<img height="1" width="1" style="display:none;" alt="" src="https://px.ads.linkedin.com/collect/?pid=3040194&amp;fmt=gif">

Top 40+ Use Cases for Generative AI-Powered Automation

Few technologies have made an entrance the same way that generative AI has exploded onto the world stage—and into the global economy. In its first year out of the gate, generative AI has broken growth/adoption records and continues to astound with the size and scope of its current and increasing impact across the spectrum of work, life, and industry. One assessment by McKinsey, considering just 16 use cases across industries, estimated $4.4 trillion in economic impact annually.

2024-06-12 16:07:04


GBI, Industry News

What is generative AI?

Generative AI is a type of artificial intelligence (AI) technology trained on foundational models, typically large language models (LLMs), with the ability to understand natural language inputs—and critically, the intent of the input—and respond with useful content where there is no single correct answer. Generative AI has the ability to analyze large and unstructured data sets and create original content output in a range of modalities, primarily text, but increasingly in other formats such as images, computer code, and sound.

To analogize, think of it as a fast-learning, creative, and smart (but not always trustworthy) project partner—for any work project. That’s how it is widely used as a stand-alone tool today—to create reports, email campaigns, translations, etc. By generating original, on-point content, generative AI provides a major boost to productivity.

How do these capabilities benefit business?

The projected value of generative AI across industries and functions is massive, but some industries stand to benefit more, and sooner, than others. That makes understanding the applications of generative AI and specific use cases for your industry even more critical. But it can be challenging to grasp what generative AI’s capabilities mean on the ground. Beyond a content-creation assistant (with significant security, privacy, and accuracy red flags), business leaders from across industries and functions are asking—what are applicable use cases for generative AI right now?

To close the gap between vast-and-vague potential and real-world business applications, this article presents generative AI use cases by work function and industry. The aim is to provide a bridge to realizing the potential of this technology, whether you implement a use case directly or take it as a starting point for brainstorming/developing applications for your own unique business needs. This compendium of generative AI use cases is meant to jumpstart your journey to realizing the potential of generative AI.

What to know before getting started with generative AI

An essential part of getting ready to apply generative AI is understanding the potential risks and concerns that come with the technology, as well as how to mitigate them.

Data security

Protecting sensitive data, whether PII or proprietary business information, from unauthorized access or misuse, is of paramount concern when introducing a new technology. For generative AI, security questions must address the inherent layers of the technology (foundational model, provider of any 3rd party add-ons/plug-ins).

Start by asking key questions such as, who can access training data that may include classified information? And how does a provider secure data from breaches? The necessity is to ensure strong encryption, choose secure AI models, and establish stringent access controls for the use of generative AI.

Privacy and regulatory concerns

Generative AI models are built on enormous data volumes and the use of personal data and intellectual property raises significant privacy and regulatory concerns. It's imperative to incorporate customer approval for data use, ensuring alignment with corporate and industry-wide compliance standards such as GDPR, PII, and HIPAA.

As more regions establish and modify laws on intellectual property rights, data protection, and security, it's essential for enterprises to establish sound generative AI policies and governance.

Accuracy and bias

Reality checks are required. Generative AI has a history of hallucinations, which means making things up without any indication that the provided content contains false or fabricated information. Additionally, output can contain inherent biases, which may have policy or even legal ramifications for businesses. Human expertise and oversight are needed to ensure the accuracy of data and system outputs.

Good news! Security and privacy controls are available

With all of the potential generative AI holds, and all of the risks, putting generative AI in the context of Intelligent Automation is a critical step to realizing its potential value. The opportunity to accelerate enterprise productivity gains and realize the next leg of the hyperautomation and end-to-end process automation journey starts with the right automation platform to bring this new technology into your business workflows safely.

The primary avenue for businesses to achieve security, privacy, and accuracy controls is to harness generative AI through a secure Intelligent Automation platform that offers end-to-end orchestration across systems and users with built-in governance and guardrails to facilitate the safe and effective use of generative AI.

The key is orchestrating generative AI as part of multi-step, multi-system process automations through an enterprise orchestration platform, which has the comprehensive tools, guardrails, governance, analytics, integrations, and more, that are necessary to deploy AI-infused automations into enterprise operations.

How this list of generative AI use cases works

This collection of use cases is intended to give shape to the real-world value of generative AI available to every organization right now and to illustrate deployments that yield substantial value with the goal of guiding organizations toward fully exploiting the combined power of automation and AI.

Designed as a quick-find catalog of generative AI applications by business function and by industry, this list is meant to help spark ideas and inspire you to start today. As generative AI steadily advances and organizations begin to capitalize on its capabilities, valuable, novel, and impactful use cases will surface even faster than they already are. Check back for ongoing updates!

