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The global automation market size was expected to generate ±$214B by the end of 2023, of which ±$29B (14%) would have come from manufacturing and factory automation.

This is because numerous processes in manufacturing are repetitive, rule-based, and automatable by RPA bots. For instance, bill of materials (BOM), data migration and analytics, invoices, and inventory reporting are highly repetitive and time consuming tasks if done manually.

These are all processes that can be automated with RPA.

In this article, we are exploring the benefits of RPA and its top 8 use cases in manufacturing.

Business benefits of RPA in manufacturing

A typical rule-based process can be up to 70%-80% automated. RPA bots handle rule-based repetitive tasks and minimize the need for human interference.

In manufacturing, RPA bots can:

Decrease the labor time spent on routine tasks

Decrease the time-to-market (TTM)

Increase data quality and minimize process errors by minimizing human interference

Maintain an audit trail

Additionally, data collected by RPA bots can be used as input to manufacturing analytics tools and digital twin of an organization software to analyze the overall progress, identify gaps, and find improvement opportunities.

For more, see a comprehensive list of RPA benefits.

Analysis of RPA use cases in manufacturing

The following processes are highly automatable by RPA:

Supply chain management Invoice processing

RPA bots can automate invoice processing by:

Extracting specific data from invoices using OCR

Converting the extracted data into a structured format

Compare invoices against purchase orders

Cross-check for duplicates

Updating invoice records in the ERP system

Learn more about invoice processing automation.

Supply chain optimization

Global supply chains are complex since they involve:

Suppliers from variety of countries

Suppliers with different levels of technical sophistication

Smaller suppliers rely more on manual processes. Their customers, as a result, need to use paper or PDF-based documents to track their order shipments. RPA can automate repetitive aspects of such processes, helping humans focus on more complex tasks.

For instance, instead of the user manually copying and pasting a tracking number on the website to know the shipment’s latest location, RPA bots can be programmed to send him/her a push notification, or an email, any time there is a change in the shipping status.

Learn more about supply chain automation.

Production: Bill of materials (BOM)

A bill of materials (BOM), also called product structure, is one of the most vital documents in manufacturing.

A BOM contains the list of raw materials, sub-assemblies, intermediate assemblies, sub-components, parts, and the quantities of each component required to manufacture an end product. RPA bots can be combined with OCR and be programmed to extract specific product or element data, and replicate human steps required to generate a bill of materials.

Learn more about RPA BOM.

Stock optimization Inventory management

Inventory management is the process of auditing and tracking the flow of assets and stock from the factory up until the point of sale. RPA bots can automate:

monitoring inventory levels

placing orders

generating receipts

tracking deliveries

replying to quotations and queries emails

Inventory reporting

RPA bots track and replicate GUI steps, therefore they can automate inventory reports by:

collecting and updating inventory data

generating inventory reports

sending reports to designated employees or personnel

For instance, a German, physical robot manufacturing company had to allocate labor to exchange data between its spare-parts inventory software and the ERP application. After leveraging RPA, they were able to have a real-time insight into their inventory of spare parts, thus automating 95% of reoders from different vendors. Moreover, manual errors from manual exchange of data — such as under/overestimating of inventory levels and wrongful reorders — were eliminated to increase reordering efficiency1.

Learn more about inventory management.

Data management

Statistics suggests that 37% of engineers’ time in the manufacturing is spent on collecting and analyzing data manually. On the other hand, manufacturing data requires continuous update and analysis. RPA bots can automate:

Cleansing inventory and financial data

Migrating data from older systems to newer systems

Processing unstructured data (e.g. invoices) via OCR

Scheduling data imports and conversions (i.e. converting data from one format to another)

Updating structured data in ERP systems

Managing data warehouses

Order fullfilment

Order fulfillment includes all the processes after receiving an order from a client, up until delivery. Bots can automate the end-to-end process of order fulfillment by:

Identifying order emails and notifications

Downloading order requirement files

Extracting relevant data

Updating the order fields and creating an order in the ERP system

Replying to clients with information about product availability and shipping options.

Proof of delivery

Proof of delivery (POD) documents are a type of receipt that proves that products or goods have been delivered to the client. RPA bots can be integrated with logistics ERP modules to track deliveries. If delivery data is detected, the bot will extract the data, update delivery fields in the ERP module, and generate a proof of delivery receipt.

Recommendations to business leaders

Manufacturing can benefit from different emerging technologies to enhance performance, improve products, mitigate risks, and avoid human errors. Businesses should:

Use intelligent automation: Intelligent automation is the next generation of RPA tools with AI capabilities that can automate complex processes.

Leverage AI and Big data analytics: Manufacturing firms produce large amounts of operations and events data. Applying analytics to this data can uncover insights about production processes, analyze productivity, and identify recurring errors. In addition, analyzing manufacturing data will provide information about market trends and customer requirements in different periods, and enable predicting future customer needs.

For more on smart manufacturing, read:

AI in manufacturing

Manufacturing analytics use cases

IoT use cases in manufacturing

Embrace virtual and augmented reality: Manufacturing businesses can rely on virtual and augmented reality to design and create product prototypes, maintenance, repair, and training new employees on machines or software.

For example, some automobile manufacturers rely on virtual reality to ensure that vehicles are tested at early phases development, in order to optimize the design, tolerance and safety features.

However, there are some challenges that executives face in manufacturing digital transformation. A report in 2023 indicates that the following are the top barriers to the adoption of new tech:

Source: Future of jobs survey 2023

Nonetheless, statistics shows that ±93% of businesses agree that novel technologies are necessary to reach their digital transformation goals, and 70% of companies either have an on-going digital transformation strategy or are planning one.

