1.4 Key technologies driving business process automation
3 min read•august 7, 2024
Business process automation is revolutionizing how companies operate. Key technologies like AI, machine learning, and robotic process automation are streamlining workflows and boosting efficiency. These tools are changing the game, allowing businesses to work smarter and faster.
Data analytics and are also crucial players. They're helping companies make sense of vast amounts of information and make better decisions. Plus, new tech like IoT and are opening up exciting possibilities for connectivity and security in business processes.
Intelligent Automation
Artificial Intelligence and Machine Learning
Top images from around the web for Artificial Intelligence and Machine Learning
Helge Scherlund's eLearning News: AI vs. Machine Learning vs. Deep Learning | Artificial ... View original
Is this image relevant?
AI vs. Machine Learning: Key Differences - IABAC View original
Is this image relevant?
Machine Learning View original
Is this image relevant?
Helge Scherlund's eLearning News: AI vs. Machine Learning vs. Deep Learning | Artificial ... View original
Is this image relevant?
AI vs. Machine Learning: Key Differences - IABAC View original
Is this image relevant?
1 of 3
Top images from around the web for Artificial Intelligence and Machine Learning
Helge Scherlund's eLearning News: AI vs. Machine Learning vs. Deep Learning | Artificial ... View original
Is this image relevant?
AI vs. Machine Learning: Key Differences - IABAC View original
Is this image relevant?
Machine Learning View original
Is this image relevant?
Helge Scherlund's eLearning News: AI vs. Machine Learning vs. Deep Learning | Artificial ... View original
Is this image relevant?
AI vs. Machine Learning: Key Differences - IABAC View original
Is this image relevant?
1 of 3
- simulates human intelligence in machines programmed to think and learn like humans
- AI systems can perform tasks that typically require human intelligence (visual perception, speech recognition, decision-making, language translation)
- is a subset of AI that enables systems to automatically learn and improve from experience without being explicitly programmed
- ML algorithms build mathematical models based on sample data (training data) to make predictions or decisions without being explicitly programmed to do so
- ML is used in a wide variety of applications (email filtering, computer vision, recommendation engines)
Robotic Process Automation and Natural Language Processing
- uses software robots or artificial intelligence workers to automate repetitive, rules-based tasks usually performed by humans
- RPA can automate data entry, form filling, data extraction, and other repetitive tasks across multiple systems and applications
- RPA can work with structured data (databases, spreadsheets) and unstructured data (emails, documents)
- is a branch of AI that helps computers understand, interpret, and manipulate human language
- NLP enables machines to read text, hear speech, interpret it, measure sentiment, and determine which parts are important
- NLP is used in various applications (language translation, sentiment analysis, speech recognition, chatbots)
Data and Analytics
Big Data Analytics and Cloud Computing
- is the process of examining large and varied data sets to uncover hidden patterns, unknown correlations, market trends, customer preferences, and other useful information
- Big Data is characterized by the 3 V's: Volume (large amounts of data), Velocity (data generated at high speed), and Variety (structured, semi-structured, and unstructured data)
- Big Data analytics helps organizations make data-driven decisions, improve operational efficiency, and gain a competitive advantage
- Cloud computing delivers computing services (servers, storage, databases, networking, software, analytics) over the Internet ("the cloud")
- Cloud computing enables organizations to store and process large amounts of data without the need for on-premises infrastructure
- Cloud computing provides scalability, flexibility, and cost-efficiency for Big Data analytics
Internet of Things and Blockchain
- The is a network of physical devices, vehicles, home appliances, and other items embedded with electronics, software, sensors, and connectivity which enables these objects to connect and exchange data
- IoT devices generate vast amounts of data that can be analyzed to gain insights and make data-driven decisions
- IoT is used in various applications (smart homes, wearables, industrial automation, smart cities)
- Blockchain is a distributed ledger technology that allows multiple parties to record transactions in a secure, tamper-proof, and transparent manner without the need for a central authority
- Blockchain ensures data integrity, transparency, and security through cryptography and consensus mechanisms
- Blockchain is used in various applications (cryptocurrency, supply chain management, digital identity, voting systems)
Democratized Development
Low-code/No-code Platforms
- enable users with little to no coding experience to develop applications through visual interfaces and drag-and-drop tools
- Low-code platforms require some coding skills, while no-code platforms allow users to build applications without writing any code
- Low-code/No-code platforms accelerate application development, reduce costs, and enable citizen developers to create applications without relying on IT departments
- Low-code/No-code platforms provide pre-built templates, modules, and connectors to integrate with various systems and data sources
- Examples of low-code/no-code platforms include Microsoft Power Apps, Salesforce Lightning, and Google App Maker
Key Terms to Review (9)
Artificial intelligence (AI): Artificial intelligence refers to the simulation of human intelligence processes by computer systems, enabling machines to perform tasks that typically require human intelligence, such as understanding language, recognizing patterns, and making decisions. AI is increasingly becoming a crucial element in various technologies that drive the automation of business processes, allowing organizations to enhance efficiency and effectiveness in their operations.
