AI-Infused Automation is The Future of Work

From a backroom Chess player to a GDP influencer, AI has come a long way in the last 2 decades. Now AI is all set to lead the automation drive to $15.7 trillion, which is a 26% increase in global GDP in the coming decade, according to PwC’s Global Artificial Intelligence Study. This technological change is the driving force behind the development of new products and automated workplace solutions. Understandably, people have growing fears about their job as AI replaces humans, but in reality, it is simply changing the way we work.

Automation is at a tipping point due to the rapid development of artificial intelligence(AI). New forms of AI can perform a slew of functions without considerable human intervention. Automated technologies can not only execute iterative tasks but also augment workforce capabilities significantly.

The question now is how best to implement intelligent automation solutions. The solution involves a perfect combination of automation and AI in a unified platform readily accessible to the organization looking to scale automation enterprise-wide and move to hyper automation.

Automation with artificial intelligence is all set to redefine the future of whole business processes. True business process automation requires more than robots. It needs AI technology working along with the bots to perform repetitive manual tasks and mimic human intelligence, analysis, and decision-making ability to take the appropriate course of action.

Why do Automation projects fail?

According to an EY study, numerous RPA (Robotic Process Automation) projects fail to meet their objectives. One of the biggest reasons why automation projects “fail” is the cost involved in RPA maintenance and support.

Here are a few reasons why bots require maintenance:

1. Changes to the user interface (UI) of applications the RPA bots interact with, and changes in any layer across application, security, and infrastructure might affect automation.
2. Changes in regulations, policies and controls impact the RPA bots.
3. Requirements missed during the development process might require rewriting again.
4. There is always a case where 99% of bots require custom scripting. The scripts developed by citizen developer usually lack resiliency as it requires technical skills.

Ultimately these break-fix cycles cause a strain on the broader business objectives like reducing costs, increasing efficiency, and improving process quality which RPA is meant to drive.

‍RPA with AI

Deployment of an RPA bot without AI is like a hammer driving a nail. When confronted with an obstacle, the hammer cannot perform, as it is supposed to. An RPA bot does its job regardless of the input, it can cause damage and possibly shut down workflow.

A higher level of intelligence to assess, learn, and optimize the outcomes is required to make better decisions and make automation resilient. AI systems with RPA bots enable decision-making based on a deep understanding of the process from all the data available using natural language processing. Deep learning AI is a more adaptive, algorithm-based technology that can mimic human thinking and intelligence and extract and analyze vast amounts of data. It can be trained to handle exceptions and repetitive tasks without human intervention. These models are upgraded periodically for more accuracy and efficiency.

‍Purpose of AI in Robotic Process Automation

Every automation should have Data at the core and the system should add intelligence to understand the data dimensions. After Data, next processes and then user.

Sourcing Data from input sources, processing it with intelligence and adding dimensions to make it rich are three activities in data-driven automation. Embedding AI in RPA addresses four needs that cannot be fulfilled just by RPA in the long term.

1. Adaptability: RPA bots have a limited understanding of everything. For example, an RPA robot can retrieve data from a known location. But if the layout changes, the robot will fail as the robot is not skilled enough to find the location of the data. AI-based recognition tools can continuously learn through machine learning, and the efficiency of the process improves. They can capture necessary information from any location, and the workflow continues without interruption.

2. Scalability: RPA robots are inflexible in the face of change, which prevents them from scaling when the organization requires upgrading. For smooth operations, the organization needs to constantly update its existing bots to handle whenever there is a variation. AI can learn from user corrections made in the past and take appropriate actions as and when required in the future, which means far less number of errors. It also means lesser involvement of human labor to build and update bots to handle new situations constantly. It gives human workers more time to focus on other strategic business activities like developing new technologies and new skills.

3. Extended scope: RPA bots can perform narrowly defined tasks with well-defined data, whereas artificial intelligence automation can be applied to numerous business cases. It can analyze data, perform data mining operations, and also identify process improvements all by itself. AI-led automation can also detect anomalies in data and processes without defining code for every possible action.

4. Resilient Automation Operations: AI can not only optimize Automation Operations by automating scheduling, optimizing utilization, and auto ticketing but it can also learn from error logs, take corrective action, and auto-heal bots. AI can fix the biggest reason for RPA project failure i.e. very high Operational cost.

‍Turbotic Automation with AI

Turbotic's end-to-end platform includes all the tools required to automate numerous business processes. It includes discovering the tasks that can be automated, measuring ROI, and everything in between. Automation and AI are complex. They are combined by bringing cognitive abilities to the RPA bots. It involves five stages:

• ‍Idea: Gather ideas across various business units to enable the democratization of Intelligent Automation.
• ‍Discovery: Analyze the business processes in the organization best suited for automation, and identify the specific RPA solutions to be used.
• ‍Build: Build best practices for RPA & Intelligent Automation solutions to shorten project lead time.
• ‍Control: Manage Intelligent Automation solutions and processes from multiple vendors in real-time.
• ‍Value: Track the ROI of the Intelligent Automation to analyze the automation investments in real-time.

Turbotic: Intelligent AI-led automation in one platform

An RPA without AI means frantically updating code and processing to keep up with changes. Even at its best, it can only automate a finite number of tasks within a clearly defined business process and not the whole process from end to end and takes a long time. This is a major disadvantage for the economic growth of the company.

Turbotic leverages AI and makes the platform more adaptive to change as it can constantly learn and optimize. It also offers organizations increased adaptability and scalability with an ever-extending scope for various applications in the foreseeable future.