AI agents in Business - real case studies

Transforming Business Operations with AI-Driven Solutions

1. AI Agents in Business Transforming Business Operations with AI-Driven Solutions Yaser Alimardani Zonozi 03.09.2024

2. Agenda Introduction to AI Agents Fundamentals of AI Agents in Business Integration and Application in Business Case Studies Future Trends Challenges and Solutions Q&A

3. Introduction to AI Agents Brief definition of AI agents Autonomous systems that perform tasks Make decisions Assist humans in business processes Assist analyzing massive data Learn Analyze environment Examples Financial Trading Dynamic Pricing Systems Autonomous Vehicles Fraud Detection

4. Fundamentals of AI Agents Types of AI Agents Utility-Based Agents Financial Trading Dynamic Pricing Systems Personalized Content Recommendations Goal-Based Agents Robotic vacuum cleaners Scan documents and analyze text Localize text Model-Based Reflex Agents Autonomous Vehicles Home automation systems Learning Agents Fraud Detection Virtual Assistants Chatbots + take actions Key functionalities Automation Decision support Assist human interactions Fetching raw data by sensors or inputs Learning

5. Integration and Application in Business How AI agents are integrated Customer Service AI-driven chatbots and virtual assistants Operations Optimizing logistics in supply chain management Decision-Making AI in predictive analytics for business strategy like f raud detection Benefits Cost savings Efficiency and speed 24/7 availability Improved customer satisfaction

6. Case Studies Case studies Case Study 1: AI in Customer Service Case Study 2: AI in Supply Chain Management Case Study 3: AI in Finance Key functionalities Automation Decision support Assist human interactions Learning

7. Key features in Customer Service platforms Key features Availability The correctness of the answer Dynamic demand of requests Cost Knowledge management Multi languages

8. AI Agents vs Human agents in Customer Service platforms Human Agents Domain knowledge is different to each agent Hard to share domain knowledge Hard to scale up for high demands and scale down for low demands Human cost Company cost (Offices, hardware) Simple tickets needs attention same as others Hard to manage working time Hard to support multi languages AI Agents Up-to-date domain knowledge Reusable domain knowledge Easy to scale up and down Learn and adjust solutions based on response of users Pay as you use (On demand cost) Simple questions can be answered easily 24/7 available Support multi languages Need human agents for complex problems

9. Case Study 1: AI in Customer Service Motel Rocks Previous strategy Using Zendesk to communicate with customers AI solution Using Zendesk Advanced AI instead of Zendesk Only focus on complex queries Result 43% of tickets deflected by AI agents 50% reduction in ticket volume due to self-service 9.44% increase in customer satisfaction Telstra Previous strategy Customers were asking questions in different context To answer questions, agents should take a look on the previous data of customers and find solution through a massive repository AI solution Used Microsoft Azure OpenAI service Gather all information agent needs to find the best answer and recommend some answers Result 20% less follow-up on calls 84% of agents said it positively impacted customer interactions 90% of agents are more effective

10. Case Study 2: AI in Supply Chain Management SAP Propose AI-Driven Supply-Chain Innovations to Transform Manufacturing Utilizing real-time data for better decision-making across the supply chain Key improvements Optimizing decisions with AI-driven insights Streamlining product development Detecting equipment anomalies by sensors and smart devices Result 10% decrease in overall supply chain planning costs 10% reduction in inventory carrying costs and stock turnover rate 15% increase in supply chain workforce productivity

11. Case Study 3: AI in Finance RAZE Banking Concern traditional risk management methods were failing to keep up with the ever-changing world of cyber threats, compliance issues and operational risk Solution Working with RTS Labs, which develops AI solutions, to build a better risk-mitigation strategy Result 45% reduction in fraudulent transactions 20% improvement in regulatory compliance efficiency

12. Future Trends Increased Autonomy AI agents taking on more complex, decision-making roles Integration with Emerging Tech AI agents combining with IoT and blockchain Personalization AI agents offering more customized experiences to users Scalability Wider adoption across various industries.

13. Challenges and Solutions Challenges Trust and transparency issues Ethical concerns Integration with legacy systems Data security and privacy Solutions Developing explainable AI Implementing robust AI governance Continuous monitoring and updating AI systems

14. References https://botpress.com/blog/real-world-applications-of-ai-agents https://yellow.ai/blog/ai-agents/ https://www.vktr.com/ai-disruption/5-ai-case-studies-in-customer-service-and-support/ https://www.vktr.com/the-wire/sap-unveils-ai-driven-supply-chain-innovations-to-transform-manufacturing/ https://www.vktr.com/ai-disruption/5-ai-case-studies-in-risk-management/ https://smythos.com/ai-agents/ethics/ https://deepmind.google/discover/blog/the-ethics-of-advanced-ai-assistants/

15. Q&A