Big Data Analytics: Big data analytics refers to the process of examining large and varied data sets to uncover hidden patterns, correlations, and insights that can inform decision-making and drive business strategies. It leverages advanced tools and techniques, including machine learning and statistical analysis, to handle massive volumes of structured and unstructured data. This capability allows organizations to enhance operational efficiency, understand customer behavior, and predict market trends effectively.
Blockchain: Blockchain is a decentralized digital ledger technology that records transactions across multiple computers in such a way that the registered transactions cannot be altered retroactively. This technology ensures security and transparency, as every participant in the network has access to the same information, making it nearly impossible to manipulate data without detection. Blockchain's ability to eliminate intermediaries and enhance trust is particularly relevant in automating business processes, as it streamlines operations and reduces costs associated with traditional transaction methods.
Cloud Computing: Cloud computing is the delivery of computing services, including storage, processing power, and software applications, over the internet. This model allows businesses to access and utilize resources on-demand without the need for extensive physical infrastructure, enabling greater flexibility and scalability. With cloud computing, organizations can innovate and automate processes more effectively by leveraging remote servers and services.
Internet of Things (IoT): The Internet of Things (IoT) refers to the interconnection of everyday devices and objects to the internet, allowing them to send and receive data. This networked connectivity enables these devices to communicate with each other and with centralized systems, streamlining processes and enhancing operational efficiency. IoT is transforming industries by enabling real-time data analysis, automation, and improved decision-making, significantly driving business process automation.
Low-code/no-code platforms: Low-code/no-code platforms are software development environments that enable users to create applications with minimal or no programming expertise required. These platforms use visual interfaces and pre-built templates to streamline the development process, making it accessible to a broader audience, including business analysts and non-technical users. This democratization of app development fosters innovation and accelerates the delivery of business solutions.
Machine Learning (ML): Machine learning is a branch of artificial intelligence that focuses on the development of algorithms and statistical models that enable computers to perform tasks without explicit instructions. By analyzing and learning from data, machine learning helps automate processes and make predictions, thereby enhancing business operations and decision-making. Its integration into various technologies has transformed business process automation, evaluation readiness, and successful implementations of AI systems.
Natural Language Processing (NLP): Natural Language Processing (NLP) is a field of artificial intelligence that focuses on the interaction between computers and human language. It enables machines to understand, interpret, and respond to text or spoken input in a way that is both meaningful and useful. NLP combines computational linguistics with machine learning algorithms to facilitate tasks like sentiment analysis, language translation, and chatbot functionality, making it a vital component in automating business processes and enhancing decision-making.
Robotic Process Automation (RPA): Robotic Process Automation (RPA) is a technology that allows businesses to automate repetitive, rule-based tasks by using software robots or 'bots' to handle them. This helps organizations streamline processes, increase efficiency, and reduce human error. RPA connects to various aspects of business process automation by enhancing workflow systems, integrating with artificial intelligence and machine learning, offering lessons from successful implementations, and presenting both benefits and challenges in process automation.