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A transformative agentic AI approach can be applied across various sectors, each with domain-specific opportunities. Below, we focus on four key industries — banking, insurance, healthcare and retail — highlighting how agentic AI solutions drive innovation and efficiency in each. These use cases illustrate the tangible benefits of the framework in action.

Banking 

Banks are increasingly adopting AI and machine learning to enhance customer service, prevent fraud and optimize operations. In front-office functions, AI-powered conversational agents (chatbots and voice assistants) handle routine customer inquiries 24/7. This not only provides quick responses to customers but also frees up human bank staff to focus on complex, high-value interactions. For example, a chatbot can assist a customer with resetting a password or checking account balances, while human agents concentrate on providing financial advice or handling loan negotiations. These agentic AI customer assistants ensure a more responsive and consistent customer experience. 

Fraud detection and risk management have been revolutionized by agentic AI in banking. Autonomous systems powered by platforms like Azure can monitor transactions in real time and spot suspicious patterns far faster than traditional rule-based systems. For instance, an agentic AI system will learn a customer’s typical spending behaviors and immediately flag anomalies (such as an unusually large overseas transaction) for review. By detecting fraudulent activities in real-time, banks can reduce losses and protect customer accounts, enhancing trust in the financial system. One large bank that implemented an AI fraud detection agent reported a significant drop in fraud losses in the first year of use, thanks to the AI’s ability to catch fraudulent transactions that slipped past older controls.

Mid-office and back-office operations in banking also benefit from agentic automation. AI agents can help with regulatory compliance by scanning transactions and communications for compliance triggers (anti-money-laundering checks, KYC verifications) more thoroughly and continuously than a human team could. In loan processing, an AI agent can autonomously gather applicant data from various sources, perform credit risk analysis and even make preliminary approval decisions based on predefined criteria, drastically speeding up loan approvals. Furthermore, banks are exploring advanced frameworks like Microsoft’s AutoGen to drive innovation in core banking. Using AutoGen, banks can connect multiple AI agents (for data retrieval, analysis and decision-making) to collaborate on tasks like intelligent report generation or portfolio optimization.

Microsoft’s AutoGen framework, in particular, has been highlighted for enabling complex multi-agent workflows in banking operations, demonstrating how coordinated AI agents can handle end-to-end processes that traditionally required multiple teams.

Overall, the banking industry’s use cases for agentic AI range from customer-facing bots to behind-the-scenes risk models. The impact is evidenced in faster service (e.g., instant responses via chatbots), improved security (real-time fraud interdiction) and more efficient operations (automation of routine tasks). As a result, banks not only save on operational costs but also deliver better customer satisfaction and comply more effectively with regulations.

Insurance 

In the insurance sector, agentic AI solutions are transforming how insurers assess risk, process claims and interact with customers. Predictive analytics and AI-driven automation are now key to modern insurance operations.

A prime use case is in claims processing. Traditionally, filing and handling insurance claims is a labor-intensive process prone to bottlenecks and errors. With AI, however, an insurer can deploy an intelligent claims agent that automatically reads claim forms (using OCR and natural language understanding), checks the details against policy coverage, detects any inconsistencies or potential fraud, and even initiates payout for simple, straightforward cases — all without human intervention. This level of automation can dramatically speed up claims settlements from weeks to mere days or hours, delighting customers at a moment of need. By leveraging Microsoft’s cloud and AI platforms as the backbone, insurers have achieved claims processing that is not only faster but also more accurate and consistent, reducing manual errors.

Another critical area is risk assessment and underwriting. Insurance companies are feeding years of historical data (e.g., actuarial tables, customer profiles, incident reports) into machine learning models to forecast risks more precisely. An autonomous underwriting agent can evaluate an insurance application (whether health, auto or property) and instantly draw on predictive models to classify the risk level of the applicant.

For example, in auto insurance, an agentic AI might integrate data from telematics (driving behavior data) and automatically adjust a driver’s risk score and premium pricing. This not only improves accuracy in pricing policies but also enables personalized products (safe drivers get immediate rewards with lower premiums, risky drivers are flagged for further review). Our experience in the insurance domain shows an enormous breadth of opportunities for such AI interventions — over 100 different use cases have been identified across the insurance value chain where AI can drive efficiency and improve customer experience.