For more on RPA

To explore RPA applications in different industries, to check out these articles:

If you still have any questions about RPA, feel free to download our in-depth whitepaper on the topic:

And if you think your business will benefit from an RPA solution, don’t hesitate to check out our data-driven list of RPA platforms and vendors.

And we can guide you through the process as well:

This article was drafted by former AIMultiple industry analyst Alamira Jouman Hajjar.


Physical robot manufacturing case study.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.





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Top 60+ It Automation Use Cases In 2023

In a research, IT teams reported spending 70% of their time on repetitive tasks and time-consuming activities.

Speed up production of information system

Reduce human intervention and operational costs

Increase customer satisfaction and operational efficiency. However, 88% of IT decision-makers and business leaders reported that they experience at least one challenge while scaling automation.

Therefore, this article explains IT automation use cases under three categories:



Business function-specific

General applications Configuration and deployment automation

Configuration and deployment automation refers to automating the configuring and deploying of software applications, systems, or infrastructure. It involves activities such as:

Configuration management: It involves setting up and configuring systems, applications, and services.

Provisioning and deployment: It refers to the deployment of infrastructure, servers, and applications.

Continuous integration/continuous deployment (CI/CD): It is the build, testing, and deployment process for software development.

Resource management includes the provisioning, management, and optimization of physical and virtual resources in these environments:

Data center resource management

Cloud server management

On-premise resource management

Patch management: It includes the installation of software patches and updates across systems and applications.

Monitoring and alerting: It requires monitoring system performance, generating alerts, and taking corrective actions.

Backup and disaster recovery: It refers to the backup, replication, and recovery of critical data and systems.

User provisioning and access management: It is the creation, modification, and deletion of user accounts and access permissions.

Security and compliance: It contains security controls, vulnerability scanning, compliance audits, and threat response.

 Incident response: Automating the detection, investigation, and remediation of security incidents and breaches.

Log management: Automating the collection, analysis, and archiving of system logs for troubleshooting and compliance.

Data management automation

181 zettabytes of data are expected to be generated by 2025, which requires effective data management strategies. Data management automation optimizes management strategies to improve efficiency, accuracy, and reliability in an organization by automating data-related tasks and activities, such as: 

Data integration and ETL (Extract, Transform, Load): With these activities, IT teams extract data from different systems and databases to transform and load data.

Data backup and restore: It refers to the backup and restoration of data across different storage systems.

Data cleansing and transformation: IT teams are expected to clean and transform data to improve data quality and consistency in analysis.

Network and Infrastructure Automation

Network and infrastructure automation allows organizations to manage and control networking and infrastructure components automatically. The way automation applies to these tasks and activities involve: 

Network automation: It includes automating repetitive tasks like network configuration, monitoring, and troubleshooting tasks.

Data center automation: Data centers are servers, storage, and networking that can be automatically managed and provided by IT automation tools. Check out some of these data center automation tools.

DNS and DHCP management: IT automation can help with the management and configuration of DNS (Domain Name System) and DHCP (Dynamic Host Configuration Protocol) services.

Mobile device management: IT teams can automate mobile device management and security including applications and data. Review our comprehensive list of MDM software.

Server lifecycle management: It is the automation of servers management throughout their lifecycle, from provisioning to decommissioning.

Process and Workflow Automation

According to McKinsey, 66% of businesses automated processes and workflows at least in one business function. 

IT automation integrates systems and applications, orchestrates the information and action flow, and replaces routine tasks, approvals, and workflows to improve efficiency and reduce manual effort. Some of the examples include:

Service request management: IT teams can handle and fulfill user service requests automatically.

Change management: Automated change management allows changing approvals to IT systems and infrastructure.

Incident management: IT automation can track, escalate and resolve IT incidents with minimal human intervention. It can also automate the escalation and notification processes for critical incidents. Compare top incident management tools.

Workflow orchestration: It automates the coordination and sequencing of complex workflows involving multiple systems and processes.

Self-service IT: End-users can manage IT resources without manual intervention.

Asset management: IT teams can automatically track and manage IT assets throughout their lifecycle.

Service catalog management: It refers to the creation, management, and delivery of IT service catalogs.

Compliance auditing: Automating the assessment and reporting of compliance with regulatory and industry standards.

Service Level Agreement (SLA) management: Automating the monitoring and reporting of SLA compliance for IT services.

Workload automation: WLA is a type of IT automation that can automate the scheduling and execution of batch jobs, complex tasks and processes.

Service desk automation: It is another type of use case that can automate common service desk tasks, such as ticket routing, categorization, and resolution.

Performance measurement automation

IT automation can help monitor, analyze, and optimize the performance of systems, apps, and infrastructures. Some examples are: 

Capacity planning: It automates resource usage analysis and forecasting to optimize capacity requirements.

Performance monitoring and optimization: IT teams can leverage automation tools to monitor and tune system performance to ensure optimal operation.

Other IT automation examples

Some IT automation techniques do not fall under specific categories. These tools may utilize software bots and machine learning technologies to automate an organization’s diverse business processes, functions, or activities.

Here are some examples of miscellaneous automation:

DevOps automation: It refers to automated process of collaboration and integration between development and operations teams for faster software delivery.

Configuration compliance: Automating the enforcement and validation of system configurations against established standards.

Quality assurance and testing automation

Quality assurance and testing automation refers to the use of automated tools and processes to enhance the efficiency and effectiveness of software testing and quality assurance activities. It involves automating tasks within the testing and quality assurance lifecycle, such as: 

Test data generation: IT automation can automate the generation of test data sets to ensure sufficient and representative test coverage.

