Building a Smarter Enterprise with Intelligent Automation – II

Contributors:

Dr Shailee Choudhary , Consultant & HOD - AI/ML New Delhi Institute of Management (PGDM)

Manish Bhardwaj , Director Software Development & Professional Services from Concentrix

Jas Gaurav Singh , from Nichols College, Massachusetts, USA 

have partnered to launch a knowledge series on Digital Transformation focusing on Intelligent Automation. This article explores various automation use cases and analyses a specific business scenario, highlighting problem statements, pivotal driving factors, process flow, pain points or catalysts that led to the need for automation.

In our previous article, we introduced the concept of Intelligent Automation, a transformative approach that can revolutionize business processes. In order to remain competitive, businesses need to embrace automation and streamline their operations. Adopting intelligent automation is a paradigm-shifting strategy that can restructure and transform how your business runs. Among its myriad of benefits are increased productivity, heightened efficiency, cost savings , resource optimization, reduced Time to Market (TTM) of launching new products, a reduction in manual efforts and enhanced customer service. In addition, we have explored the potential of AI-powered Robotic Process Automation (RPA) to handle unstructured data and execute cognitive tasks, such as natural language processing, image recognition and intelligent decision-making. This article explores various automation use cases and analyses a specific business scenario, focusing on problem statements, pivotal driving factors or catalysts that led to the need for automation. Focusing on gaining a comprehensive understanding of the process flow required to create intelligent automation tailored specifically to the identified business scenarios.

Intelligent Automation Solution landscape

You can reimagine how your business operates with Intelligent Automation (IA) regardless of what industry you are in, and can realize multiple benefits specifically in process-intensive and document-centric industries.  Intelligent Process Automation has many use cases and can give outstanding efficiency, accuracy and cost benefit in a variety of sectors, departments, and roles.

• Banking and financial insurance, micro finance companies,

o   Build precise credit models

o   OCR based quick document check and process B2B & B2C loans

o   Financial trade execution

o   Chatbots for improved customer engagement and query resolution

o   Insurance claim processing

o   Client profiling and risk assessments

o   Procure to Pay

o   Order to Cash

o   Record to Report

Distribution companies, manufacturing, SCM / logistics (road transport, shipping),

o   Reseller and Supplier creation

o   Optimize logistics (transport) routes

o   Identify and eliminate bottlenecks in supply chain

o   Complex order processing involving thousands of SKUs leading to

o   Blocking inventory

o   Steady state analysis

o   Raising PO on OEM/manufacturers

o   Complex invoicing

o   Vehicle assignment and tracking including statutory documentation

o   Returns processing

HR Services (Hire to Retire)

o   Data entry

o   Payroll

o   New hires, transfers, exits

o   Time and attendance mgmt

Healthcare, pharmaceutical and medical device OEMs

o   Document handling

o   Patient registration to exit process

o   Patient Invoicing

o   insurance documentation

o   order processing

o   Steady state analysis

o   Raising PO on OEM/manufacturers

o   Staff scheduling

Use Case – Intelligent Automation of Invoicing Workflow

Let’s explore the applications of Intelligent automation in Finance functions in the middle and back office operations namely Accounts Payable/ Accounts Receivable, Financial Reporting and validating journal entries etc. This process workflow hardly changes which makes it a perfect candidate for software robots to take over.

In this article, we will analyze IA application for optimizing one of the key financial functions for any business i.e. Invoicing Automation. Corporates can have thousands of suppliers with invoices receives in multiple formats, layouts and templates (both semi and unstructured). Invoice Processing aims to streamline Accounts payable processes, which involves scanning and extracting invoice data, vendor invoice processing, vendor verification and streamlining the data into an ERP or accounts payable system. The invoicing processing system is extremely complex due to its dependency on varied factors like huge monthly volumes, varying formats and business rules specific to different supplier types. The popularity of IA based invoicing workflow grew during the pandemic as it delivers time effective, scalable solution to address all the pain points in the legacy invoice processing system.

