What is IPA?11 April 2021
Intelligent Process Automation - the final frontier of the next-generation operating model!
Post the global recession of 2007–09, businesses, lean business process management and smart workflow management have become the primary focus of many companies. An operating model that not only improves cost efficiencies, customer satisfaction, and employee engagement but also simultaneously enhances productivity is the most dependable one rather than the rule-based RPA. That’s where IPA comes into the picture, converging AI with RPA.
Is your process operating model intelligent enough to address your process needs?
With this key question, the need for IPA comes in as the ultimate answer. IA, which comes under the umbrella of AI, is basically a set of latest technologies - a new RPA revolution that combines RPA with the power of AI, and the latest development in ML & data analytics.
- According to KPMG, enterprise investments in IA technology will potentially reach $232 bn from its present value of $12.5 bn by 2025. This will increase the large scale adoption of Intelligent Process Automation Technology across several industries.
- As per the prediction of Gartner, the RPA will become the fastest-growing software category by 2019 as the industry will grow by 57%. In fact, with a potential impact of $5 to $7 trillion by 2025, RPA is expected to perform tasks as much as 140 million full-time employees do. This makes the IPA market even more promising.
What is Intelligent Process Automation (IPA)?
- Intelligent Process Automation (IPA) or IA is an application of Artificial Intelligence.
- With the help of document AI technology, it helps you automate your document processing pipeline in no time using deep learning and other advanced AI and ML techniques in the cloud.
- It can automatically extract information from any complex or unstructured documents without having to take any manual input.
- It clearly indicates IA’s capabilities if learning and improving day by day.
- IA is a convergence of technologies that produces automation capabilities and quantifiably elevates business value along with competitive advantages for businesses.
The technologies in Intelligent Process Automation
RPA + AI = Intelligent Process Automation
IPA is a convergence of AI, RPA, machine learning and related technologies such as computer vision and cognitive automation. These technologies together contribute to richer automation capabilities and possibilities, opening doors to even more business value for companies. The core technologies in Intelligent Process Automation include:
Smart workflows is for
- Real-time monitoring of the status of the entire process
- Management of handoffs between systems and human beings
- Providing statistical data on issues, blockages and bottlenecks
- Enhancement of the process visibility
- Improvement of operational efficiency
Machine learning is for
- Prediction based on data inputs and analytics
- providing insights on recognized patterns
- Natural Language Processing (NLP) is for
- Interpreting text-heavy communications
- Understanding the intent of the communication
This technology helps IA enable chatbots and virtual assistants to understand and interpret spoken or written language from the text-inputs.
- Natural Language Generation (NLG) is for
- Creation of narratives from the data collected
- Presenting a better story for decision making based on the data gathered
- Cognitive agents for building a virtual workforce capable of executing more sophisticated tasks with intelligence, such as
- Carrying out tasks
- Learning from data sets
- Communicating with human users
- Offering customer engagement suggestions to human supervisors
Intelligent Process Automation Use Cases:
What might IA look like when in action? Let’s take this example of an insurance company:
- A human claims employee collects data from 13 different systems to provide a “business as usual” service. In case of IPA, RPA can help eliminate manual clicks, NLG can comprehend text-heavy communications, ML can help make decisions that don’t have to be preprogrammed, cognitive agents offer customers suggestions, and smart workflows provide real-time tracking of handoffs between systems and people.
- One large BFSI institution leveraged an RPA transformation at scale to automate more than half of the tasks in record-to-report processes. This created annual run-rate efficiencies of 30 percent or more for them.
- Using IPA methodology, another organization achieved an 80 % of reduction in their processing and operational costs in excess queue procedures.
- Another financial institution in the FT500 used IPA to unlock an annual reduction of £175 million in operational costs and save over 120 FTEs.
A study by McKinsey reported enterprises across global industries have been deploying IPA, with the significantly impressive outcome:
- Automation of 50 to 70 % of human tasks is done without any human interference as technology enables machines to possess cognitive capabilities.
- 20 to 35 % annual run-rate cost efficiencies gained by companies post the implementation of IPA’s role in the operating model.
- A massive reduction in straight-through process time of 50 to 60% has been discovered.
