Traditional automation approaches can be effective in simple tasks but they often rely on rigid, pre-defined rules and are limited in their ability to adapt to changing circumstances. This can lead to inefficiencies and errors, especially when dealing with complex tasks or data.
Intelligent automation combines the speed and efficiency of traditional robotic process automation (RPA) with the adaptability and decision-making capabilities of artificial intelligence (AI). By using AI algorithms to analyze data and make decisions, intelligent automation systems can learn and adapt to changing circumstances, resulting in more accurate and efficient processes.
In this article, we’ll explore 25 use cases, examples, and applications of intelligent automation in different business functions and industries.
Use cases in common business activities and functions
1. Data extraction
By leveraging AI techniques such as machine learning and NLP, intelligent bots can go beyond traditional OCR technology and extract unstructured data from PDFs, images, or handwritten documents. Intelligent bots leverage AI to understand the context of the document, reduce the noise in documents, and improve their accuracy as they extract data.
Feel free to check our article on intelligent document processing for a more detailed account.
2. Data entry and update
Bots can integrate company systems and can be programmed to update databases in different business functions such as HR, customer services, or sales. Bots can extract data from customer service interactions, emails, reports, enterprise applications, etc. to update information such as:
- Employee personal information, payments, PTOs,
- CRM data such as customer information, purchases,
- Logistics data, including shipments, inventory, and delivery.
Customer services
3. Interacting with customers
Intelligent bots with conversational AI capabilities can interact with customers make recommendations, and provide self-service tools.
4. Customer onboarding
AI-powered bots can streamline the customer onboarding process by:
- Guiding customers throughout the process,
- Collecting documents from customers,
- Extracting data from documents and entering it into company systems,
- Verifying provided information.
HR
5. Recruitment
NLP-powered bots can increase the efficiency of the recruitment process by:
- Scraping data from recruitment websites to identify potential candidates,
- Gathering candidate resumes,
- Screening resumes by comparing candidate information with job requirements,
- Sending emails to candidates according to their results.
6. Employee onboarding
Employee onboarding can be repetitive and time-consuming, but it is a critical process for employee experience. Intelligent bots can coordinate onboarding tasks across different departments, provide a more personalized onboarding experience, and streamline the onboarding process by:
- Creating user accounts for the tools used within the company and delivering the credentials to new hires,
- Granting access to files and applications according to new employees’ roles,
- Sending relevant onboarding documents to employees and answering FAQs.
7. Payroll processing
AI-powered bots can automate repetitive and error-prone payroll processing tasks such as recording overtime, keeping track of clock-in and clock-out information, and calculating commissions.
For more examples, feel free to check our article on the use cases of intelligent automation in HR.
Sales & marketing
8. Price optimization
Intelligent bots can help you set optimal prices for your products and services by analyzing:
- Supply and demand changes,
- Customer data such as demographics or spending habits,
- Historical data and market trends,
- Your competition’s prices.
9. Bid adjustment
Marketing teams adjust bids on digital advertising platforms to show ads more or less frequently depending on factors such as time or audience’s location, age, or device. AI-powered bots can significantly improve the bid adjustment process by analyzing numerous other factors affecting sales and automatically adjusting bids.
10. Lead nurturing
The lead nurturing process includes tasks such as lead identification, lead scoring, and sending customized proposals to qualified leads. Intelligent bots can collect additional information about identified leads from public sources, assess them and assign them a score according to an algorithm, and automatically send customized proposals to qualified leads.
11. Order-to-cash (O2C)
Intelligent bots can help businesses automate O2C processes by:
- Capturing order and customer data and issuing invoices,
- Sending automatic emails that contain payment links,
- Recording sales on the company’s books.
Finance & accounting
12. Accounts receivable (AR) / Accounts payable (AP) automation
Intelligent automation can help businesses reduce errors during Accounts Receivable (AR) and Accounts Payable (AP) processes and prevent miscalculations and delayed payments.
For accounts receivable processes, AI-powered bots can keep track of vendor data and transactions, extract data from invoices, and cross-check invoices against purchase orders. By using fraud detection algorithms, they can also check for fraudulent documents.
For accounts payable processes, bots can auto-generate invoices, keep track of days-sales-outstanding (DSO), process payments, and reconcile balance sheets after the payments.
13. Financial reporting
AI-powered bots can automate the financial report generation process by extracting data from business systems and relevant documents, performing necessary data manipulations, assembling reports in designated formats, and sending them to relevant stakeholders based on the content.
Industry-specific use cases
Healthcare
14. Appointment scheduling
Bots with conversational AI capabilities can enable self-service patient appointment scheduling by interacting with patients about their health problems, providing available time slots for different physicians, and letting patients set, reschedule, and cancel appointments. Bots can also reduce no-shows by sending reminders to patients.
15. Health insurance processing
Intelligent bots can undertake time-consuming and error-prone health insurance processing tasks such as preauthorization and claims processing, reducing healthcare insurance fraud and improving customer satisfaction with faster claims processing.
Financial services & banking
16. Know your customer (KYC)
Financial institutions can reduce the manual work involved in KYC processes using intelligent automation. Intelligent bots can extract data from customer documents, validate the information, identify risk areas, and send the cases to relevant staff in which human decision-making is necessary. Using intelligent automation, banks can speed up KYC processing times, reduce error rates, and improve regulatory compliance.
17. Loan processing
Intelligent bots can extract data from customers’ documents, prepare due diligence by combining this information with other documents, and assign a credit score for each loan application.
18. Anti-money laundering (AML)
AI-powered bots can conduct name screening by comparing customers’ information with watchlists, monitor transactions to detect suspicious activities, and reduce the manual work during customer offboarding.
Insurance
19. Underwriting
Intelligent automation can help insurers better price the risk and improve underwriting processes by:
- Extracting data from application documents,
- Consolidating relevant data from external and internal sources,
- Use ML models to offer a premium.
Claims processing
Claims processing is a labor-intensive process, and efficient claims processing is important both in terms of profitability and customer retention: 87% of customers say effective claims processing influences their decision to choose an insurer. AI-powered automation can accelerate the document processing with NLP-backed document extraction and improve other core claims processes such as:
20. First notice of loss (FNOL): Intelligent bots can guide policyholders after a loss to take pictures or videos, submit relevant information, etc., and enter this information into company systems.
21. Fraud detection: Bots can leverage AI algorithms to detect fraudulent claims.
Retail
22. Inventory management
Retailers can prevent stockouts and waste by leveraging intelligent automation. Intelligent bots can use AI/ML models and historical sales data to estimate the optimum inventory levels for different products, in different places and times.
23. Returns processing
Returns management is important for retailers as it affects customer retention: 96% of customers whose returns experience went well stated that they would shop from the retailer again. Intelligent bots can reduce manual work by collecting necessary information from customers, guiding them during their return process, sending status notifications, and updating inventory databases.
Manufacturing
24. Predictive maintenance
Intelligent bots can be integrated with sensors and IoT devices connected to machinery. As a result, manufacturers can keep track of the health of their equipment in real-time, predict machine failures, set and update maintenance schedules, and alert staff when maintenance is required which is part of the predictive maintenance process.
25. Bill of materials (BOM) creation
Bots with intelligent document processing capabilities can standardize bill of materials creation, generate BOM by extracting data from documents, and alert staff in case of missing information. This can help manufacturers reduce human errors resulting in delayed production and product recalls.
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