What are the typical use cases and ROI for deploying AI chats?

Customer support and service

One of the most common areas for implementing AI chats is customer support, where these systems bring measurable economic benefits and improve the user experience.

Key use cases in customer support

AI chats offer a wide range of applications in customer service:

• First-line support automation: Automating the resolution of common and repetitive queries (order status, product information, solving basic problems)
• 24/7 availability: Providing continuous support regardless of human operator working hours
• Multilingual support: Offering support in multiple languages without the need for specialized language teams
• Assistance to human agents: Preparing answers, searching for information, and automating routine tasks for human operators
• Self-service support portals: Implementing interactive, conversational interfaces for problem-solving and information retrieval
• Proactive support: Identifying and resolving potential issues before they escalate

Economic benefits and ROI metrics

Implementing AI chats in customer support brings measurable economic benefits:

• Reduction in query processing costs: Average cost reduction per contact by 25-50%, depending on query complexity and automation level
• Reduced response time: Typically a 60-80% reduction in the time needed to get an initial response
• Increased first contact resolution rate: Average improvement of 15-25% due to consistent and data-driven answers
• Reduced escalation rate: Typically a 20-30% decrease in the number of queries escalated to higher support levels
• Increased agent productivity: Average increase in human operator efficiency by 25-35% thanks to AI assistance

For an organization processing 10,000 customer queries per month, this can represent an annual saving of 3-5 million CZK, with a typical return on investment period of 6-12 months.

Customer experience and indirect benefits

In addition to direct economic benefits, implementing AI chats also brings significant improvements to the customer experience:

• Increased customer satisfaction: Average CSAT increase of 10-20 points due to immediate availability and quick resolution
• Reduced customer churn rate: Typical reduction of 5-15% due to improved response times and effective problem-solving
• Response consistency: Elimination of variability in support quality and ensuring a uniform tone and approach
• Expansion of self-service support options: Increase in the proportion of customers using self-service channels by 30-50%
• Personalization of interactions: Tailoring support based on customer history and context

Sales and marketing

In sales and marketing, AI chats offer significant potential for improving conversion rates, personalizing the customer journey, and increasing the overall effectiveness of sales activities.

Sales use cases and applications

AI chats transform sales processes through the following applications:

• Interactive product discovery: Assisting customers in finding suitable products based on their needs and preferences
• Proactive sales assistance: Offering relevant help at key moments in the customer journey
• Personalized product recommendations: Generating contextually relevant recommendations based on preferences and history
• Lead qualification automation: Identifying and pre-qualifying potential customers before involving human salespeople
• Purchase completion assistance: Proactively addressing obstacles and uncertainties during the purchasing process
• Post-purchase follow-up communication: Automated follow-up after purchase to increase retention and cross-selling opportunities

Marketing applications

In marketing, AI chats offer innovative ways of engagement and interaction:

• Interactive campaigns: Creating engaging, conversational marketing activities
• Personalized content delivery: Distributing relevant content based on interests and needs
• Event promotion and registration: Assisting with event promotion and simplifying the registration process
• Social media engagement: Automated and personalized interactions on social platforms
• Marketing research and feedback collection: Interactive collection of feedback and market insights

Economic benefits and ROI metrics

Implementing AI chats in sales and marketing activities brings measurable economic benefits:

• Increased conversion rates: Average increase of 20-35% thanks to personalized assistance during the purchasing process
• Increase in average order value: Typical increase of 10-25% due to relevant cross-sell and upsell recommendations
• Reduction in cart abandonment rate: Decrease in shopping cart abandonment rate by 15-30% through proactive assistance
• Increased ROI of marketing campaigns: Average improvement of 15-25% due to personalization and better targeting
• More efficient lead qualification: Reduction in lead qualification costs by 30-50% thanks to automation of initial screening

For an e-commerce platform with a monthly turnover of 10 million CZK, implementing an AI chat can mean additional annual revenue of 15-25 million CZK, with a typical return on investment period of 3-9 months.

Internal use and employee experience

Besides external customer-focused applications, AI chats also bring significant benefits when deployed internally to improve employee experience and increase the efficiency of work processes.