- Improved ROI most often in triple-digit percentages found
How IPA Used in Different Industries
- Claim Automation in Insurance:
- According to a study by McKinsey, it predicts that automation will be reducing the cost of the claims journey by around 30%, while Autonomous shows that the insurance industry is set to have a massive reduction of $1.5 trillion by automating claims processing. The data says it all.
- Most of the claims processes in conventional insurance companies need hours of manual effort by respective employees while IPA platforms can enable automation in most of those important yet routine steps.
- IPA helps in porting over customer data from completed claim-forms into the company’s CRM systems or other database using field mapping. In case of printed forms, information can be scanned, digitized, and ported into the database as well.
- This eliminates repetitive click work and long hours of data entry jobs for the employees. Hence, improves the process efficiency, reduces the operational costs, risks and time; above all enhances the overall customer experience.
- Mortgage Cross-Sell in Banking:
- According to a Mortgage Bankers Association performance report, the average acquisition value for a mortgage customer sits at $7,747 while 80% of banking customers do not have their primary bank connected with their mortgage.
- Banks want to reduce these acquisitions costs as much as possible. They can decrease their acquisition costs by cross-selling mortgages to their existing customers.
- With IPA platforms, the banks have access to vast amounts of business data, which can alert bankers while existing users exhibit home buying behaviour. It can also help suggest which loan products the banker should cross-sell to them based on their data profile.
- Imagine it’s the personal tax season again and a never-ending barrage of tasks is haunting you down. IA can automate literally 80% of the work. A report from Accenture in 2015 predicted that by 2020, 40% of transactional accounting work will be automated.
- Most people think automation will kill CPA however the fact is surprising. IPA technology allows automating tasks that typically require human intellect, including visual perception, speech recognition, decision-making, and even translation.
- In an accounting department, IPA technology can help the accounting software learn to perform analyses automatically and draw conclusions without any human intervention.
- Tasks such as invoice categorization, bank reconciliations, and auditing expense submissions etc can also be automated.
- As the interest in applying IPA in law is increasing, the Legal Sector is slowly transforming - from the lawyers’ billable hours computed automatically to the prediction technology to help forecast litigation outcomes.
- Law firms and professional services companies want to make the best decision in their businesses where IPA can automate the workflows of its professionals.
- IPA helps litigators perform due diligence to discover background information including contract review, legal research and electronic discovery etc.
- Legal analytics pulled by the IPA tools help the lawyers use the data points from past case law, win/loss rates and even the history of a judge to be used for trends & patterns.
- IPA tools can be used in document automation in legal firms in order to create filled out documents as per the data input.
- Identification of the suitable automation tool or even the combo of, compliance and security totally depends on the requirements of a particular organization.
- IA can help leverage advanced analytics in compliance, risk management, and continuous monitoring programs
- IA can provide data insights, conclusions, and predictions by-
- Utilizing data inspection
- Cleansing and modelling
- A typical sales cycle of a customer goes from prospecting to closed and from closed to onboarded - this entire cycle involves multiple processes like massive amounts of data exchange, messages, requests and more.
- Classifying the tasks performed and understanding these unstructured data using the IPA tool can help your organization improve the performance of Sales and Support
- Customer support automation & analyses replaces the entire effort for manual content review. It not only speeds up cycle time to identify relevant data and highlight outliers but also discovers new insights within the existing unstructured data.
- RFP Analysis and Composition help analyze past RFP’s to determine the winning language. Natural language processors (NLPs) can scan and determine the intent of text input (message), based on which it helps generate an automated response addressing the query.
- For example, the text inputs in the chatbots determine the level of urgency in the customer request, the sentiment (e.g. anger or frustration or disappointment) by leveraging NLP. Managing the interaction according to the level of severity/priority hence becomes easier.
- Sales and workflow automation helps in content mining to identify sales opportunities, discovery and CRM workflow automatically. IPA also helps manage the river of customer input received across the internet, email, and CRM.
- IPA helps align your massive & rapid datasets as per your domain needs in a few days.
- IPA components, supported by API's can meet the requirement of extracted & structured data into your downstream systems.
- With Research Process Automation, you can make a machine to perform the research on behalf of a human employee, it automatically reads and assists you in analyzing the 100-page research report in less time than would take a couple of days for a human.