Below we summarize a basic IA Invoicing workflow, as each business requires a customized approach to design an automated solution aimed to minimize human involvement and retain the high level of accuracy.

The depicted case automates the reconciliation of entire process of invoice processing with required control points, rule-based validations and verifications. Its teamed up RPA with state-of the art technologies such as Artificial Intelligence and Machine learning to drive intelligent automation that delivers reduction in turn around time and manual efforts.

Impetus Points (ROI derivatives)

The data presented in this article is derived from a real-world business case that evaluated the effectiveness of an RPA combined with AI solution. To assess its impact, we conducted a thorough analysis of the time and motion needed to complete a task, utilizing a Time Motion study methodology.

We have customized the data to provide a clear and illustrative explanation. The analysis was conducted on seasoned Finance Executives whose primary responsibility was to approve or reject invoices. These invoices were notably intricate, comprising a minimum of 15 distinct Stock Keeping Units (SKUs) with varying quantities and falling under different GST (Goods and Services Tax) brackets.

Efficiency Calculations for one month:

o   Human Throughput

1. person hours (1 person day) : 17 invoices
2. 1 person hours : 17/8 invoices : 2.125 invoices
3. 176 person hours (1 month of 22 days) : 176*17/8 invoices = 374 invoices

o   Bot Throughput

1. 1 person day : 240 invoices
2. 1 month (of 30 days) : 30*240 = 7,200 invoices = 19.25Therefore, in 1 month: deliverable of 1 bot = deliverable of 19.25 humans

Transformation: Pain Points

When it comes to the deployment of Intelligent Automation (IA), majority of businesses are still in their early phases. Even when there is no shortage of buzz around these technologies, there are a number of issues that must be resolved before they can be fully utilized by enterprises. We'll look at some of the main obstacles to integrating Intelligent Automation in businesses in this post.

1. Data Silos: A data silos is an isolated data repository within an organization that hinders seamless data sharing and analysis. In order for Intelligent Automation to be effective, data needs to be collected from a variety of sources and processed in a centralized location. Businesses that have traditionally operated with siloed data stores will face a challenge with AI & RPA, since they need to collect and process data from a variety of sources. For instance, in a manufacturing company, sales data might reside in a separate database from production data, which makes it difficult to analyze the relationship between sales trends and production efficiency. To overcome this challenge, businesses must implement data integration and strategies to break down these silos and facilitate the flow of information across the organization for efficient operations.
2. Resistance to change: This challenge poses a considerable obstacle, driven by employees apprehensions about potential job insecurity, financial/budgetary constraints and ethical dilemmas tied with adoption of Intelligent Automation. With thoughtful strategic planning and meticulous execution, these challenges can be mitigated.
3. Security and Privacy: When implementing AI and RPA within an enterprise, it is important to consider the security and privacy implications of these technologies. Here are some key considerations:

• Data security:  AI and RPA rely on data to function, and so this data should be secure. This means ensuring that only authorized users have access to it. (e.g. using encryption).
• Privacy: Another key consideration is privacy. When collecting and storing data, enterprises need to ensure that they comply with all relevant privacy laws and regulations. This includes ensuring that individuals have given their consent for their data to be used, and that it is being used in a transparent way.
• Third-party risk: When using third-party AI or RPA services, enterprises need to carefully consider security and privacy implications. They need to understand how these services work, and what security measures are in place to protect their data.

Conclusion

To sum up, Intelligent Automation is here, and its set to create a sustainable business ecosystem to help businesses streamline their operations, reduce costs, achieve competitive edge and overall efficiency. As the bots handle the monotonous tasks for you, your team can invest time in a smarter way. Intelligent automation combines a range of technologies that helps in streamlining business process workflow and is well suited to analyze unstructured, semi-structured and structured data. IA is transforming industries across the board, so buckle up and get ready to join the future of business!