Test environment setup: In the testing cycle, test environment setup takes 17% of the time. IT automation can reduce this time by assisting with setting up and configuring test environments, including deploying required software and infrastructure.

Test result analysis: It refers to the automated analysis of test results, including the identification of failures, defects, and performance issues. Also, it can automate the generation of test reports and documentation to provide comprehensive test coverage and traceability.

Test script maintenance: It is the automated maintenance and version control of test scripts to ensure accuracy and consistency, which counts for 12% of time if done manually.

Test execution scheduling: IT automation enables the scheduling and coordination of test executions across different platforms and environments.

Test coverage analysis: IT automation tools can analyze test coverage to identify gaps and ensure adequate testing of software functionality.

Test Automation: In a research, manual testing is identified as the most time-consuming activity in the testing cycle by 35%. IT automation can tackle this challenge by automatically executing software tests to validate functionality and performance.

Industry-specific use cases

IT automation software can be deployed in different industries to reduce human error, manual processes, and operational costs. There are numerous different automation-related terms to describe automation technologies and these could be referred to as health tech or HR tech in different settings. However, they will involve automating tasks of IT processes.

These industries include:


According to McKinsey’s report, 43% of healthcare tasks can be automated to reduce processing costs and human error.

Patient Data Management: IT automation allows healthcare organizations to update and synchronize patient records across different systems and departments.

Appointment Scheduling: Automated systems can handle appointment booking, reminders, and rescheduling, improving efficiency and reducing errors.


Finance automation can save ~70% of finance operations costs by lowering mistakes and human intervention.  IT automation in finance can streamline following activities:

44. Fraud Detection: IT automation can help detect suspicious activities and patterns in real-time, enabling financial institutions to prevent fraud and enhance security.

45. Compliance and Reporting: It refers to automating regulatory compliance processes, such as generating reports and ensuring data accuracy, can save time and reduce the risk of errors in financial services.

46. Risk Assessment: Finance teams can automatically analyze large volumes of financial data and identify potential risks, such as credit risks or market trends.


64% of manufacturing-related activities can leverage AI powered automation technologies, such as intelligent automation, robotic process automation, or IT automation tools.

47. Inventory management: Manufacturing firms can automate inventory tracking, ordering, and restocking processes to optimize stock levels, reduce costs, and prevent stock-outs.

48. Quality Control: Manufacturing companies play automated systems for real-time monitoring, data collection, and analysis to ensure product quality and identify defects early in the manufacturing process.

Retail and E-commerce

Retailers can lower IT inefficiencies and data inaccuracy by increasing automation use. The way an automated system can help retail and e-commerce platforms include:

49. Order Processing and Fulfillment: It automates order processing, inventory updates, and fulfillment workflows to accelerate order handling and reduce errors.

50. Customer Support: IT automation solutions can handle common customer inquiries, provide support, and improve response times.

Logistics / supply chain

IT automation types like workload automation and data warehouse automation can help logistics firms with:

51. Route Optimization: IT automation tools can automate the process of selecting optimal routes based on real-time data, such as traffic conditions, to improve efficiency and reduce delivery times.

52. Supply chain visibility: Logistics firms can use IT automation to track and monitor shipments, provide real-time customer updates, and enhance overall supply chain visibility.


Telecom companies can benefit from IT automation in several ways. Here are a few examples:

53. Network Management: Automation can assist in managing and monitoring telecom networks, improving efficiency, and reducing manual errors. It can involve provisioning, configuration management, and network troubleshooting. Automated network management systems can identify and resolve network issues in real-time, leading to faster problem resolution and improved network performance.

54. Service Provisioning: Automating service provisioning processes can streamline the delivery of telecom services to customers. This includes automated order management, new service activation, and provisioning resources like phone lines, broadband connections, or cloud services. By reducing manual intervention and streamlining processes, companies can accelerate service delivery, enhance customer satisfaction, and reduce errors.

55. Customer Support: Automation can enhance customer support services in the telecom industry by automatically handling basic customer inquiries, troubleshooting, and checking ticket status. Such automated ticketing systems can categorize and route customer issues to the appropriate support teams, ensuring faster response times and efficient issue resolution.

56. Billing and Revenue Assurance: IT automation can improve telecom companies’ billing accuracy and revenue assurance processes. Automated systems can gather customer data to generate accurate invoices and reconcile billing information with service provisioning and usage data. This helps reduce billing errors, identify revenue leakage, and improve financial processes.

57. Network Security: Automated systems can continuously monitor network traffic, detect anomalies, and trigger security alerts. Additionally, automated patch management and vulnerability scanning can help ensure that network infrastructure and systems are up-to-date and protected against potential security threats.

Business functions-specific

The major business functions that can leverage IT automation include:


HRIT automation can significantly streamline and enhance HR processes such as employee onboarding process, recruitment process, and performance management. According to Mckinsey, 56% of HR tasks can be automated to decrease processing costs and time.

58. Creating user Accounts: IT automation can create and provide new user account for a new employee, including:

59. Equipment and Software Setup: Automated workflows can assist and set up the necessary equipment for a higher employee experience, such as:

60. Onboarding workflows: IT automation can facilitate the creation and execution of standardized onboarding workflows, with onboarding tasks like digital forms completion (e.g., tax forms, emergency contacts), training module assignments, and task reminders for HR teams. Automated notifications can be sent to new employees to ensure a smooth employee onboarding process.

61. Self-Service Portals: Self-service portals or intranet platforms with automation capabilities can allow new employees to access essential information, resources, and training materials at their convenience. Automated workflows within this portal may guide employees through the process, provide relevant documentation, and facilitate communication with HR and IT departments.

