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Intelligent Automation or Cognitive Automation is the combined use of numerous automation technologies – Business Process Management (BPM), Artificial Intelligence (AI) and Intelligent Process Automation (IPA).
The chief purpose of Intelligent Automation is to significantly scale and streamline decision making across organizations.
Among other things, Intelligent Automation:
Intelligent Automation has manifold applications across several industries. An automotive manufacturer might use IA to reduce the risk of human error or speed up production.
A life science or pharmaceutical company can leverage intelligent automation to cut down costs and improve resource efficiencies. Similarly, an insurance provider can use intelligent automation to address compliance requirements and calculate rates.
Since large amounts of data are used in Intelligent Automation and the calculations are accurate to the last decimal point, the benefits are manifold and cut across a range of industries. Here are the key benefits of automation:
Automation of processes and systems and using data and analysis to ensure accuracy can significantly accelerate the production capacity. IA adds to the ability of a business to scale up faster while mitigating risks and without compromising quality.
Repetitive tasks are prone to human errors which are effectively weeded out through the strength of decision-making powered by artificial intelligence.
Customer experience is vastly improved by providing answers to queries faster (preferably immediately). Artificial intelligence facilitates a richer and more positive experience for the customer, giving a company an edge over its competitors.
Several industries must adhere to specific regulatory practices and policies. With intelligent automation, the entire approach to compliance can be made consistently based on precedence and best practices.
Intelligent automation is instrumental in streamlining processes that are otherwise heavily dependent on legacy
systems or laborious tasks. These systems and tasks have historically been costly, resource-intensive and human-error-prone. Consequently, IA applications are found in several industries.
Life Sciences
In highly regulated business environments such as drug production, calibration and measurement of products are among the most important processes - quite apart from the fact that a tremendous measure of effort goes into data collection and analysis as well. It is well established that without sufficient analysis and results, a drug trial cannot be said to be successful.
On the contrary, the creation and rapid scaling-up of the Covid-19 vaccines around the world is a stellar example of how automated processes have the potential to turn the life sciences industry on its head.
Automotive
The automotive industry is greatly affected by all the improvements manufacturers can collectively make with the help of intelligent automation.
With smart automation, manufacturers can better adjust and predict production patterns through varying degrees of supply and demand.
In the automotive industry, IA can also help in eliminating errors in production, procurement, support and other areas. In addition,
IA-powered robots can greatly help in alleviating manual labor and facilitate the discovery of production defects.
Insurance
In the insurance industry, the most time and resource-intensive tasks include paperwork on claims and appraisals. With IA, these tasks can be performed and simplified with nearly immaculate levels of accuracy.
Moreover, intelligent automation also empowers businesses to ensure they stay inside compliance regulations. To wit, these companies are also able to evaluate individual or organizational risk and calculate rates of insurance accordingly.
Healthcare
Healthcare businesses use intelligent automation systems with NLP (natural language processing) to homogenize the approach to data practices like collection, analysis and core medical practices like diagnosis and treatment.
With chatbots being increasingly used for remote healthcare appointments (and even diagnosis) requiring minimal human intervention, IA has already proved its effectiveness in the healthcare industry.
Intelligent automation goes beyond recording a list of manual steps and executing them. It is about using intelligent automation to automate complete business operations with fully integrated intelligent process automation and machine learning.
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The first thing that comes in handy is a handy list of tasks that the organization would like to automate. This typically includes tasks performed by developers across multiple locations.
First, all business processes must agree to embrace the new culture of automation. Selection and prioritization of tasks can begin based on different assessment criteria like the use of manpower, duration and frequency.
Most businesses make the mistake of prioritizing those procedures that are highly visible, mission-critical or seemingly promise greater savings. Those may not be necessarily the first processes an organization should look to automate. The best idea is to gun for short-term results that prioritize small and easy processes to deliver early value and prove operational effectiveness.
Intelligent automation will make no difference to a business if the implementation is flawed, irrespective of how well the former processes have been implemented. Implementing intelligent automation across specific processes will bring up technical challenges that are specific to the particular process.
Impeccable implementation requires business analysts with great expertise and experience to work closely with an RPA specialist or at the very least a change management specialist.
Once the identification is quietly in place, feasibility assessment develops deeper insights into each of these processes. Creating a checklist with all applicable eligibility criteria is the quickest way to assess automation feasibility.
The checklist works best when all facts of the business (including risk, legal, technical and other domain-specific attributes) are included along with clear terms of compliance defined at the beginning of the process.
It might seem an obvious thing to keep a track of automation processes that are already underway. However, several organizations tend to believe that IA is a self-sustaining process once it is set up.
Once set up, it is important to regularly monitor the process to ensure that the best RTO (Recovery Time Objective) is in place.
For the start, it is highly recommended to onboard (even if temporarily) an RPA developer to participate in the workshop and explain some of the technicalities of the automation process which might not be obvious to attendees with a non-technical background. It is a given that the business analyst should be greatly familiar with the industry or at least their respective business processes. The redesigned automation process will be heavily informed by incoming insights from these analysts.
Note: Some intelligent automation programs work with the mission to reorganize entire operating models and business processes. However, such endeavors must be undertaken only when: