Artificial intelligence is the key to unlocking the potential of big data. State-of-art technologies and innovative platforms are available in the era of digital transformation that not just help your organization to adapt modern tech stack but can provide the EDGE your organization may just be looking for. However, developing effective AI and automation systems requires a solid foundation in two key pillars: problem solving and systems thinking.
Problem solving involves the ability to identify, analyze, and evaluate different options to make informed decisions based on data and rules. This is a critical component of AI, which is designed to solve complex problems and make decisions based on data and rules.
Systems thinking, on the other hand, involves understanding the interconnectedness and interdependence of different components of a system. This is particularly important in designing and developing automation systems, as changes made to one part of the system can have significant impacts on other parts.
By combining problem solving and systems thinking, organizations can develop AI and intelligent automation systems that are more effective, efficient, and adaptable. This requires a multidisciplinary approach that involves experts from different fields, including computer science, engineering, mathematics, and psychology, among others.
Advanced technologies like Robotics Process Automation (RPA), Business Process Management (BPM), Artificial Intelligence (AI), Cognitive Automation, and Infrastructure Automation can help businesses automate redundant tasks, accelerate delivery, enhance quality, and streamline business processes. Machine learning, a branch of AI, has made it possible to handle tasks that require judgement and decisions in business operations.
With the right tools, people, and procedures, businesses can develop fully automated workflows that streamline their operations, reduce mistakes, and cut costs. By leveraging the power of AI and intelligent automation, organizations can stay competitive in the era of digital transformation and gain an edge in the market.
By eliminating human inputs, process automation streamlines a system, reducing mistakes, accelerating delivery, enhancing quality, cutting costs, and streamlining the business process. To develop a fully automated workflow, combines software tools, people, and procedures.
Advanced technologies help your business by automating the redudant tasks of your business operations. The advancements in Machine Learning has made it possible to handle the tasks requiring judgement and decisions in business operations. Let’s deep dive into this topic by studying its different branches listed below:
- Robotics Process Automation (RPA)
- Business Process Management (BPM)
- Artificial Intelligence (AI)
- Cognitive Automation
- Infrastructure Automation
Robotics Process Automation (RPA)
To achieve operational excellence using RPA we must pay attention in breaking down or simplyfying the business process. As you have the process divided into most granualar level tasks. You should look to label or categorize the tasks that totally repetitive without the requirement of any on the go learning or intelligence. Similarly label the tasks requiring criticial thinking to perform it. Its important to indentiy the complex tasks within a process while implementing RPA to achieve the excellence in opeartions. With growing field of RPA as technology many platforms have launched RPA tools. Let’s get familiar with some of the popular ones in the market:
- Automation Anywhere
- UI Path
- Microsoft Power Platform
- Blue Prism
Microsoft Power Platform
It seems that Microsoft have very broad future perspective for its Power Platform. Its aim is to provide end-to-end automation solutions for businesses. This platform consists of 5 pillars empowered with 3 innovative features:
- Power Apps
- Power Automate
- Power BI
- Power Virtual Agents
Power Apps offers an efficient low-code development environment for creating unique business apps. For easy connection and interaction with current data, it provides services, connectors, and a scalable data service and app platform (Microsoft Dataverse). Power Apps makes it possible to create cross-platform web and mobile applications.
Power Automate: Users can build automated workflows between applications and services using Power Automate. It aids in automating routine business procedures like data collecting, approval of decisions, and communication.
Power BI (Business Intelligence): Power BI is a most widely used business intelligence tool. Business analysts provide insights by running various analysis on business data. To help people make quick, well-informed decisions, it may communicate these insights through the data visualisations that make up reports and dashboards. Businesses can concentrate more on using data rather than managing it thanks to Power BI’s scalability across an organisation and built-in governance and security.
Power Virtual Agents: With the support of a guided, no-code graphical interface provided by Power Virtual Agents, anyone can make effective chatbots without the assistance of data scientists or engineers. By allowing subject matter experts to deploy and maintain a custom solution themselves, it reduces the amount of IT work needed to do so.
The features that make Power Platform to be the most Powerful RPA tool amongst all:
Microsoft Dataverse is a scalable data service and app platform that enables users to safely store and manage data from various sources, integrate that data in business applications using a common data model to ensure ease and consistency for users, and present that data to customers in a consistent and standardised way. The Microsoft Power Platform’s components can communicate with one another thanks to Microsoft Dataverse. It serves as the framework for the data consolidation, visualisation, and manipulation processes.
AI Builder: Users and developers can add AI capabilities to the processes and Power Apps they design and utilise using AI Builder. With the help of a turnkey solution called AI Builder, you can quickly incorporate intelligence into your workflows and apps and forecast outcomes to help boost business performance.