62. IT Policy Compliance: Automation can help ensure that new employees are aware of and compliant with IT policies and security measures.

63. Recruitment process: By leveraging IT automation in recruitment processes, HR teams can automatically scan job boards, career websites, and social media platforms to source candidates, screen resumes based on predetermined rules to shortlist candidates, manage applicant information to track applicants and schedule interviews.


With IT automation, sales and marketing teams can:

Increase customer retention

Lead to higher an uplift by 10%

Improve customer experience by 43%

Handle customer onboarding process with minimal human intervention by automating.

66. Sales process and workflow automation: IT automation can streamline the sales process, other processes, and workflows, such as the procurement process or purchase orders process, by automating repetitive tasks such as sending follow-up emails, generating quotes, and managing contracts. Automated workflow can improve customer experience and reduce human errors.

67. Reporting and analytics: IT automation tools can collect and consolidate real-time and historical data from various sources, providing actionable insights into sales performance. As a result, a sales team can make data-driven decisions to enhance their customer retention rate.

Further reading

Explore more on IT automation types by checking out:

Assess different vendors for each IT automation type by checking out our comprehensive and data-driven lists:

If you have more questions, we can always help:

Hazal Şimşek

Hazal is an industry analyst in AIMultiple. She is experienced in market research, quantitative research and data analytics. She received her master’s degree in Social Sciences from the University of Carlos III of Madrid and her bachelor’s degree in International Relations from Bilkent University.





Top 30 Nlp Use Cases In 2023: Comprehensive Guide

Natural language processing (NLP) is a subfield of AI and linguistics which enables computers to understand, interpret and manipulate human language. 

Although machines face challenges in understanding human language, the global NLP market was estimated at ~$5B in 2023 and is expected to reach ~$43B by 2025. And this exponential growth can mostly be attributed to the vast use cases of NLP in every industry.

You may be familiar with many day-to-day NLP applications such as autocorrection, translation, or chatbots. However, NLP has numerous impactful applications that business leaders are not aware of. Therefore, we compiled a comprehensive list of NLP use cases and applications and categorized them according to relevant industries and business functions:

General applications

1. Translation

One of the top use cases of natural language processing is translation. The first NLP-based translation machine was presented in the 1950s by Georgetown and IBM, which was able to automatically translate 60 Russian sentences to English. Today, translation applications leverage NLP and machine learning to understand and produce an accurate translation of global languages in both text and voice formats.

2. Autocorrect

NLP is used to identify a misspelled word by cross-matching it to a set of relevant words in the language dictionary used as a training set. The misspelled word is then fed to a machine learning algorithm that calculates the word’s deviation from the correct one in the training set. It then adds, removes, or replaces letters from the word, and matches it to a word candidate which fits the overall meaning of a sentence.

3. Autocomplete

Autocomplete, or sentence completion, combines NLP with certain machine learning algorithms (e.g. Supervised learning, Recurrent neural networks (RNN), or Latent semantic analysis (LSA)) in order to predict the likelihood of using a following word or sentence to complete the meaning.

4. Conversational AI

Conversational AI is the technology that enables automatic conversation between computers and humans. It is the heart of chatbots and virtual assistants like Siri or Alexa. Conversational AI applications rely on NLP and intent recognition to understand user queries, dig in their training data, and generate a relevant response.

Chatbots have numerous applications in different industries as they facilitate conversations with customers and automate various rule-based tasks, such as answering FAQs or making hotel reservations.

For instance, Haptik produced a virtual assistant for Tata Mutual Fund to enhance customer retention and reduce call center workload. Initiative augmented the workforce of Tata by allowing employees to focus solely on urgent customer issues, by cutting call center enquiries by approximately 70%.

You can request a demo to see Haptik’s conversational AI solutions in action.

5. Automated speech/voice recognition

Voice recognition, also known as automatic speech recognition (ASR) and speech to text (STT), is a type of software that converts human speech from its analog form (acoustic sound waves) to a digital form that can be recognized by machines. ASR works by:

Splitting the audio of a speech recording into individual sounds (tokens),

Analyzing each sound,

Using algorithms (NLP, deep learning, Hidden Markov Model, N-grams) to find the most probable word fit in that language,

Converting the sounds into text.

Today, smartphones integrate speech recognition with their systems to conduct voice search (e.g. Siri) or provide more accessibility around texting. 

Source: Lekta

6. Automatic text summarization

Automatic text summarization is the process of shortening long texts or paragraphs and then generating a concise summary that passes the intended message. There are 2 main methods to summarize texts:

Cleaning the text from filling words

Sampling the text into shorter sentences (tokens)

Creating a similarity matrix that represents relations between different tokens

Calculating sentence ranks based on semantic similarity

Selecting sentences with top ranks in order to generate the summary (either extractive or abstractive)

7. Language models

Language models are AI models which rely on NLP and deep learning to generate human-like text and speech as an output. Language models are used for machine translation, part-of-speech (PoS) tagging, optical character recognition (OCR), handwriting recognition, etc.

Some of the famous language models are GPT transformers which were developed by OpenAI, and LaMDA by Google. These models were trained on large datasets crawled from the internet and web sources in order to automate tasks that require language understanding and technical sophistication. For instance, GPT-3 has been shown to produce lines of codes based on human instructions.

For more in-depth knowledge on sentiment analysis data collection, feel free to download our whitepaper:

Retail & e-commerce use cases

8. Customer service chatbot

A 2023 survey revealed that 65% of decision-makers in customer service believe that a chatbot can understand the customer’s context, and 52% said that chatbots can automate actions based on customer responses. Chatbots in customer service can:

For instance second hand car dealer Cars24, reduced its call center cost 75% by automating FAQs with a chatbot that deployed on WhatsApp and mobile app of the company.