Generative AI use cases by business function

Customer service

Complaint resolution

The customer service workload across industries has increased dramatically over the past few years, with call volumes rising by as much as 600%. Generative AI combined with Intelligent Automation can support faster and higher-quality customer service complaint resolution at scale by assisting agents with real-time information retrieval and the ability to resolve cases quickly within a single primary application (e.g., Salesforce).

In this case, a new customer request triggers automated customer data retrieval from disparate systems. Generative AI delivers the case files to a customer service agent for review. Generative AI creates a personalized email response in seconds, which can be sent using an automation assistant from the same work application. The result is increased efficiency and response quality.

Triage and response to email requests for order information

One of the biggest challenges of triaging customer inquiries is the sheer volume of customer interactions. At the enterprise level, customer service can receive hundreds of thousands of inquiries every day. Inquiries in industries such as healthcare, banking, and insurance are often complex in nature, with high financial and personal stakes. It can be challenging for agents to navigate multiple systems quickly, triage the inquiry to the right team, and respond with the most up-to-date expertise in a timely manner.

In this case, generative AI and Intelligent Automation can support faster customer service inquiry triage and response. A new customer inquiry is processed by Document Automation to extract customer information. Generative AI analyzes the data and predicts the likelihood of resolving the issue with recommendations for actions based on the assessment. Automation can pick up on the chosen recommendation to route for processing and update the ERP system. Generative AI can create follow-up communications, such as an email to the customer asking for more information.

Customer inquiry sentiment analysis

The time required to research and respond to a complex customer inquiry is often high, largely impeding agents’ ability to resolve the issue quickly. Some cases, such as airline customer complaints and denied healthcare claims, can be complex, requiring more time and resources to investigate and resolve. Escalation workflows are often delayed due to inadequate info.

In this case, generative AI can review and understand the context and intent of an incoming customer service inquiry. Automation can execute looking up corresponding relevant resolutions in a knowledge base, and then generative AI can draft responses to match the resolution and specific contextual details of the inquiry. For simple inquiries, the response can be automatically sent without agent involvement. For complex issues, the agent can review the draft response and proceed with the workflow from there.

Customer response quality

The quality of customer service is a major factor in customer satisfaction. In the enterprise context of high-volume customer inquiries and high-speed turnaround targets, response quality is a difficult factor to keep tabs on.

In this case, generative AI-powered automation can review customer service inquiry response content for quality before agents reply. The automation could return a response quality score or assessment. Responses below a certain threshold could trigger generative AI to write suggested improved content for the agent to use.

Finance and accounting

Detect anomalies and discrepancies in financial reporting and documentation

Financial audits are a critical aspect of business operations, ensuring that financial data is accurate and compliant with regulatory requirements. However, manual financial audits are time-consuming, prone to errors, and may not be able to identify complex patterns or anomalies. This can result in inaccurate financial reporting, compliance issues, and increased risks of fraud and errors.

In this case, generative AI can swiftly process vast volumes of financial data, leveraging machine learning algorithms to identify irregularities that may be easily overlooked in manual reviews. Generative AI can scrutinize each financial or accounting transaction, pinpointing patterns indicative of fraud or errors. Its ability to learn and adapt over time means results will consistently improve as its detection capabilities improve.

Supplier discount negotiation

Companies set budgets and requirements, such as discounts and payment terms. But, comparing a supplier's demands with trends, commodity values, and competitors' costs is time-consuming, tedious, and takes up a lot of resource time. The result is both delays and missing out on savings and discounts.

In this case, generative AI and Intelligent Automation can work to review unstructured agreements and trend data across finance operations, compare terms, and recommend actions, as well as negotiate next steps, reducing the time to reach final terms to a matter of days.

Accurate and timely financial data

Financial data analysis and forecasting are critical aspects of business planning, enabling organizations to make informed decisions based on accurate assessments and projections about the financial health of the organization. However, manual data analysis and forecasting are prone to errors and may take too much time to surface complex patterns or anomalies, exposing the organization to increased risk.

For this use case, the combination of generative AI and Intelligent Automation can help reduce errors, enhance risk management, and accelerate the financial reporting process. Automation can gather updated financial and accounting data along with historical and contextual documents. Generative AI can review, analyze, and synthesize the data to generate reporting and surface patterns and outliers, expediting the process for analysts to empower organizations to make more informed decisions based on accurate and timely financial data, resulting in better business outcomes.

Invoice processing

Accounts payable (AP) is a labor-intensive function that often involves manually extracting vendor invoice data into the AP system, such as SAP, taking as long as 90 days to complete the invoice process. Manual invoice data entry is a prime source of errors, which can lead to payment delays and other issues. Regulations for data security and fraud prevention also heighten the scrutiny around invoice processing, adding to the time and resources spent on the invoicing process itself as well as monitoring and auditing the process.

