Real-World AI Use Cases

Situation

The bank's contact center handles thousands of customer interactions daily. Agents operate under high workload and limited time per call.

Key challenges:

• Agents spend significant time searching for information across multiple systems
• Knowledge about products and processes is fragmented and not always up to date
• Service quality depends on the experience of individual agents
• It is difficult to ensure a consistent communication standard with customers
• Cross-sell and up-sell opportunities are often missed

As a result, handling time increases, customer service quality declines, and sales effectiveness is reduced.

Solution

An intelligent agent assistant that operates in real time during customer interactions.

• Real-time guidance. AI analyzes the conversation and suggests relevant responses, scripts, and phrasing
• Access to knowledge and products. The assistant instantly provides information on products, pricing, procedures, and policies
• Next-best-action recommendations. AI suggests optimal conversation flows, including clarifying questions, product offers, and objection handling
• Customer context. Integration with CRM and banking systems enables visibility into customer data and interaction history
• Sales support. AI identifies cross-sell and up-sell opportunities and guides agents on when and what to offer

Situation

The bank's contact center faces a continuous increase in the volume of customer inquiries related to standard and repetitive requests.

Key challenges:

• a high share of routine inquiries that do not require agent involvement
• contact center overload during peak periods
• increasing service costs as operations scale
• long customer wait times
• limited service availability outside working hours

As a result, the contact center becomes a bottleneck for scaling customer service and negatively impacts customer experience.

Solution

AI Customer Support is a system of virtual assistants that enables automated customer service across voice and digital channels.

• Automated request handling. AI assistants process routine customer inquiries without agent involvement
• Omnichannel service. Support for voice and digital channels (contact center, mobile app, website, messaging platforms)
• Integration with banking systems. Real-time access to customer data, products, and transactions
• Personalized responses. Responses are generated based on customer profile and interaction context
• 24/7 scalability. The system can handle thousands of interactions simultaneously without quality degradation

Situation

The bank is transforming its IT landscape, moving from a monolithic core banking system to a product-based model where products are developed and managed as independent services.

Key challenges:

• long time-to-market for new products
• strong dependency of product changes on the core banking system and complex IT architecture
• fragmented processes: business, IT, legal, and marketing functions operate asynchronously
• high cost of developing and modifying product offerings
• risk of impacting stability of existing systems when introducing changes

As a result, the bank is constrained in its ability to launch products quickly and manage its product portfolio flexibly.

Solution

AI Deposit Factory is a product platform with an AI-driven pipeline for designing, launching, and managing deposit products.

• Dedicated product factory. Deposit products are implemented as independent services integrated with front-end channels and the general ledger (GL)
• AI-driven product development pipeline (Product as Code). AI automates key stages of product creation from concept to launch
• Product definition. AI structures product ideas into a complete specification including terms, parameters, constraints, and usage scenarios
• Requirements generation. Automatic creation of functional and integration requirements aligned with the bank's IT architecture
• Product development and launch. AI supports code generation, test cases, documentation, and sales materials
• Unified product lifecycle. Synchronization of business logic, IT implementation, and supporting materials within a single process

Situation

The company works with a large volume of contracts and external documents that must be verified for compliance with internal policies and regulatory requirements.

Key challenges:

• a significant share of manual document review performed by experts
• high risk of missing inconsistencies and critical terms
• long approval cycles due to multiple review iterations
• lack of unified standards and transparency in control processes
• limited ability to scale the process

As a result, operational and legal risks increase, interactions with counterparties slow down, and overall business process efficiency declines.

Solution

AI Document Compliance is a centralized system for automated document verification across all stages of the approval process.

• Requirements checklist management. The system uses structured requirements (internal and external), defined manually or automatically based on existing documents
• Automated document validation. AI analyzes documents for compliance with requirements, identifies discrepancies, and generates comments
• Content-level comparison. Version comparison to detect changes, deviations, and potential risks
• End-to-end control. Validation is performed before, during, and after the approval process
• Result tracking and escalation. All checks are logged, with automatic escalation to responsible stakeholders when required

Situation

A government agency is developing a new digital legal assistance service for citizens to replace an outdated website and improve accessibility of public services.

Key challenges:

• difficulty in obtaining clear and understandable legal guidance for real-life situations
• high workload across offline service channels and legal support staff
• need to consider individual context of each citizen's request
• large volume of regulatory documents and complexity of their interpretation
• lack of convenient tools for document preparation and interaction with government authorities, complexity of form completion

As a result, citizens face challenges in obtaining timely and accurate legal support, while government services experience high workload and low efficiency.