To explore more use cases, feel free to read our in-depth article about chatbot use cases in customer service.

9. In-store bot

Several retail shops use NLP-based virtual assistants in their stores to guide customers in their shopping journey. A virtual assistant can be in the form of a mobile application which the customer uses to navigate the store or a touch screen in the store which can communicate with customers via voice or text. In-store bots act as shopping assistants, suggest products to customers, help customers locate the desired product, and provide information about upcoming sales or promotions.

10. Market intelligence

11. Semantic based search

Semantic search refers to a search method that aims to not only find keywords but understand the context of the search query and suggest fitting responses. Many online retail and e-commerce websites rely on NLP-powered semantic search engines to leverage long-tail search strings (e.g. women white pants size 38), understand the shopper’s intent, and improve the visibility of numerous products. Retailers claim that on average, e-commerce sites with a semantic search bar experience a mere 2% cart abandonment rate, compared to the 40% rate on sites with non-semantic search. 

Read our article on the Top 10 eCommerce Technologies with Applications & Examples to find out more about the eCommerce technologies that can help your business to compete with industry giants.

Healthcare use cases

12. Dictation

To document clinical procedures and results, physicians dictate the processes to a voice recorder or a medical stenographer to be transcribed later to texts and input to the EMR and EHR systems. NLP can be used to analyze the voice records and convert them to text, in order to be fed to EMRs and patients’ records.

13. Clinical documentation

14. Clinical trial matching

NLP can be used to interpret the description of clinical trials, and check unstructured doctors’ notes and pathology reports, in order to recognize individuals who would be eligible to participate in a given clinical trial. The algorithm used to develop such an NLP model would use medical records and research papers as training data in order to be able to recognize medical terminology and synonyms, interpret the general context of a trial, generate a list of criteria for trial eligibility, and evaluate participants’ applications accordingly.

A team at Columbia University developed an open-source tool called DQueST which can read trials on chúng tôi and then generates plain-English questions such as “What is your BMI?” to assess users’ eligibility. An initial evaluation revealed that after 50 questions, the tool could filter out 60–80% of trials that the user was not eligible for, with an accuracy of a little more than 60%.

15. Computational phenotyping

Phenotyping is the process of analyzing a patient’s physical or biochemical characteristics (phenotype) by relying on only genetic data from DNA sequencing or genotyping. Computational phenotyping uses structured data (EHR, diagnoses, medication prescriptions) and unstructured data (physicians vocal records which summarize patients’ medical history, immunizations, allergies, radiology images, and laboratory test results, as well as progress notes and discharge reports). Computational phenotyping enables patient diagnosis categorization, novel phenotype discovery, clinical trial screening, pharmacogenomics, drug-drug interaction (DDI), etc.

In this case, NLP is used for keyword search in rule-based systems which search for specific keywords (e.g. pneumonia in the right lower lobe) through the unstructured data, filter the noise, check for abbreviations or synonyms, and match the keyword to an underlying event defined previously by rules.

16. Computer assisted coding (CAC)

Computer Assisted Coding (CAC) tools are a type of software that screens medical documentations and produces medical codes for specific phrases and terminologies within the document. NLP-based CACs screen can analyze and interpret unstructured healthcare data to extract features (e.g. medical facts) that support the codes assigned.

17. Clinical diagnosis

NLP is used to build medical models which can recognize disease criteria based on standard clinical terminology and medical word usage. IBM Waston, a cognitive NLP solution, has been used in MD Anderson Cancer Center to analyze patients’ EHR documents and suggest treatment recommendations, and had 90% accuracy. However, Watson faced a challenge when deciphering physicians’ handwriting, and generated incorrect responses due to shorthand misinterpretations. According to project leaders, Watson could not reliably distinguish the acronym for Acute Lymphoblastic Leukemia “ALL” from physician’s shorthand for allergy “ALL”.

18. Virtual therapists

Virtual therapists (therapist chatbots) are an application of conversational AI in healthcare. NLP is used to train the algorithm on mental health diseases and evidence-based guidelines, in order to deliver cognitive behavioral therapy (CBT) for patients with depression, post-traumatic stress disorder (PTSD), and anxiety. In addition, virtual therapists can be used to converse with autistic patients to improve their social skills and job interview skills. For example, Woebot, which we listed among successful chatbots, provides CBT, mindfulness, and Dialectical Behavior Therapy (CBT).

Banking use cases

19. Stock prices prediction

NLP is used in combination with KNN classification algorithms to assess real-time web-based financial news, in order to facilitate ‘news-based trading’, where analysts seek to isolate financial news that affects stock prices and market activity. To extract real-time web data, analysts can rely on:

To learn how web scraping is used in finance, read In-Depth Guide to Web Scraping for Finance.


Bright Data’s Data Collector is a web scraping tool that targets websites, extracts financial data in real-time, and delivers it to end users in the designated format.

20. Credit scoring

Credit scoring is a statistical analysis performed by lenders, banks, and financial institutions to determine the creditworthiness of an individual or a business.

NLP can assist in credit scoring by extracting relevant data from unstructured documents such as loan documentations, income, investments, expenses, etc. and feed it to credit scoring software to determine the credit score.

In addition, modern credit scoring software utilize NLP to extract information from personal profiles (e.g. social media accounts, mobile applications) and utilize machine learning algorithms to weigh these features and assess creditworthiness.