For invoice processing, generative AI can help enhance efficiency and save time for AP teams by making end-to-end invoice processing automation possible. Generative AI can quickly flag discrepancies between invoices and ERP forms. Generative AI is capable of understanding any invoice format, including unstructured and complex, to identify and capture the required details such as supplier names, invoice numbers, and amounts. Once an invoice is processed, generative AI can create personalized emails to send to the supplier.

Risk-based audit reviews

With the rise of embedded finance and digitalization, auditors face an ever-growing challenge: the sheer volume of data. Every year, financial transactions multiply, making manual review and risk identification increasingly difficult and time-consuming. The adoption of generative AI in auditing saves time, reduces costs, and mitigates risk.

For audit reviews, generative AI, with its ability to process vast amounts of data and generate meaningful insights, can swiftly identify patterns and trends that might elude human auditors. Generative AI can conduct audit tests through advanced pattern recognition and predictive modeling, identifying fraudulent activities, errors, and inconsistencies. It can determine audit frequency based on the severity of risks, prioritize high-risk processes, and effectively allocate resources. And it generates clear, concise summaries of audit results, making it easier for finance and accounting stakeholders to understand the outcomes and make informed decisions.

Reporting: MD&A

Preparing the initial draft of management discussion and analysis (MD&A) is a time-consuming process, often taking weeks or even months to compile, analyze, and present financial data in a comprehensive and understandable format. Along the way, the potential for human error can introduce risk into this critical process. Applying Intelligent Automation and generative AI to the preparation of MD&A reports, as well as other internal and external financial statements, can significantly reduce the time required while increasing the accuracy and consistency of data.

For creating financial statements, generative AI and Intelligent Automation can rapidly sift through vast amounts of financial data to identify key trends, anomalies, and highlights. With these insights, generative AI can create an initial report draft using clear and concise language. Once reviewed and approved, generative AI can assist in translating the report to additional languages and draft communications to accompany the delivery of the report.

Supplier auto-response

Interacting with suppliers involves numerous email exchanges to discuss details like product specifications, delivery schedules, and payment terms. Manually responding to each case can be time-consuming and prone to errors or delays, which can negatively impact the supplier relationship.

With Intelligent Automation and generative AI, organizations can automate supplier interactions. Generative AI can scan incoming emails, understand the context, and generate appropriate responses. Combined with Intelligent Automation, it can directly respond to common queries like order status, payment confirmations, and delivery timelines, freeing up staff to handle more complex issues. Generative AI can also personalize the responses based on the supplier's profile and past interactions, enhancing the supplier relationship. This process can reduce response time from days to minutes.

Supplier risk and research

Evaluating potential suppliers involves an extensive review of their financial stability, operational capacity, and reputation. Manual research and analysis can take weeks and still miss critical information.

For supplier risk assessments, Intelligent Automation and generative AI can streamline this process by automatically gathering data from various sources like financial reports, news articles, and social media posts. It can then analyze this data to assess the supplier's risk level and highlight any red flags like financial instability, legal issues, or negative reviews. This information can help companies make informed decisions quickly and confidently.

Procurement inquiries

Handling procurement inquiries is a crucial but time-consuming task. Businesses receive numerous questions about product availability, pricing, delivery schedules, and more. Responding to each inquiry promptly and accurately is foundational to establishing customer relationships and maintaining customer satisfaction.

In this case, generative AI and Intelligent Automation can automate the handling of procurement inquiries. Generative AI can understand the nature of the inquiry, triggering automation to retrieve necessary information from company systems and then generate a detailed response. It can flag complex inquiries for human intervention. This approach can significantly reduce the response time and improve the accuracy of the responses.

Contract review

Reviewing contracts is a critical task that requires meticulous attention to detail. Companies need to ensure terms and conditions are favorable, cover all business requirements, and comply with all relevant regulations. However, manual contract review can take days or even weeks and is prone to errors.

For contract reviews, generative AI can scan the contract text, identify key clauses, and compare them with the company's standard terms. It can then flag any unfavorable terms or potential compliance issues for further review by the contract operations team. This process can significantly speed up contract reviews, reduce errors, and ensure all contracts align with business requirements.

Overdue payments

Businesses often deal with customers who delay payments beyond the due date. This can disrupt cash flow and strain business relationships. Manually tracking and following up on overdue payments can be time-consuming, potentially adding further delay to recovering payment and increasing the finance team's workload.

In this case, generative AI and Intelligent Automation can expedite the process by tracking invoice due dates, identifying overdue invoices, calculating late fees, and generating personalized reminder emails. The automated collections process can also escalate persistent cases for further action.