These range from automated policy renewal reminders to AI advisors that guide customers in choosing the right coverage to advanced analytics that help insurers manage their capital and reserves more effectively. 

On the customer service front, insurance companies use AI chatbots to answer coverage questions or help customers file claims in a guided manner. This is particularly useful during natural disasters or large-scale events, when claim volumes surge — AI agents can handle the spike in inquiries and initial filings, ensuring customers aren’t stuck waiting on hold.

Generative AI is also being employed: For instance, to summarize complex policy documents into plain language summaries for customers, or to draft personalized communication when a claim is approved or denied. In call centers, agent-assist AI can listen to live calls and provide human agents with real-time suggestions or relevant information, improving call resolution quality.

Additionally, fraud detection in insurance benefits from agentic AI. AI systems analyze claims data, social media and even claimant behavior patterns to flag potentially fraudulent claims (for example, detecting if the same person has filed claims under multiple identities, or if a medical claim shows suspicious billing codes). These AI-driven fraud flags help investigators prioritize their efforts, leading to significant savings in fraudulent payouts.

Implementing these agentic AI solutions leads to not just cost savings but also superior customer trust and loyalty. When claims are processed swiftly and transparently, customer satisfaction rises. Moreover, by automating routine tasks, insurance companies enable their workforce to focus on more nuanced judgment calls and on designing better insurance products. When integrating responsible AI solutions, it’s important to ensure that decisions (like policy pricing or claim rejections) made by AI are fair and explainable to regulators and customers alike. With the insurance industry being data-rich and historically document-heavy, the infusion of agentic AI is truly streamlining operations from end to end, heralding a new era of agile and customer-centric insurance services.

Healthcare

Healthcare providers are leveraging agentic AI to make significant strides in diagnostics, treatment personalization and operational efficiency.

One of the most impactful use cases is in medical diagnostics: AI systems, trained on vast amounts of medical data and imagery, can assist doctors by analyzing tests and scans with a high degree of accuracy. For example, an AI agent can examine radiology images (X-rays, MRIs, CT scans) and highlight areas of concern or potential anomalies that a physician should review. These diagnostic agents act as a second pair of eyes, often catching early signs of conditions like tumors or fractures that might be subtle. In some studies, AI models have demonstrated accuracy on par with specialists in detecting certain illnesses. Integrating such an agent into the workflow means a radiologist can act more quickly and confidently, focusing attention where the AI flags issues. This leads to earlier and more accurate diagnoses, which is critical for patient outcomes. 

Beyond diagnostics, personalized treatment plans are another area where agentic AI shines. Every patient’s condition is unique, and AI can sift through thousands of similar case histories and medical research papers to recommend tailored treatment options. For instance, in oncology, an agentic solution can analyze a cancer patient’s genetic data alongside treatment databases to suggest which therapies have the highest probability of success. This augments the doctor’s decision-making with data-driven insights. Similarly, AI-driven systems can monitor patient vitals in real time (for hospitalized patients or through wearable devices for outpatients) and alert clinicians to any worrying trends, sometimes even adjusting medication dosages automatically (such as insulin delivery for diabetics via smart pumps) in response to the patient’s current status. 

Patient data management and administrative tasks in hospitals also benefit from AI automation. Scheduling agents can optimize appointments and staff rosters, reducing wait times and avoiding physician overload. In emergency departments, triage agents can prioritize incoming patients by severity using symptom-checking algorithms, ensuring critical cases get attention faster. Moreover, hospitals use AI for predictive analytics, such as forecasting patient admission rates (to allocate enough beds and staff) or predicting which patients are at risk of readmission after discharge, so that preventative care can be provided. These predictions improve operational efficiency and patient care quality. 

Another emerging area is drug discovery and research: while not a direct provider use case, the healthcare industry at large benefits from agentic AI that can analyze biochemical data to suggest new drug compounds or identify potential side effects of existing drugs. Pharmaceutical companies and research institutions, sometimes in collaboration with tech firms, are using autonomous agents to run simulations and analyze trial data faster than ever. This could significantly speed up the development of new treatments. 