Conversational banking can also help credit scoring where conversational AI tools analyze answers of customers to specific questions regarding their risk attitudes.


Insurance use cases

21. Insurance claims management

NLP can be used in combination with OCR to analyze insurance claims. For example, IBM Watson has been used to comb through structured and unstructured text data in order to detect the right information to process insurance claims, and feed it to an ML algorithm which labels the data according to the sections of the claim application form, and by the terminology that commonly is filled into it.

Finance department use cases

22. Financial reporting

NLP can be combined with machine learning algorithms to identify significant data in unstructured financial statements, invoices, or payment documentations, extract it, and feed it to an automation solution, such as an RPA bot utilized for reporting in order to generate financial reports.

23. Financial auditing

NLP enables the automation of financial auditing by:

Screening financial documents of an organization

Classifying financial statement content

And identifying document similarities and differences

In turn, this enables the detection of deviations and anomalies in financial statements.

24. Fraud detection

NLP can be combined with ML and predictive analytics to detect fraud and misinterpreted information from unstructured financial documents. For instance, a study revealed that NLP linguistic models were able to detect deceptive emails, which were identified by a “reduced frequency of first-person pronouns and exclusive words, and elevated frequency of negative emotion words and action verbs”. The researchers used an SVM classifier algorithm to analyze linguistic features of annual reports, including voice, active versus passive tone, and readability, detecting an association between these features and fraudulent financial statements.

HR use cases

25. Resume evaluation

NLP can be used in combination with classification machine learning algorithms to screen candidates’ resumes, extract relevant keywords (education, skills, previous roles), and classify candidates based on their profile match to a certain position in an organization. Additionally, NLP can be used to summarize resumes of candidates who match specific roles in order to help recruiters skim through resumes faster and focus on specific requirements of the job.

26. Recruiting chatbot

Recruiting chatbots, also known as hiring assistants, are used to automate the communication between recruiters and candidates. Recruiting chatbots use NLP for:

Screening candidate resumes,

Scheduling interviews,

Answer candidates’ questions about the position,

Build candidate profiles,

Facilitating candidate onboarding.

27. Interview assessment

Many large enterprises, especially during the COVID-19 pandemic, are using interviewing platforms to conduct interviews with candidates. These platforms enable candidates to record videos, answer questions about the job, and upload files such as certificates or reference letters. NLP is particularly useful for interview platforms to analyze candidate sentiment, screen uploaded documentations, check for references, detect specific keywords which can reflect positive or negative behavior during the interview, as well as transcribe the video and summarize it for archiving purposes.

28. Employee sentiment analysis

Feel free to read our article on HR technology trends to learn more about other technologies that shape the future of HR management.

Cybersecurity use cases

29. Spam detection

NLP models can be used for text classification in order to detect spam-related words, sentences, and sentiment in emails, text messages, and social media messaging applications. Spam detection NLP models typically follow these steps:

Data cleaning and preprocessing: removing filling and stop words.

Tokenization: sampling text into smaller sentences and paragraphs.

Part-of-speech (PoS) tagging: tagging a word in a sentence or paragraph to its corresponding part of a speech tag, based on its context and definition.

The processed data will be fed to a classification algorithm (e.g. decision tree, KNN, random forest) in order to classify the data into spam or ham (i.e. non-spam email).

Source: Machine learning for email spam filtering: review, approaches and open research problems by Dada et al.

30. Data exfiltration prevention

Data exfiltration is a security breach that involves unauthorized data copying or transfer from one device to another. To exfiltrate data, attackers use cybersecurity techniques such as domain name system (DNS) tunneling (i.e. DNS queries which reflect a demand for information sent from a user’s computer (DNS client) to a DNS server) and phishing emails which lead users to provide hackers with personal information. NLP can be used to detect DNS queries, malicious language, and text anomalies in order to detect malware and prevent data exfiltration.

For more on NLP

To explore what natural language processing is, and what are its products, feel free to read our in-depth articles:

If you believe your business will benefit from a conversational AI solution, scroll down our data-driven lists of:

Additionally, you may download our whitepaper to get the most latest information about conversational AI:

And we can guide you through the process:

Links to Haptik on this page are sponsored.

This article was drafted by former AIMultiple industry analyst Alamira Jouman Hajjar.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.





Top 5 Use Cases Of Intelligent Automation In Hr In 2023

Human resource (HR) management is an important part of any business and involves many manual and repetitive processes. The adoption of AI and automation technologies in HR can bring significant benefits in terms of cost reductions and revenue increases. For instance, according to McKinsey, 40% of companies have been able to decrease their HR costs by more than 20% through the use of AI in 2023.

Intelligent automation, also known as cognitive automation or hyperautomation, which combines automation solutions such as RPA with AI techniques such as machine learning, NLP, computer vision, and conversational AI, can help companies automate end-to-end HR processes.

We list 5 use cases for intelligent automation in the human resources:

1. Recruitment

Screening resumes to eliminate unqualified candidates and creating a candidate shortlist can be a very time-consuming task if done manually. NLP-powered intelligent bots can:

Gather and screen resumes,

Compare applicant data with relevant job requirements and previously accepted applicant data,

Eliminate unqualified applicants,

Send emails to candidates according to their results.

Since it doesn’t work with predefined keywords and rules, AI-powered automation can reduce the number of false negatives and false positives, making the hiring process more efficient.

It is important to note that AI systems can be biased. Therefore, companies need to test and evaluate their automated recruitment tools to avoid biased hiring decisions.