- IPA can impact the Corporate Actions Process Automation by enabling to make better investment decisions with the help of high-quality complete data
The benefits of Intelligent Process Automation:
Intelligent Process Automation helps enterprises bring more operational and business efficacy:
- Increases process efficiency
IA not only improves accuracy but also fastens the processing speed by reducing process handling times. It helps businesses improve process analytics and management data by concentrating on the improvement of the bottlenecks of the process.
1.Reduces operational costs as well as risks
When a good portion of business processes are performed without having to spend any human hour, the entire operation runs more efficiently. IA ensures significant productivity with higher service levels. It can immediately deliver a significant reduction in expenditure.
When the work is being automated, it’s done round-the-clock, completed faster and more accurately due to faster decision making.
2. Lower cost drives maximized workforce productivity
IPA helps reduce the document capture processing time by 67% while the labour costs decrease by up to 50%. This translates to comparatively lower operational costs, better productivity and shorter workflow cycles.
3. Improves customer experience
IPA leverages ML algorithms and Natural Language Processing (NLP) to capture and understand text inputs. IPA acts autonomously and improves over time without any human intervention.
4. Brings effectiveness in monitoring and fraud detection
In today’s industry trend of outsourcing, the more the so-called busy companies work with external parties, the more the risk of fraud increases. Thanks to the key AI tools, such as Machine Learning, you get easy monitoring as a solution that provides better control and visibility.
IPA Vs RPA - The key difference:
Being a conventional technology, RPA has restricted capabilities when implemented alone. It can perform any repetitive action but can not consider nuances or exceptions. IPA does not only perform tasks but can take decisions faster than a human employee without any human instruction or interference.
The difference between RPA and IPA is similar to that between basic process automation and intelligent process automation.
Below are the limitations of IPA vs RPA:
IPA incorporate with-
- Artificial Intelligence (AI)
- Teaching versus programming
- Natural language recognition and processing
- Digestion of super data sets
- Predictive analytics
- Hypothesis generation
- Evidence-based learning
RPA incorporate with-
- RPAMacro-based applets
- Screen scraping data collection
- Vision-type building blocks
- Process mapping
- Business Process Management (BPM)
- Pattern recognition
- Ability to work with unstructured data
Companies are in need of a technology that mimics human intelligence itself and not just the activities carried out by them, especially, when we are undergoing a massive digital transformation in all the industries and verticals. These emerging needs of modern enterprises can not be sufficed with rule-based RPA.
IPA empowers leaders to make many complicated decisions and get the most out of years of investments in a multitude of intricate systems at the same time. When automated, any given task is done faster than a smart human employee. IPA leaves the human mind to tackle more complex and important tasks that need human intelligence.
Contact Botminds and book a demo to explore how IPA can help business can transform your business with intelligent workflow processing automation tools and futuristic operating models.
Frequently Asked Questions (FAQs)
What’s the difference between RPA and IPA?
- RPA was designed to mimic activities carried out by humans while IPA is an evolved technology that mimics human intelligence.
- RPA can only perform automation within its predefined process while IA can perform automation without anything explicitly programmed.
What is IPA in automation?
- IPA is an evolved version of automation technology that enables machines to possess cognitive capabilities such as comprehending a huge amount of structured and unstructured data, analyze it, understand and learn it at the same time.
- IPA does not only mimic and perform human activities but also captures human intelligence.
What is the purpose of intelligent process automation?
The purpose of designing IPA is to empower businesses with advanced technologies and agile processes for a smarter, better, and faster future of business process, workflow and operating models; and empower the users to live a next-generation customer experience.
What are the core components of process automation?
- IPA combines RPA and AI with other related technologies such as computer vision technology, machine learning, natural language processing and speech recognition.
- Machine learning algorithms help it gather, organize, track, analyze, store and make sense of a vast amount of valuable data.
- IA uses this data for improving the existing operations, addressing or up-to-the-minute correction of any issues, promptly forecasting the needs and developing the best practices.
What factors do clients consider when employing IPA?
- Combining RPA and AI, IA is an intelligent automation system
- Machine learning algorithms help IA use a large volume of data to learn patterns and predict outcomes as well as act autonomously without any human intervention
- The data gathered by ML helps it perform analytics efficiently
- It enables you to calculate risk in real-time
- It helps monitor suspicious transactions
- Easily verifies transactions more accurately
- Quickly notifies account holders in case anything is suspicious