Invoice reconciliation

Invoice reconciliation involves matching invoices with purchase orders and delivery notes to verify the accuracy of the transactions. Any discrepancies can result in financial losses or compliance issues, but the process of manual reconciliation can be slow and error-prone.

With generative AI combined with Intelligent Automation, it is possible to automate the invoice reconciliation process. Generative AI can scan invoice documents in any format, extract relevant details, and match them against each other, flagging any discrepancies for further investigation and enabling error-free reconciled invoices to continue through the automated process. Automating this process can significantly speed up reconciliation, reduce errors, and ensure accurate financial reporting.

SOX controls

The Sarbanes-Oxley (SOX) Act requires companies to establish internal controls and procedures for financial reporting to reduce the risk of fraud. Implementing and monitoring these controls can be complex and, therefore, time- and resource-consuming. Applying Intelligent Automation with generative AI can help ensure continuous compliance, reduce the risk of penalties, and save valuable audit time.

In this case, generative AI can assist the process by automatically checking company transactions against SOX controls. It can identify any violations and generate detailed reports for the audit team, working with automation to notify and deliver reports to stakeholders.

Fraud detection

Fraudulent activities can result in significant financial losses and reputation damage. Detecting fraud requires constant monitoring of transactions and patterns that may indicate suspicious activity. Monitoring transactions for signs of potential fraud has already benefited from the advent of AI tools designed to excel at catching sophisticated fraud schemes that may elude manual oversight.

In this case, generative AI and Intelligent Automation can further enhance fraud detection by analyzing large volumes of data to identify unusual patterns or anomalies that may indicate fraud. Flagging suspicious transactions or use patterns can trigger an automation to alert security teams in real time, allowing for quick action. This process can significantly improve detection rates and reduce the impact of fraud by enabling faster containment.

Accounting: accruals

Accurate accrual tracking and estimation of future payments and receipts involves identifying transactions that require accruals, estimating amounts, and matching revenues and expenses to their correct periods. The process is characterized by nuances and complexities, especially on the expense side, which make manual accrual accounting time-consuming and prone to errors.

In this case, generative AI with Intelligent Automation can automate the accrual process by identifying transactions that require accruals, estimating amounts, and recording them in the accounting system. Generative AI can also generate reports detailing all accruals for review by the finance team, which, on approval, can be automatically sent out with the help of Intelligent Automation. This approach can ensure accurate financial reporting, reduce the workload of the finance team, and minimize the risk of errors.

Budgeting and forecasting

Budgeting and forecasting are at the core of business decisions and involve complex predictions about future revenues and expenses. Reviewing past performance, identifying market trends, and evaluating business plans requires nuanced analysis of wide-ranging data, making it challenging to consider all relevant factors.

In this case, Intelligent Automation and generative AI can assist by automatically analyzing relevant data and generating detailed budgets and forecasts. It can also highlight potential risks or opportunities based on the predictions. This approach can improve budgeting and forecasting accuracy, enable proactive decision-making, and save valuable planning time.

Profitability improvement opportunity identification

Identifying opportunities to improve profitability requires a wide-ranging evaluation of business operations, including contracts, to find areas where terms can be improved, costs can be reduced, or revenues increased. Valuable but slow to execute, this work requires a deep understanding of the business and the ability to analyze large amounts of unstructured data.

In this case, generative AI and Intelligent Automation, in particular Process Discovery, can help streamline the process by analyzing business processes and unstructured information across operations, contracts, and financial data to highlight areas with high costs, low revenues, or inefficiencies.

Revenue growth opportunity identification

Identifying opportunities for revenue growth involves strategic analysis and creative thinking to zero in on new markets, products, or strategies that can increase sales. It’s complex work that requires a deep understanding of the industry, relevant markets and market segments, customer preferences, and the competitive landscape. Time-consuming manual information gathering and analysis may not capture all potential opportunities or take too long, leading to missed revenue opportunities.

In this case, generative AI can expedite the process by automatically analyzing unstructured information, including market data, customer behavior, and competitor activities, to identify potential avenues for revenue growth. Based on the analysis, it can suggest new markets to enter, products to develop, or strategies to adopt. This approach can accelerate revenue growth, enhance competitiveness, and support strategic planning.

Liquidity and capital optimization

Managing liquidity and optimizing capital is a multifaceted task, demanding an intricate understanding of business operations, financial trends, and economic indicators. Traditional methods can be labor-intensive and might not capture all the nuances that influence cash flow.

In this case, generative AI can analyze vast amounts of financial data, learn patterns, and predict future cash flows based on historical data and market trends. Intelligent Automation can then use these predictions to optimize capital allocation, ensuring that funds are available where and when they're needed. The result is better liquidity management, which, in turn, reduces the risk of cash shortfalls and maximizes capital efficiency.

