams are used to:

Constantly test the current state of company operations.

Predict future scenarios.

AI algorithms also account for a large set of dynamic parameters that should affect the conclusion of automation technology to execute a duty based on the current company strategy state.

Business Process Management

The techniques are used to automate business operation workflows. The domain of BPM involves the use of different technologies and techniques to model, analyse, and optimise company processes. BPM integrates the behaviour of systems and users to deliver results that support the business plan.

BPM methods are highly data-driven, which makes them a qualified candidate to integrate AI capabilities that can model complicated systems accurately.

Robotic Process Automation

It is a part of the broader BPM chain that automates individual studies in the BPM pipeline and interfaces with the backend methods through a graphical user interface (GUI). Traditionally, the automation and backend interface would need manual scripting and reliable application programming interfaces (APIs).

RPA generally performs task-centric, rule-based automation across APIs and GUIs. In a complicated company operation pipeline, these individual lessons can be highly intertwined with a variety of enterprise functions. In this context, isolated automation of highly dependent tasks shows limited progress in productivity and often bottlenecks the BPM capability of the organisation.

How do I implement intelligent automation?

So how do you transition from traditional business process management and robotic process automation to an AI-enabled intelligent automation medium?

The following framework can assist you in incorporating cognitive abilities into your active workflows and accelerating your digital conversion capacity.

Set the goal: new digital expectations.

Value speed: users examine proactive value providers in the digital era. Focus IA investments on removing implementation bottlenecks and recognising opportunities for productivity gains. Simple charging and shifting of automation technologies will not suffice.

Integrate IA designs into the support system for brainstorming problems and delivering significance to end-users.

Prepare for a disruptive journey.

Redefine your organisational structure and culture to prepare for IA-based, value-driven, user-engagement company procedures.

Recruit in-house professionals to maximise the importance of IA stuff.

Design IA systems that replicate human intelligence and behavior, particularly for service management (ITSM) systems facing direct user interactions—like ticketing systems.

Innovate in RPA with the right strategy.

• Begin with a proof of concept.
• Establish the right expectations.
• Concentrate on your actions to develop the right solutions. Only 30% of the time is spent configuring the actual robot systems.
• Obtain the entire stakeholder's support and executive buy-in.
• Create a library of tools that will strengthen RPA execution.
• Automate short, iterative sets of duties instead of automating complex RPA pipelines end-to-end.
• Monitor outcomes continuously.
• Integrate the company and IT teams to maximise the system alignment of RPA projects.
• Build sustainable solutions.

Partner with the back post for value. Your IT back office should guide the company's importance as it goes through the intelligent automation maturity curve. Upgrade and convert the back office such that it can engage end-users and deliver value to business clients.

Execute, improve, and reiterate

• ready for the future through continuous progress.
• experiment, monitor improvement, gain user feedback, and iterate.
• Concentrate on the flexibility to modify and enhance in response to changing company needs.
• These guidelines can assist you in getting started with intelligent automation initiatives in the right direction: highly concentrated on business value, providing positive end-user engagement, and decreasing waste processes.

FAQs

What is intelligent automation?

Intelligent Automation (IA) is a combination of robotic process automation (RPA) and artificial intelligence (AI) technologies that together empower rapid end-to-end business process automation and accelerate digital transformation.

What is an intelligent automation process?

Intelligent Process Automation (IPA) refers to the application of artificial intelligence and related technologies, including computer vision, cognitive automation, and machine learning, to robotic process automation.

Which processes are best suited to use intelligent automation?

Even though more and more AI is being incorporated with RPA to automate more cognitive tasks, rule-based processes are the easiest to succeed with. Tasks that require too much human intervention may not be suitable for RPA because they may end up with too many exceptions that then need to be handled by humans.

What is the difference between AI and intelligent automation?

RPA generally focuses on automating repetitive, frequently rule-based activities, whereas intelligent automation uses artificial intelligence (AI) technologies, including machine learning, natural language processing, structured data interaction, and intelligent document processing.