2. Onboarding

Onboarding is an important process that impacts employee experience and retention. However, only 12% of employees believe their organization does a good job with onboarding. Intelligent automation can help companies streamline the onboarding process by:

Creating email addresses and user accounts for the tools used,

Granting access to required files or applications,

Sending relevant onboarding documents to the new employee,

Introducing them to the tools they will be using.

These can help companies reduce the time spent on manual onboarding tasks, quickly familiarize new employees with the company, and provide a more personalized onboarding experience.

You can also check our article on onboarding automation.

3. Travel & expense management

Travel and expense management is a repetitive and error-prone process that poses compliance risks due to potential issues such as missing receipts and non-compliant expenses. OCR-enabled intelligent automation can:

Collect employee travel expense receipts,

Extract the required data from receipts,

Cross-check individual expenditures against company and external expenditure regulations,

Complete payments or request approval of items that do not comply with expense policies.

Feel free to check our article on travel and expense automation for more.

4. Payroll processing

Accurate payroll processing is critical for employee satisfaction, but the process involves error-prone tasks such as calculating commissions and overtime. Intelligent bots can reduce the time spent on processing payrolls and reduce errors by:

Collecting clock-in and clock-out data of employees from login platforms,

Recording overtime and adjusting the compensation accordingly,

Calculating commissions based on the company’s commission rules,

Generating reports on different payroll statistics for data-driven decision-making.

For more, feel free to check our article on payroll automation.

5. Offboarding

Similar to onboarding, offboarding an employee also involves tasks that can be automated with intelligent bots to improve efficiency and employee exit experience, such as:

Generation of exit documents,

Revoking access to company systems,

Conducting exit surveys,

Processing final payments and ensuring that departing employees no longer receive payment.

You can check our article on offboarding automation for more.

For more on HR automation

Feel free to check our other articles on:

Feel free to explore intelligent automation use cases for your business in our comprehensive article on intelligent automation examples.

You can check our data-driven list of intelligent automation solutions. If you need help in choosing a solution, we can help:

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.





Top 9 Real Estate Chatbot Use Cases & Best Practices In 2023

Real estate is the top industry benefiting from chatbots, followed by travel, education, and healthcare. Chatbots are available 24/7 for:

In this article we explore  the top 9 use cases of chatbots in real estate to show their full potential for the real estate companies.

Why are chatbots important for real estate?

A survey showed that the first step for a home buyer is to search for properties online, and on average, it takes 10 weeks to settle on a property. 9 out of 10 respondents younger than 62 years old said that the most important feature of online search was the property photos.

From a customer point of view, chatbots are helpful in searching for homes to buy or rent, because they provide:

24/7 service: Most people search for properties outside working hours which makes it harder to reach out to a live agent.

Instant responses: Businesses that implement chatbots in their customer service department save up to 30% of costs by speeding up response times.

Multiple language options: This is especially important for foreigners moving to a new country where they do not speak the language.

Interaction capabilities beyond just text: For example, chatbots can help customers navigate pictures or the gallery of a property, especially for properties with numerous pictures. After viewing pictures, they could immediately ask more questions to the bot or skip the property completely.

Allowing chatbots to handle these queries frees up the real estate agents to focus on finding properties and optimizing their marketing strategy. Chatbots can lead to savings of up to $23 billion from annual salaries.


Salesforce Service Cloud Contact Center is a comprehensive customer service solution that enables organizations to manage their customer support operations and deliver good-quality customer experiences. Intelligent chatbots in the Contact Center provides personalized recommendations to the customers, automates answering customer questions and hands customers to the relevant agent.

What are the top use cases of chatbots in real estate?

Here are the top 9 use cases of chatbots in real estate:

Typical chatbots will pop up once a user opens a website. Real estate chatbots can start a conversation with an online user about their purpose for visiting the agency’s website (e.g. are they looking to buy, sell, or rent?) and ask for their contact information. 

Chatbots can then reach out to potential customers, send them emails about properties relevant to their search queries, or text them about promotions and campaigns on rental homes.

2. Build customer profiles

Chatbots can ask users questions to understand users’ preferences in terms of:

Area, city, town


Requirements (e.g. rent, buy, lease)

Property type (e.g. apartment, house, condo)

Number of rooms, bathrooms, backyard options, open or closed garage options

Chatbots can collect these information from users to create a profile for each user and provide them with personalized property options and listings.

3. Answer questions about properties

Users searching for properties to buy or rent may have questions about the properties that are not directly available online, such as the average of monthly bills, the history of the building, and previous owners or tenants.

Chatbots can have access to the agency’s database which holds this information, and can provide prospective customers with the information according to the agency’s third-party privacy policy. Chatbots can also answer FAQs about the agency, working hours, available locations, etc.

4. Provide virtual property tours

Users can also ask the bot to show a specific room or feature in the house, or provide more information about the spaces and measurements.

5. Schedule property viewings

Users can schedule a walkthrough with a live agent via the chatbot. Real estate chatbots can be programmed to search within agents’ calendars and provide customers with available days and slots for them to choose. 

Once users pick a date, the chatbot will automate the scheduling, add the event to the agent’s calendar, and send emails or messages to the customer for confirmation.

6. Follow up after property viewings

Chatbots can directly contact customers via messages or emails after property viewings to ask them if they have decided on the purchase, or provide them with different suggestions if they are still looking. 

7. Check for mortgage options

Users can check with chatbots to see if they qualify for a mortgage, ask for tips to qualify, and apply for a mortgage via the chatbot . Real estate agencies can connect their chatbots with partner banks or lending institutions to directly notify them about their financing options.

A survey has shown that 16% of buyers look online for more information on how to get a mortgage and general home buyers tips, and 14% apply for a mortgage online.