Investment management

In the complex arena of investment management, the diversity and volume of both structured and unstructured data, from charts and financial statements to industry reports, are overwhelming, presenting a challenge to existing analysis methods, which often fall short of capturing the full spectrum of market dynamics and individual investor preferences.

For portfolio management, generative AI can analyze intricate data sets and categorize investments based on geography, industry, sector, and ESG parameters. Using insights from investment research, generative AI can also provide personalized recommendations for portfolio holdings across financial instruments, such as ETFs, stocks, cryptocurrencies, bonds, and mutual funds.

The value of generative AI for investment management extends to risk management, where it can support in-depth risk analysis covering liquidity, credit, and market risks and provide respective confidence levels, along with tail-risk analyses, by generating data to stress-test the portfolio under hypothetical market condition scenarios. Last but not least, generative AI can create reports to communicate these analyses effectively.

Tax optimization

Tax liability prediction and planning take time and resources that continue to be in short supply across tax and finance teams, which spend nearly three-quarters of their time on routine work, such as data preparation, tax return compliance, and reconciliation. Accurate tax planning requires understanding tax laws, predicting tax liabilities, and planning strategies to minimize tax obligations.

In this case, generative AI can analyze tax laws, historical tax data, and business financials to predict future tax liabilities accurately and ensure compliance with tax laws. The benefits can often be seen immediately, with one company realizing $120 million in tax savings within three weeks.

Collections support

Recovering debts is a critical aspect of maintaining healthy cash flow. With evolving global regulations, compliance is a constant challenge for collections teams, impacting performance and complicating new agent onboarding.

In this case, the combination of generative AI and Intelligent Automation can drive efficiency, quality, and informed decision-making. With automated real-time call monitoring, generative AI can assist new agents, keeping them compliant and trigger alerts when collection calls don’t follow compliance best practices. To increase debt recovery through more tailored collection strategies, generative AI can evaluate historical collections data, payment behaviors, and market factors to recommend recovery approaches and predict success. Using call data, generative AI can deliver compliance analyses and identify trends such as increased use of specific non-payment reasons.


IT help desk customer inquiry sentiment analysis

Responding to complex customer inquiries, such as airline customer complaints or denied healthcare claims, takes time, challenging the goal of quick resolution. Whether researching the customer case, filling in gaps in customer information, or drafting appropriate communications, the potential for generative AI and Intelligent Automation to accelerate responding to and resolving customer inquiries is large. With generative AI, agents can handle more interactions in a shorter amount of time and reach resolution faster, reducing wait times and improving customer experience.

In this case, generative AI can assist with sentiment analysis of customer service inquiries by understanding the context and intent of incoming customer messages. With categorized intent and identified sentiment, Intelligent Automation can look up the relevant resolution in the knowledge base and initiate corresponding follow-up actions, in particular triggering generative AI to write a response communication to send back to the customer or, for more complex cases, route a draft response to a customer service agent for review and next steps.

Automated IT ticket response

A spike in IT help desk tickets can slow response times and impact productivity. With generative AI and Intelligent Automation, you can streamline the IT ticketing process and improve both the speed and quality of responses.

In this case, generative AI can instantly review incoming IT tickets, understanding the type and urgency of the issue. It can then generate an appropriate response or propose a solution based on past tickets and resolutions. Integrated Intelligent Automation delivers these responses to the users, saving the help desk team valuable time and reducing resolution time for end users.

IT help desk ticket classification and triage

Classifying and triaging IT help desk tickets is a crucial step in ensuring issues are addressed on time and by the right team. Manual triage is time-consuming and at risk of errors, while auto-categorization rules are insensitive to ticket priority. By automating classification and triage with the help of generative AI, the IT team can focus on resolving issues rather than sorting through tickets, improving overall efficiency and customer satisfaction.

In this case, generative AI can analyze incoming support tickets and classify them based on issue type, severity, urgency, and any other relevant parameters. It can then triage the tickets, working with Intelligent Automation integrated into your ticketing application to route them to the appropriate team or individual.

Cybersecurity threat detection analysis

Cybersecurity threats are increasing and constantly evolving, making it challenging for IT security teams to identify and respond to all threats in time to contain or mitigate the risk effectively. Applying generative AI and Intelligent Automation offers a proactive approach to cybersecurity to reduce vulnerability and enhance the resilience of IT infrastructure.

In this case, generative AI can assist by analyzing network traffic, user behavior, and system logs to identify potential security threats. It can predict the likelihood of a genuine threat and recommend actions based on that assessment. Intelligent Automation can trigger immediate protective measures when a threat is detected, such as isolating affected systems or initiating backups. Generative AI can also provide detailed reports of the incident, aiding in post-incident analysis and future threat prevention.