Connectors: You may link apps, data, and devices in the cloud via connectors. Consider connectors to be the command and information highway. Microsoft Power Platform has more than 600 connectors, allowing all of your data and actions to connect seamlessly. Salesforce, Office 365, Twitter, Dropbox, Google services, and more are a few examples of well-known connectors.
- Infrasturcture Automation
With ever evolving modernisation, improvment and growing complexity it has become difficult for IT to keep up and deliver the Performance without the risk of downtime. There are so many variables that remain unconsidered while an organization aim to achieve consistency and optimization. By bringing intelligent automation as a solution to IT infrastructure, the risk of downtime can be handled along with the streamlining of IT process.
RPA vs Automation
RPA (Robotic Process Automation) and Automation are related but distinct concepts. Automation refers to the use of technology to automate tasks and processes, reducing the need for human intervention. RPA specifically refers to the use of software robots to automate repetitive, routine tasks that were previously performed by humans. RPA provides a more efficient, scalable and cost-effective solution for these tasks, freeing up human resources to focus on higher value tasks.
The Power of RPA: Real-life Use Cases and Benefits
The Power of RPA: Practical Use Cases and Advantages
Explore the potential of RPA with examples from actual applications in various industries. Learn how businesses are cutting expenses, improving efficiency, and streamlining operations.
RPA, robotic process automation, automation, use cases, effectiveness, streamlining, and cost cutting are some related terms.
RPA in the banking, healthcare, manufacturing, and customer service sectors
Introduction: RPA, or robotic process automation, has revolutionised the way businesses streamline operations and boost productivity. Many diverse industries have adopted the technology, which offers firms significant advantages in terms of decreased costs and increased accuracy. In this article, we’ll examine several actual RPA use cases and examine the advantages they provide.
Use Case 1: RPA in Banking
One of the first sectors to use RPA was the banking sector. RPA is being used by banks to automate repetitive operations including loan processing, account opening, and fraud detection. RPA enables banks to speed up operations while improving accuracy, which improves the client experience.
Use Case 2: RPA in Healthcare
RPA is being used by healthcare organisations to increase the effectiveness of their processes. RPA can be used, for instance, to automate processes like managing medical records, processing insurance claims, and arranging appointments. This facilitates the reduction of mistakes and processing times within healthcare institutions, improving patient outcomes.
Use Case 3: RPA in Manufacturing
RPA is being used by the manufacturing sector to automate repetitive and routine processes. RPA can be used, for instance, to automate quality control, inventory management, and the production process. This leads to increased efficiency, reduced costs, and improved product quality.
Use Case 4: RPA in Customer Service
Another area where RPA is having a significant influence is customer support. RPA is being used by businesses to automate processes including answering consumer questions, giving out product details, and addressing complaints. This aids businesses in providing better, quicker customer service, which raises client happiness. These are just a few of the numerous RPA use cases that are having an impact across various industries. RPA gives businesses the potential to streamline operations, boost productivity, and cut costs by automating repetitive and regular procedures. It’s intriguing to see what other advantages the technology will eventually provide as it continues to proliferate.
Some of the most prevalent RPA use cases are covered in above post.
Intelligent Automation
In today’s technology-driven world, businesses are continually looking for ways to improve their processes, increase efficiency, and drive growth. One of the ways businesses can do this is through the use of automation. Automation refers to the use of technology to automate repetitive or time-consuming tasks, freeing up employees to focus on more complex and value-added work. In this article, we will explore different use cases for automation and discuss open source and paid tools and technologies related to them.
Use Case 1: Marketing Automation
Marketing automation is the use of technology to streamline and automate marketing tasks and processes. It includes email marketing, social media marketing, lead generation, and lead nurturing. By automating these tasks, businesses can save time, increase efficiency, and improve the overall effectiveness of their marketing campaigns.
Example: A company might use marketing automation software to send out personalized emails to customers who have abandoned their shopping carts on the company’s website. The software can automatically send these emails with specific discounts or incentives to encourage the customer to complete their purchase.
Open Source Tool: Mautic is an open-source marketing automation platform that provides a range of features, including email marketing, lead generation, and lead nurturing.
Paid Tool: HubSpot is a paid marketing automation tool that offers a range of features, including email marketing, social media marketing, lead generation, and lead nurturing.
Use Case 2: HR Automation
HR automation refers to the use of technology to streamline and automate HR tasks and processes. It includes recruitment, onboarding, employee engagement, and performance management. By automating these tasks, businesses can save time, reduce errors, and improve the overall employee experience.
Example: A company might use HR automation software to automate the onboarding process for new employees. The software can automatically send out welcome emails, provide access to company policies and procedures, and track the progress of the onboarding process.
Open Source Tool: OrangeHRM is an open-source HR management platform that provides a range of features, including recruitment, onboarding, and performance management.
Paid Tool: BambooHR is a paid HR automation tool that offers a range of features, including recruitment, onboarding, employee engagement, and performance management.