HR and employee onboarding applications

In human resources, AI chats offer several effective implementations:

• Onboarding assistance: Interactive guide for new employees with personalized information and documentation
• HR self-service support: Automated answers to common HR queries (benefits, leave, work policies)
• Training and development: Personalized training materials and support for continuous learning
• Employee feedback collection: Interactive and anonymous tools for collecting feedback
• Recruitment and candidate communication: Automated initial communication with candidates and screening

Knowledge management and internal support

AI chats transform access to company knowledge and internal support:

• Internal helpdesk: Automated resolution of common IT queries and issues
• Knowledge base access: Intuitive, conversational access to company knowledge and documentation
• Meeting assistant: Automation of minute-taking, identification of tasks, and follow-up actions
• Document search and summarization: Efficient searching and summarization of extensive documents
• Cross-departmental knowledge sharing: Improved access to information across organizational units

Operational efficiency and ROI metrics

Internal implementation of AI chats brings significant economic benefits:

• Increased employee productivity: Average increase of 15-25% due to faster access to information and automation of routine tasks
• Reduction in internal support costs: Typical decrease of 30-50% thanks to automation of common query resolution
• Reduced onboarding time: Average reduction of 25-40%, leading to faster productivity for new employees
• Reduced employee turnover: Typical reduction of 5-15% due to better support and information availability
• More efficient knowledge transfer: Reduction in time spent searching for information by 30-50%

For an organization with 500 employees, internal implementation of an AI chat can bring annual savings and productivity gains worth 5-10 million CZK, with a typical return on investment period of 9-18 months.

Sector-specific implementations

Different sectors implement AI chats in specific ways reflecting their unique needs, regulatory environments, and business models.

Retail and e-commerce

In the retail and e-commerce environment, AI chats offer several highly effective applications:

• Virtual shopping assistant: Personalized guide through the shopping process with recommendations based on preferences
• Order tracking and management: Interactive order tracking and resolution of related issues
• Stock availability check: Real-time information on product availability and alternatives
• Personalized promotions: Targeted offers and discounts based on customer profile and history
• Voice shopping integration: Expanding shopping options with voice interactions

Impact on ROI: Average increase in conversions by 25-40% and increase in customer lifetime value by 15-30%, with a typical payback period of 4-8 months.

Financial services

In the financial services sector, AI chats are implemented with an emphasis on security and regulatory compliance:

• Account management assistance: Secure access to account information and basic transactions
• Financial advice: Basic financial advice and personalized product recommendations
• Loan pre-qualification: Automated screening and pre-qualification for loan products
• Fraud detection and reporting: Assistance in identifying and reporting suspicious activities
• Financial education: Interactive educational content about financial products and concepts

Impact on ROI: Average reduction in operating costs by 20-35% and increase in cross-selling revenue by 10-25%, with a typical payback period of 8-14 months.

Healthcare

In healthcare, AI chats are implemented with an emphasis on empathy and accuracy:

• Patient triage: Initial assessment of symptoms and directing to appropriate care
• Appointment scheduling: Simplifying the process of booking and changing appointment times
• Medication reminders: Personalized notifications and monitoring of medication adherence
• Follow-up care: Automated monitoring of patient status after treatment
• Health education: Personalized information about health conditions and prevention

Impact on ROI: Average reduction in appointment no-show rates by 25-40% and reduction in administrative costs by 15-30%, with a typical payback period of 10-18 months.

B2B and professional services

In the B2B environment and professional services, AI chats are implemented with an emphasis on expertise and complexity:

• RFP and bid assistance: Support in preparing and managing proposals
• Access to technical documentation: Simplified access to extensive technical documentation
• Client onboarding: Streamlining the process of engaging new clients
• Project status updates: Automated reporting on project status
• Supplier management: Simplifying communication and coordination with suppliers

Impact on ROI: Average increase in sales productivity by 20-35% and improvement in client retention by 10-20%, with a typical payback period of 6-12 months.

ROI calculation methodology

Accurate and realistic calculation of return on investment is crucial for justifying the implementation of AI chats and setting the right expectations for stakeholders.