Automated IT support

In the quest for seamless, fast, and efficient IT support, many organizations are launching employee-facing chatbots to deliver an automated conversational experience. While chatbots can provide seamless integration with corporate systems and access to the existing enterprise knowledge base, they are not equipped to interpret intent, frustrating the tool's potential as a faster route to information and issue resolution. Adding generative AI, however, makes effective chatbot support not just possible but a reality.

In this case, generative AI can be added to an existing chatbot experience to understand intent. Intelligent Automation then works to initiate actual actions on systems and data based on the content of the chat conversation.


Contract reviews

Legal teams spend significant bandwidth meticulously reviewing lengthy and complex contracts for terms or clauses that could be problematic to avoid potential legal and compliance pitfalls. Legal language and contract complexity also tend to make contracts intractable for business stakeholders, hindering a clear understanding of terms and conditions.

In this case, generative AI with Intelligent Automation can securely handle sensitive contract data. It can learn legal guidelines and industry regulations to review contracts for compliance automatically. And it can highlight areas of concern, suggest amendments, and generate plain-language summaries for stakeholders.


Language translation

In today's globalized business environment, translating corporate content into various languages is essential for effective communication and engagement. Manual translation services are costly, and turnaround time can be longer than the pace of business demands.

In this case, generative AI can automate the translation process. It can understand the context and nuances of corporate content from website copy and marketing materials and translate it accurately into the desired language. Additionally, applying Intelligent Automation can schedule automatic translations of new content, ensuring that corporate communications are timely and accessible to all stakeholders, irrespective of language.


Sales outreach

Effective sales outreach is key to driving business growth. However, crafting personalized outreach messages can be a challenging and time-consuming task.

In this case, generative AI for sales outreach can analyze customer data, understand their preferences and needs, and generate persuasive, personalized outreach messages. Integrating generative AI with Intelligent Automation powers the sales outreach process, ensuring timely and consistent communication with potential customers and increasing conversion chances.

CRM data cleansing

One of the common complaints from sales teams is about poor data quality in CRM systems, which affects the accuracy of analysis and hinders decision-making. However, manual data cleansing is tedious, still prone to errors, and tends to fall down the list of priorities for time-strapped sales administrators.

In this case, generative AI can automate the data cleansing process. It can identify and correct errors in CRM data, remove duplicates, and fill in missing information. With Intelligent Automation, data errors or records that may require human input can be sent for review. The combination of Intelligent Automation and generative AI enables scheduling regular data cleansing to maintain CRM data that is accurate and up-to-date.

Generative AI use cases by industry

Banking and financial services

Transaction disputes (credit card, checking/savings)

Essential to ensuring the integrity and trust of customer accounts and to protect from fraud, handling transaction disputes remains a largely manual process. Disparate systems and the increasing volume of disputes strain an already error-prone and time-consuming effort.

In this case, generative AI and Intelligent Automation can work in tandem to reduce transactional losses, increase operational efficiency, and improve customer satisfaction. Automation begins on receipt of customer requests, which are automatically logged into the queue in Fiserv or another core banking system. Next, generative AI and automation scan and summarize the customer request and send it for a representative to review.

The bank representative can launch an automated workflow to retrieve related and historical data across systems such as ERP, payments, and CRM, which is then summarized by generative AI. With the information on hand, the bank representative can adjudicate the disputed transaction and initiate actions associated with the determined resolution via automation, with support from generative AI to draft appropriate communications to the customer.

Fraud detection and SAR investigation

Vigilant analysis of transaction data to detect potential fraudulent behavior is vital to the integrity of banks and financial services companies. Fraud detection analysis spans multiple data sources, from transaction records and customer information to external data, including deny lists and watchlists. The increasing complexity of fraud and high false positive rates can overwhelm under-resourced investigation teams, who already struggle to work across disparate banking systems.

Cost and time barriers to system integrations continue to stymie real-time fraud detection. For fraud detection and SAR investigation, generative AI and Intelligent Automation can help reduce operational losses, increase efficiency, improve customer satisfaction, and strengthen regulatory compliance.

In this case, an always-on system driven by generative AI and automation can gather and monitor data around the clock while continuously analyzing behavior patterns, device information, and social media activity. Generative AI can pre-process data and then analyze the data to identify patterns of potential fraud (e.g., identify theft, unauthorized payments/transfers, unauthorized account opening/closure, falsified credit applications, etc.) based on historical information.

When suspicious activity or transactions are identified, generative AI can trigger automated workflows with immediate tasks for investigators to act on. Data is available for in-depth review, and investigators can request additional information with help from an automation assistant or move to trigger automated next steps (e.g., transaction holds, account freezes, credit denials, etc.).