8. Collect reviews

After completing a purchase, or signing a rental contract, a chatbot can reach out to a customer via personalized emails to ask them to fill a survey about their experience, or via texts to have a conversation and understand their overall satisfaction with the agency.

9. Analyze market trends


Haptik is a conversational AI company that builds chatbots and intelligent virtual assistants for numerous companies from a variety of sectors. Disney, HP, Tata, and Zurich Insurance are the examples of Haptik’s Fortune 500 clients.

As of today, chatbots that are deployed by Haptik performed over 4 billion conversations. And vast training data set and valuable intent recognition capability makes their chatbot suitable to numerous industries, including real estate.  

To learn more about what AI can do for the real estate industry, feel free to read our in-depth article Real Estate Digital Transformation: Techs & Applications.

What are the real estate chatbot best practices?

To make the best of your real estate chatbot, you might want to consider the following best practices:

For more on chatbots

We’ve also investigated chatbot use cases and applications in different industries, such as:

If you still have more questions about chatbot and conversational AI technology, feel dree to read our in-depth whitepaper on the topic:

You may also find our whitepaper concerning voice bots:

And if you are interested in investing in an off-the-shelf chatbot or voice bot solution, don’t hesitate to check out our data-driven lists of vendors for chatbots and voice bots.

And we can guide you through the process:

This article was drafted by former AIMultiple industry analyst Alamira Jouman Hajjar.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.





Edge Computing: Definition, Characteristics, And Use Cases

Traditional cloud computing networks are significantly brought together, with data being collected on the fringe edges and sent back to the essential servers for taking care.

This plan grew out of the way that most of the devices arranged near the edge came up short on computational power and limited capacity to separate and then again process the data they accumulated.

How much data is ceaselessly being made at the edge is turning out to be decisively speedier than the limit of associations to manage it.

As opposed to sending data to a cloud or a distant server homestead to achieve the work, endpoints should send data to an Edge Enrolling contraption that cycles or separates that data.

What is Edge Computing?

Edge Computing is closer to data source and limit, and figuring tasks should be possible in the edge enrolling center point, which diminishes the center data transmission process.

Carrying this handling ability to the edge of the association helps address the data trial by creating, generally, shut IoT structures.

A conclusive goal is to restrict cost and lethargy while controlling association bandwidth.

A huge benefit Edge Figuring offers that would be helpful is the reduction of data ready to be sent and taken care of in the cloud.

It underlines closeness to clients and outfits clients with better shrewd organizations, thus further creating data transmission execution, ensuring consistent taking care, and decreasing conceded time.

Benefits of Edge Computing

Edge registering has arisen as one of the best answers for network issues related to moving gigantic volumes of information created today. Here are the absolute most significant benefits of edge processing −

Reduces Latency − Inactivity alludes to the time expected to move information between two organizational focuses. Huge distances between these two focuses and network clogs can create setbacks. As edge figuring carries the focuses nearer to one another, idleness issues are nonexistent for all intents and purposes.

Saves Bandwidth − Transmission capacity alludes to the rate at which information is moved in an organization. As all organizations have a restricted transmission capacity, the volume of information that can be moved and the number of gadgets that can cycle this is restricted too. By sending the information servers to the places where information is created, edge registering permits numerous gadgets to work over much more modest and effective data transmission.

Execution Expenses − The expenses of executing an edge foundation in an association can be complicated and costly. It requires a reasonable degree and reason before the organization and extra gear and assets to work.

Inadequate Information − Edge figuring can handle incomplete data arrangements that should be characterized during execution. Because of this, organizations might wind up losing important information and data.

Security − Since edge registering is a circulated framework, guaranteeing sufficient security can be challenging. There are takes a chance engaged with handling information outside the edge of the organization. The expansion of new IoT gadgets can likewise build the chance for the aggressors to invade the gadget.

Edge Computing Use-Cases

Edge figuring draws information handling closer to business activities. It has numerous varieties, with numerous IT experts seeing it as a development of the conveyed ‘lights out’ server farm idea. Regardless of how savvy the end-point is; all Edge approaches share similar engineering.

Center information center(s) with satellite areas store and cycle information and cooperate with end-focuses.

Edge comprises organization doors, server farms, and everything IoT.

The motivation behind the Edge is to convey dispersed application administrations, give knowledge to the end-point, speed up execution from the center data frameworks or gather and forward data from the Edge end-point sensors and regulators.

The shortfall of a concurred and acknowledged Edge processing definition requested we make our own subsequent in three distinct kinds of purpose cases −

Remote ‘Lights Out’ Edge Server, farms can be a little hardware rack in different far-off areas or numerous enormous server farms. It is the most different, non-standard Edge climate. It requires new hierarchical models, modern programming application designs, and a high degree of reflection to the picture, conveying low touch control and the capacity to scale and deal with a heterogenous blend of gear.

Holder IT Edges, is where combined frameworks reside. This climate comprises an answering stack including at least one of the accompanying; servers, operating system, stockpiling, organization, and improved power and cooling to help all the hardware in the contained climate. The compartments are exceptionally normalized notwithstanding, customization is accessible to suit explicit Edge prerequisites with choices for extra parts.

Internet of Things (IoT), where profoundly accessible processors empower constant investigation for applications that can hardly hold on to decide. IoT end-directs go on toward getting more brilliant with a more remarkable capacity to work freely and settle on choices without routine correspondence with center stage.


With edge computing, things have become fundamentally more successful. Accordingly, the idea of business assignments has become higher. Edge figuring is a sensible solution for data-driven undertakings that require lightning-fast results and a raised level of flexibility, dependent upon the current status of things.

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