Components of ROI calculation

A comprehensive ROI analysis for AI chat implementation should include the following components:

• Initial investment costs: All upfront costs including licenses, implementation, integration, and training
• Ongoing operational costs: Recurring costs including API fees, maintenance, updates, and personnel
• Direct cost savings: Quantifiable savings, primarily reduction in personnel costs and operational efficiency
• Revenue increase: Incremental revenue from higher conversions, cross-selling/upselling, and customer retention
• Accelerated time-to-value: Faster realization of benefits compared to traditional solutions
• Risk-adjusted projections: Consideration of potential risks and variability in expected outcomes

Framework for ROI calculation

For a structured ROI calculation, we recommend the following approach:

1. Establish baseline: Document the current state of key metrics (cost per contact, conversion rates, average order value)
2. Calculate total investment: Detailed quantification of all costs over the relevant time horizon (typically 3 years)
3. Project benefits: Conservative estimation of expected benefits based on industry benchmarks and specific context
4. Adjust for time value: Account for the time value of money and the gradual ramp-up of benefits
5. Sensitivity analysis: Evaluate the impact of different scenarios and variables on the overall return
6. Assess non-financial benefits: Qualitative assessment of benefits that cannot be directly quantified financially

Key performance indicators for monitoring ROI

For ongoing monitoring of ROI realization, it is important to track the following metrics:

• Interaction deflection rate: Percentage of interactions successfully resolved by the AI chat without human intervention
• Cost per interaction: Average cost per interaction compared to the baseline
• Conversion lift: Percentage improvement in conversion rates attributable to the AI chat
• Resolution time: Average time required to resolve a query compared to the pre-implementation baseline
• Change in customer satisfaction: Change in CSAT, NPS, or other customer satisfaction metrics
• Automation rate trend: Evolution of the share of fully automated interactions over time

Case studies and benchmarks

Real-world implementations of AI chats across various sectors provide valuable insights into potential return on investment and best practices.

Case Study: Retail Implementation

Context: A medium-sized e-commerce retailer (annual turnover 500 million CZK) implemented an AI chat for customer support and sales assistance.

Implementation: Deployment of a multi-purpose AI chat integrated with CRM, e-commerce platform, and knowledge base.

Results after 12 months:

• 70% reduction in response time to customer queries (from an average of 4 hours to 15 minutes)
• 35% increase in conversion rate for customers interacting with the AI chat
• 28% increase in average order value due to personalized recommendations
• 22% reduction in customer service costs due to a 65% self-resolution rate
• ROI: 380% after the first year, with breakeven point reached after 5 months

Case Study: Financial Services

Context: A medium-sized financial institution implemented an AI chat for customer support and initial lead qualification.

Implementation: Secure AI chat with emphasis on regulatory compliance, integrated with the core banking system and CRM.

Results after 12 months:

• 45% reduction in processing time for common queries related to accounts and transactions
• 32% increase in lead qualification efficiency due to automated pre-screening
• 25% reduction in call center volume due to an effective self-service channel
• 18% increase in cross-selling conversion due to personalized product recommendations
• ROI: 220% after the first year, with breakeven point reached after 9 months

Case Study: Internal Implementation

Context: A large corporation (2500+ employees) implemented an AI chat for internal support and knowledge management.

Implementation: Enterprise-wide AI assistant integrated with internal systems, HR database, and knowledge repositories.

Results after 12 months:

• 75% reduction in time needed to find internal information and documents
• 42% decrease in IT helpdesk requests due to self-service solutions
• 30% reduction in time spent on administrative tasks due to automation
• 35% improvement in employee satisfaction with internal support services
• ROI: 180% after the first year, with breakeven point reached after 11 months

Industry benchmarks and trends

Based on aggregated data from various implementations, we can identify the following benchmarks and trends:

• Average ROI horizon: 150-300% in the first year for a properly implemented solution
• Typical breakeven point: 6-12 months depending on the sector and implementation complexity
• Automation rate: 60-80% for transactional and informational queries, 30-50% for more complex interactions
• Impact on customer satisfaction: Average CSAT improvement of 15-25 points with effective implementation
• Agent productivity increase: 25-45% increase in human agent efficiency in a hybrid model
• Impact on conversion: 20-40% increase in conversion rates when used in sales-oriented use cases