AML transaction monitoring

AML compliance relies heavily on the expertise of individuals who must make judgment calls and follow processes, which can sometimes lead to errors and inconsistencies in identifying and reporting suspicious activity. The ever-increasing volume of transaction data presents a significant challenge to banks and financial institutions. Mining this vast amount of data and navigating the high rate of false positives can overwhelm investigators, leading to delays in legitimate transactions and increased costs.

For AML transaction monitoring, the introduction of generative AI and Intelligent Automation can arm investigators with comprehensive data to enable more confident judgment calls and initiate automated next steps such as transaction holds, account freezes, etc., reducing the burden on investigators, increasing operational efficiency, and ensuring a higher level of AML compliance.

In this case, an always-on system powered by generative AI and automation can continuously ingest and analyze large volumes of data 24/7 from various sources, including transaction records, CRM, public info on the web, and watchlists. Generative AI can pre-process and organize the data by transaction amounts, types, customer demographics, and geographical location for easy analysis and identification of outliers and inconsistencies.

Generative AI can apply machine learning models to identify patterns based on historical data of potential fraud, such as high-risk transactions, potential money laundering, terrorism funding, transactions prohibited by economic sanctions, and other criminal activity. If the system detects a suspicious transaction or activity, it alerts automation, triggering immediate tasks for investigators to take action. The investigators can review all data and request additional data using an automation assistant directly in the banking system.

Loan underwriting approval decisioning

Loan underwriting involves assessing the risk of potential borrowers and determining if they meet the lending criteria. It’s a time-consuming process that is prone to human error, especially when dealing with complex loan types such as mortgages, auto loans, credit cards, and commercial loans. The challenge is further amplified by the need to comply with Fair Lending laws and avoid bias or discrimination in decision-making.

For loan underwriting approval decisioning, generative AI brings speed, accuracy, and efficiency to the process. With Intelligent Automation, generative AI can deep dive into an applicant's details, extracting and interpreting necessary data, and compare it with predefined lending criteria and historical data. The assessment feeds into the final decision where the underwriter can trigger Intelligent Automation workflows with approval, adjustment, or decline of the loan. In high-risk scenarios, applications can be flagged for human review before moving through the decision workflow automation.

Credit limit decisioning

Credit limit decisioning must balance risk management with customer satisfaction. With the integration of generative AI and Intelligent Automation, the process can be significantly optimized, enhancing accuracy, efficiency, and speed.

In this case, generative AI can dive into the intricate unstructured details of applicants’ credit history, analyzing past financial behaviors, current financial standing, and potential risks. By interpreting this data against predefined lending criteria and historical data, generative AI can produce a summary risk profile of each applicant, with payment and transaction history, to assist the underwriter in making credit limit decisions on complex accounts or feed into automated next steps. Generative AI and automation can draft communications and follow up with customers based on the credit limit decision, enhancing operational efficiency and supporting customer satisfaction with transparent communication.

Default loan workouts and foreclosure

Handling defaulted loans and foreclosure cases is complex, with much to be gained for financial services companies by finding ways to optimize the content and cadence of communications, creating repayment plans, and handling legal steps. Integrating generative AI and Intelligent Automation can streamline and accelerate decision making while reducing costs.

In this case, generative AI can synthesize information from various unstructured data, such as the borrower's profile, collateral details, and payment transaction history. Generative AI can draft comprehensive risk profiles for each delinquent loan, supporting underwriters in determining optimal workout programs, such as revised payment schedules or partial loan forgiveness. Based on their decision, Intelligent Automation can execute the corresponding workflows, powering speed and efficiency.

Complaint resolution

Complaint resolution is a critical aspect of customer service in the financial services industry. It typically involves examining multiple sources of customer information such as the account, product, and transaction history, to determine appropriate resolution paths.

For complaint resolution, generative AI can quickly collate account data and analyze the product and transaction history to understand the context of the complaint, reducing the time spent on researching information from multiple sources. Additionally, generative AI can look for patterns and trends in customer behavior that may shed light on the complaint, allowing for a more accurate and timely resolution. Based on these insights, Intelligent Automation can expedite appropriate resolution actions whether refund, apology, or change in service, and complex cases can be escalated for human review.

CSR enablement and next best action for service requests

Customer service representative (CSR) enablement and determining the next best action (NBA) for service requests are critical aspects of customer service in the financial services industry. Ensuring customer service quality involves a thorough understanding of customer information, transaction history, and an accurate prediction of customer needs. The process can be complex and time-consuming, especially when dealing with multiple systems and a large volume of requests.

In this case, generative AI embedded within an Intelligent Automation assistant can increase CSR effectiveness. Generative AI can provide a concise summary of customer information, suggested introductory scripts, and post-call documentation of customer interactions, along with insights into patterns and trends in customer behavior, allowing for more accurate predictions of customer needs, and driving better quality customer service. In tandem, Intelligent Automation can expedite CSR actions whether resolving an inquiry, suggesting a product, or escalating the call.