Solution

AI Legal Advisor is an intelligent legal support service based on regulatory knowledge and expert content.

• Unified knowledge base. A centralized repository of regulations, real-life scenarios, instructions, templates, and FAQs
• Context-aware consultation. AI analyzes the request and clarifies relevant details through dialogue, adapting its guidance to the citizen's specific situation
• Step-by-step recommendations. Clear instructions on required actions, documents, and points of contact
• Document preparation support. Assistance with completing forms and preparing required documents
• Content accuracy and governance. Content is maintained through a combination of AI and expert validation with regular updates

Situation

The bank is implementing a growing number of strategic initiatives and projects, while the workload on the Project Management Office (PMO) is increasing significantly.

Key challenges:

• Fragmented data across projects and initiatives
• High share of manual coordination, including meeting minutes, reminders, and approvals
• Limited PMO staffing and overloaded key PMO experts
• Delays between decision-making and actual execution
• Limited transparency of strategy execution status
• Limited PMO capacity to manage projects

As a result, management receives information with delays and cannot influence the execution of strategic initiatives in a timely manner. The leadership recognized the need to move from a manual coordination-based PMO to a digitally managed PMO, where Board decisions are immediately translated into execution.

Solution

AI PMO Assistant is a system of digital assistants that automates key PMO processes and enables real-time management of the portfolio of initiatives.

• Project Committee Assistant. Automation of agenda preparation, decision recording, and launch of follow-up actions
• Project monitoring. AI automatically collects statuses and identifies deviations in timelines and resources
• AI reporting and analytics. Preparation of management reporting and analytics for leadership
• Initiative portfolio management. Support for project prioritization and early identification of strategic risks
• Decision execution automation. Bots create tasks in systems, launch assignments, and monitor execution deadlines

Situation

The bank regularly processes a large volume of requests from regulators and customers received through traditional channels (mail hardcopies, e-mails, formal requests).

Key challenges:

• strict regulatory deadlines for response submission must be met
• a high share of unstructured and partially structured requests
• required data is distributed across legacy systems with limited accessibility
• strong dependency on manual work and cross-functional coordination
• complexity in handling non-standard requests

As a result, the process is labor-intensive, slow, and exposed to operational and compliance risks.

Solution

AI Regulatory Processing is an end-to-end processing pipeline for handling requests from intake to response registration using AI and RPA.

• Request recognition and data extraction. AI analyzes incoming requests and extracts key parameters and requirements
• Classification and routing. Determination of request type and processing logic: standard scenario or escalation to internal units
• Data extraction from systems. RPA retrieves data from legacy systems without requiring API integrations
• Cross-functional interaction management. AI agents initiate requests to internal departments and process responses (including via e-mail)
• Response generation. For standard requests, the system generates responses automatically. For non-standard requests, it aggregates the required data and prepares a draft response for employee review
• Control and registration. The response is verified by an employee and automatically registered in internal systems

Situation

The company faced a critical limitation in scaling its client business:

• requirement for 100% KYC checks of all clients and related parties
• high complexity of verification (beneficiaries, shareholders, representatives, governing bodies)
• limited Compliance team capacity to handle growing client volumes
• high risk of regulatory violations and sanctions when processing manually

Additionally, the process was fragmented, dependent on manual operations, and did not ensure consistent quality or transparency of checks.

Solution

A centralized automated KYC verification system based on RPA was implemented, covering the full verification cycle.

• Automatic retrieval and processing of client application data from internal systems
• Extraction of data across the full client structure: client, beneficiaries, shareholders, representatives, related parties
• Execution of a complete set of checks for each entity
• Verification against multiple internal and external data sources
• Automated analysis of open sources and media
• Centralized aggregation of results and reporting
• Key feature: transition from isolated checks to a scalable Compliance verification factory operating 24/7 and supporting both onboarding and ongoing monitoring of the client base

Situation

The quality of customer service in the contact center directly depends on the skills of agents, while quality management and training remain resource-intensive and limited in coverage.

Key challenges:

• it is not possible to monitor 100% of agent interactions
• quality assessment is selective and subjective
• training requires significant time and resources
• agent errors are repeated and not systematically addressed
• there is no transparent link between interaction quality and business outcomes

As a result, customer service quality declines, operational losses increase, and the team's efficiency growth is constrained.

Solution

AI Training & Quality is a system for accelerated agent development embedded into daily contact center operations.

• Identification of skill gaps in agents
• Personalized training recommendations
• AI simulator with conversation scenarios
• Training based on real cases
• Monitoring of progress and training effectiveness