Use Case 3: Finance Automation
Finance automation refers to the use of technology to automate financial tasks and processes. It includes accounts payable and receivable, financial reporting, and budgeting. By automating these tasks, businesses can save time, reduce errors, and improve financial accuracy.
Example: A company might use finance automation software to automate the accounts payable process. The software can automatically match invoices to purchase orders, route them for approval, and generate payments to vendors.
Open Source Tool: Apache OFBiz is an open-source finance automation platform that provides a range of features, including accounts payable and receivable, financial reporting, and budgeting.
Paid Tool: QuickBooks is a paid finance automation tool that offers a range of features, including accounts payable and receivable, financial reporting, and budgeting.
Conclusion:
Automation has the potential to transform the way businesses operate. By automating repetitive and time-consuming tasks, businesses can save time, increase efficiency, and improve the overall effectiveness of their operations. There are both open source and paid tools and technologies available for businesses to use, depending on their specific needs and budget. It is important for businesses to carefully consider their automation needs and choose the right tool or technology for their unique situation.
End to End Document Automation using Microsoft Power Automate and Power Platform
- Receive Documents -Get paperwork from your clients
- Extract – Information from the papers is extracted.
- Validate – If necessary, a person can verify or amend the retrieved data.
- Export -Export the data you’ve retrieved to your ERP or another data storage system of your choosing.
- Monitor – Use the offered monitoring tool to monitor your entire procedure.
For processing documents at scale, Power Automate and the Power Platform offer a comprehensive solution that includes AI-powered data extraction, human validation, process orchestration, pipeline monitoring, and more.
One model may be trained to handle up to different document formats. A model like that may comprehend newly acquired formats. Thus, you may perhaps oversee hundreds of vendors.
This solution may be altered to include more models and some logic to guide users to the right model. Just update the fields list in the setup section of the validation application and add new labels to the model.
In case of requied supervision, the data extracted may be examined and changed in the validation programme. Also, you may retrain the model using the problematic documents to increase its accuracy for next processing.
To execute any flow that orchestrates the process, you just need one Power Automate licence.
Each user who must manually evaluate and approve documents needs a Power Apps licence.
The process owner needs a Power Apps licence to setup the process.
Based on the number of documents handled each month, AI Builder capacity. The AI Builder calculator enables you to determine the ideal capacity.
What is an AI Platform?
An AI platform is a software system or framework that provides developers and data scientists with tools and infrastructure to build, test, and deploy various types of artificial intelligence applications. An AI platform typically includes a set of tools for data management, model development and training, and deployment and monitoring of machine learning models.
AI platforms can be used to build a wide range of applications, from chatbots and virtual assistants to predictive maintenance and fraud detection systems. Some examples of popular AI platforms include TensorFlow, PyTorch, and Keras, which are open-source frameworks for building machine learning models, as well as cloud-based platforms such as Amazon SageMaker, Google Cloud AI Platform, and Microsoft Azure Machine Learning, which provide a wide range of AI services and tools for businesses and developers.
Automated Machine Learning (AutoML)
The process of automating the activities involved in using machine learning to solve real-world issues is known as automated machine learning (AutoML).
From starting with a blank dataset through deploying an actual machine learning model, AutoML may
focus on different phases of the machine learning process. By offering tools like AutoGluon, TransmogrifAI, Auto-sklearn, NNI, and H2O AutoML, it tries to simplify these stages for non-experts and make it simpler for them to apply machine learning.
For systems like H2O, MLBoX, and Spark, these packages allow automatic model selection and ensembling in addition to capabilities like hyperparameter tweaking and model assessment.
AutoML is a crucial tool for academics since it aims to reduce human error while still producing reliable
results. It may be utilised in a variety of industries, including robots, image identification, and natural language processing.
Exploring the Four Types of Artificial Intelligence: Reactive Machines, Limited Memory, Theory of Mind, and Self-Aware Systems
- Reactive Machines: Reactive machines are the most basic form of AI. They do not have the ability to form memories or use past experiences to inform future decisions. Instead, they rely on their current inputs to make decisions in real-time. Examples of reactive machines include chess-playing computers and self-driving cars.
- Limited Memory: Limited memory AI systems have the ability to use past experiences to inform future decisions. These systems can store and recall information, but they cannot reason about it or make decisions based on it. Examples of limited memory AI systems include facial recognition software and recommendation systems used by online retailers.
- Theory of Mind: Theory of mind AI systems have the ability to understand the mental states of others, such as their beliefs, desires, and intentions. These systems can predict how other people will behave based on their mental states. Examples of theory of mind AI systems are still mostly in the research phase, but they could have applications in fields like psychology and human-computer interaction.
- Self-Aware: Self-aware AI systems are the most advanced form of AI. These systems have a sense of self and can understand their own emotions and motivations. They can also understand the emotions and motivations of others. Self-aware AI systems are currently the stuff of science fiction, but some researchers believe they could become a reality in the future.
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