Cross-selling is a key strategy in the financial services industry, aimed at both enhancing customer satisfaction and driving revenue growth. It involves a comprehensive understanding of customer information and product history, along with the prediction of customer needs. Determining cross-selling opportunities and acting on them is a complex task, especially for a large customer base.

In this case, Intelligent Automation and generative AI can boost efficiency and propel sales by providing in-depth analysis of customer profiles and product history to suggest potential products and services that match customer needs or will increase customer retention. Generative AI can identify patterns and trends in customer behavior, allowing for increased accuracy in predicting customer needs and powering targeted product recommendations. Intelligent Automation further advances the process by deploying machine learning models to refine recommendations and produce tailored product offerings for diverse scenarios, such as credit cards to checking account customers, brokerage accounts to savings/CD customers, or auto/home loans to existing clients.


Medical summary for practitioners

Physicians spend on average 16 minutes reviewing patient electronic health records (EHR), regardless of the length of the visit. This time includes trying to pull together the important pieces of a patient's history to synthesize the overall medical picture, set priorities, and prescribe appropriate treatment or next steps. Even for uncomplicated cases, the task requires reviewing many sources of information including hospital notes, unstructured doctors' and nurses' notes, lab and imaging tests, medication history, family and social history, records from previous providers and specialty referrals, and more. This task becomes even more difficult when the patient has a complex medical history and multiple conditions that may affect each other.

In this case, robotic process automation (RPA) gathers patient data and generative AI creates a clear, concise summary that points out important issues that must be addressed or care gaps that should be filled. The result is an estimated 70-80% time savings on finding and synthesizing information, and a 40% productivity boost.

Patient message triage

33% of physicians spend two hours or more outside office hours responding to patient emails and messages, taking away personal and family time. It's a top cause of burnout in an alarming 63% of physicians, which poses significant risks to medical practice including disruptive behavior, increased medical errors and risk of malpractice, substance abuse, lower patient satisfaction scores, and longer patient recovery.

In this case, Intelligent Automation and generative AI can execute patient message triage, leveraging RPA and generative AI to analyze the unstructured content of messages to identify concerns and issues, and quickly summarize the message after checking the EHR system for context. This assistance allows physicians to spend roughly 75% less time deciphering the problem.

Generative AI can also list initial recommendations. The physician can consider the proposed recommendations and make changes, then use Automation Co-Pilot to launch the appropriate responses, whether to send the patient to the ER, book an appointment the next day, order labs or drugs, etc.

After-visit summary for patients

Studies indicate that a significant percentage of patients misunderstand medication instructions and cannot accurately relay their doctor's expectations after visits. However, studies also highlight the value patients place on a written medical summary or an after-visit summary (AVS). An AVS aids in patient recall of visit details, enabling them to update relatives more accurately, and fosters better patient-doctor communication. Patients feel empowered to ask more questions, while doctors believe it solidifies their treatment plans and encourages patient treatment compliance. Despite these benefits, doctors find the process time-consuming and are split on whether it's a worthwhile investment of time.

In this case, Intelligent Automation and generative AI can together accelerate collecting and collating patient data to draft a personalized AVS for the physician to review, in a fraction of the time. The automation can leverage RPA to quickly and securely retrieve, validate, curate, format, and assemble patient data from diverse systems including EHR, insurance coverage, and demographics to enable generative AI to understand the complete medical context before creating a summary.

Generative AI can then synthesize and summarize, leveraging its strength in understanding unstructured information and context, and its ability to tailor output to match personalization parameters such as language, age, culture, educational level, healthcare literacy, etc. The draft AVS will be ready to be reviewed, validated, and edited/updated by the physician as necessary, including current visit information, diagnoses, treatment recommendations, and follow-up, before being given to the patient and their family or caregiver.

Population health analyses

Crucial for identifying trends, preparing research, understanding health disparities, and planning interventions to improve health outcomes across different populations, population health analyses involve sifting through vast and varied datasets, including EHR, census data, insurance databases, and social determinants of health, each with its unique format and structure, requiring significant time and resources.

In this case, Intelligent Automation and generative AI can efficiently gather, curate, and analyze the data required for population health analyses. Intelligent automation can leverage RPA to securely and quickly retrieve, validate, and collate data from multiple disparate systems to build a comprehensive view of a population's health status.

With this prepared dataset, generative AI can identify patterns, trends, and correlations. It can generate insights into disease prevalence, risk factors, health disparities, and the impact of social determinants on health outcomes. These insights can then be summarized in a format tailored to each target audience, whether policymakers, healthcare providers, or the public. Generative AI can also create predictive models based on the analyzed data to help model and forecast health trends and outcomes, allowing for proactive planning and intervention.