In summary, agentic AI in healthcare assists doctors and nurses by providing data-driven support for diagnoses and decisions, automates mundane yet important tasks, and helps tailor care to individual patients. The ultimate outcomes are improved patient outcomes (through timely and precise care), enhanced operational efficiency (doing more with limited healthcare staff and resources) and reduced burnout for medical professionals (by offloading data-crunching tasks to AI). With the guidance of responsible AI practices, healthcare can embrace these technologies while keeping the focus squarely on compassionate, human-centered care.

Retail

Retailers are adopting AI to optimize inventory management, personalize customer experiences and streamline supply chains.

In the retail industry, margins are often tight and customer loyalty is hard-won; agentic AI provides tools to operate smarter and react faster to market demands.

One of the most impactful retail use cases is inventory optimization and demand forecasting. AI agents analyze historical sales data, seasonal trends and even external factors like weather or local events to forecast product demand with high accuracy. As a result, retailers can maintain optimal stock levels — avoiding the twin problems of stockouts (which lead to lost sales and unhappy customers) and overstocking (which ties up capital and may lead to markdowns or waste). For example, a fashion retailer might use an AI system to predict that demand for raincoats will spike next month due to weather forecasts, and thus it should redistribute inventory to stores in regions expecting heavy rain. By predicting demand and adjusting inventory in advance, retailers can reduce waste and markdowns (improving profitability) and ensure customers find the products they want.

Some retailers report significant improvements, such as a notable reduction in lost sales (due to better in-stock availability) and lower inventory holding costs, after implementing AI-driven demand forecasting tools.

Personalized customer experiences are another domain where agentic AI is making waves. E-commerce platforms and brick-and-mortar retailers alike are using AI to tailor the shopping experience to each customer. Recommendation engines, powered by machine learning, analyze browsing and purchase history to suggest products a customer is likely to want, increasing cross-sell and upsell opportunities. In-store, some retailers have experimented with AI-driven personalized promotions — for instance, a loyalty app that greets a customer when they enter and offers a tailored discount based on their past purchases. Chatbots on retail websites serve as personal shopping assistants, handling customer queries about product details, checking stock at nearest stores and even helping with the checkout process. These chatbots operate continuously and can handle multiple customers at once, significantly enhancing online customer service responsiveness.

Supply chain and logistics operations in retail also gain efficiency through agentic AI. From warehouse management to delivery routing, AI systems can optimize each step. In warehouses, AI-driven robots (a physical manifestation of agentic solutions) can autonomously pick and move goods, guided by algorithms that optimize picking routes and storage organization. When it comes to delivery, AI can plan logistics and delivery routes for shipments to minimize transit times and costs, accounting for traffic conditions and fuel usage. For global retailers, autonomous agents monitor supply chain risks – for example, by analyzing news and alerts, an AI agent might warn of a potential delay due to a port strike or a factory issue, prompting the retailer to re-route shipments or find alternate suppliers proactively.

Retailers are also employing AI vision systems in stores: autonomous checkout (like Amazon’s famous Go stores) uses sensors and AI to let customers simply walk out with goods and be automatically charged, eliminating checkout lines. Even without fully autonomous checkout, many stores use AI-based video analytics for purposes like detecting theft or understanding shopper behavior patterns within the store (which aisles see the most traffic, etc.).

The key is integrating these AI solutions seamlessly with existing retail systems such as ERP, CRM and POS systems and creating a unified view of the customer and inventory. For example, by integrating inventory AI with both the warehouse management system and the point-of-sale system, a retailer can automatically trigger restocks or inter-store transfers the moment sales data indicates a certain threshold.

The result for retailers is improved customer satisfaction (customers find what they need, when and where they need it, and receive personalized service) and better operational efficiency (leaner inventories, efficient logistics and lower costs). In a sector where consumer expectations are high and competition is fierce, agentic AI offers a way to differentiate through superior service and agile operations.

This article was made possible by our partnership with the IASA . The CAF’s purpose is to test, challenge and support the art and science of Business Technology Architecture and its evolution over time as well as grow the influence and leadership of chief architects both inside and outside the profession. The CAF is a leadership community of the , the leading non-profit professional association for business technology architects.

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