1. What is Agentic AI, and What is it’s Framework?
2. Why are Agentic AI Frameworks Important?
3. What are the Key Components of AI Agents?
4. Top Agentic AI Frameworks You Should Know in 2025
5. Why Partner with Tx?
6. Summary

From a single AI monitoring and flagging scams for banks to a multi-agent system supporting inventory optimization and management, agentic AI is proving to be a game-changer for businesses. In the last couple of years, the world has witnessed rapid advancements in the AI domain. Whether it’s generative AI, contextual chatbots, or robots learning through observation, AI is advancing rapidly and impacting our daily lives. Agentic AI is the recent buzz with immense potential to empower systems with independent decision-making abilities as they learn from collaboration and experience.  

As we are already in 2025, enterprises need frameworks to help them streamline AI workflows and complex decision-making processes. But before we talk about the top Agentic AI frameworks in 2025, let’s understand what they are and why they are essential. 

What is Agentic AI, and What is Its Framework? 

Agentic AI, or agent-based AI, is a set of intelligent agents that can make independent decisions and take actions to achieve specific goals. It allows machines to analyze, learn, adapt, and respond to queries/inputs based on the objective and environment. This level of autonomy is not possible in traditional AI, which solves isolated tasks following fixed rules. For example, autonomous vehicles use sensor data to drive across the streets through traffic without programming for every scenario.  

 An agentic AI framework transforms how users build intelligent systems to cater to real-world applications.  It is a model that designs autonomous AI systems to make decisions and act independently without or with less human involvement. These machines learn from experience and adapt to the situations with minimal supervision. Agentic AI frameworks will transform industries by enabling independent and more intelligent machines to handle most tasks. 

Why Are Agentic AI Frameworks Important?

Agentic AI is highly scalable and autonomous compared to traditional AI systems. However, an agentic framework is necessary to deploy AI agents when needed. In the AI field, AI agent frameworks are necessary for several reasons: 

• Pre-built components streamline AI agent development, reducing time and effort.  

• Consistent methodologies enhance collaboration and knowledge sharing.  

• Supports everything from single-agent apps to complex multi-agent systems.  

• Simplifies AI development, making advanced techniques more approachable.  

• Automates basic tasks of AI agent development, enabling focus on new tech innovations.   

Without an agentic framework, businesses would need to design, program, and build agents from scratch and monitor their performance individually. This would consume a significant amount of resources and time. An agentic AI framework offers pre-built templates for the AI agents to save time and effort. For example, if an enterprise needs customer support AI agents, an agentic framework will simultaneously facilitate the quick deployment of high-quality agents. 

What are the Key components of AI agents? 

Agentic AI, AI Agents, or autonomous agents are programs capable of performing tasks autonomously without human help. These agents can construct a detailed plan to complete a complex task. The function calling step helps them connect with external tools like APIs, web searches, data sources, and other AI agents. These autonomous agents can learn from received feedback and store the data in their memory for future reference.   

Enterprises use various programming languages, such as JavaScript, Python, etc., to build AI agents. However, the quicker way is to use Agentic AI frameworks, which serve as platforms for developing, releasing, and managing AI agents.

Key Components of AI agent frameworks include:

• An agent architecture for defining the AI agent’s internal processes, such as decision-making, memory units, and engagement capabilities. 

• Tools for connecting AI agents with their respective operating environments (real-world or simulated). 

• Task management systems are used to manage the task completion status of agents. 

• Communication protocols for facilitating engagement between agents and between humans and agents. 

• ML algorithms for enabling learning mechanisms for agents so they can improve their performance. 

• Integration tools that connect agents with APIs, external data sources, and more. 

• Monitoring and debugging protocols to track agents’ performance, issues, and behavior. 

Top Agentic AI Frameworks You Should Know in 2025 

CrewAI 

CrewAI is a lean Python framework that empowers organizations with precise low-level control and high-level simplicity for developing autonomous AI agents. It is also open source with role-based architecture that treats agentic AI as a “crew” of “workers.” Here are its key features: 

• Facilitate human-like collaboration between agents 

• Build systems that can handle dynamic and team-based activities 

• Built-in delegation and task-mapping features 

• Industry-specific and customizable modules for AI agent development 

• Supports connections with various LLMs like Google Gemini, Claude, and OpenAI GPT models. 

• Can be accessed on GitHub 

Microsoft AutoGen 

AutoGen specializes in orchestrating multiple AI agents and helps build autonomous, event-driven systems by solving complex issues in a distributed environment. It offers support for developing systems requiring multi-agent engagement, which makes it an ideal tool for cloud automation and IT infrastructure management. Microsoft AutoGen is an open-source tool with features such as: 

• Support for LLMs and conventional APIs 

• Easy to integrate with external services and tools 

• Multi-agent conversation framework with flexible flow control 

• Integrated task recovery and error-handling mechanisms 

• Agent roles and behaviors customization 

• Advanced task prioritization and reasoning 

• AutoGeen Bench tool for assessing and benchmarking AI agent’s performance 

• AutoGen Studio for a no-code interface to create agents 

Smolagents 

Smolagents is an open-source Agentic AI framework that equips organizations with a toolkit for developing intelligent multi-agent systems. It focuses on collaboration, flexibility, and modularity to enable developers to create sophisticated AI systems that can work in collaboration and independently. Its key features include: 

• Advanced management systems to maintain context state across interactions 

• Dynamic workflow orchestration for task management 

• Modular multi-agent architecture 

• Facilitate easy integration with multiple language models and APIs 

• Facilitate inter-agent transmission with robust communication protocols 

• Support autonomous and human-supervised workflows 

AutoGPT 

AutoGPT is a GPT-4-based language model that executes objective-defined activities via prompt input. This model rapidly advances the autonomous AI agents wave by improving decision-making and integrating sophisticated functionalities. Its key features include: 

• Enables seamless interaction between multiple agents to solve complex problems 

• AI agents plan, prioritize, and execute tasks independently 

• Stores previous interactions to improve future decision-making 

• Adjusts actions based on real-time feedback and changing goals 

• Seamlessly connects with external APIs, databases, and software for enhanced automation 

• Understands and generates human-like text for better interactions 

• Improves performance over time through continuous learning 

LangChain 

LangChain is another open-source framework that builds LLM-powered applications like AI agents and chatbots. Its modular architecture represents abstractions of complex concepts and steps needed to work with LLMs. Developers can chain tools, prompts, and memory into coherent pipelines, allowing them to build complex LLM-powered apps quickly. Its key features include: 

• Supports interactions between multiple AI agents to tackle complex workflows 

• Connects with APIs, databases, web search, and other external tools for enhanced functionality 

• Uses advanced techniques like self-reflection and step-by-step reasoning for better decision-making 

• Enhances responses by integrating real-time data retrieval from knowledge bases 

• Allows developers to fine-tune and extend capabilities based on specific business needs 

• AI agents analyze tasks, generate solutions, and execute them with minimal supervision 

• Retains and recalls past interactions to improve contextual understanding and response accuracy 

Why Should You Partner with Tx? 

At Tx, we understand the significance of agentic AI in the business environment. Our TxLabs.ai is at the core of our innovation efforts, where we combine cutting-edge AI capabilities with automation expertise to deliver robust agentic AI solutions. We empower your business by achieving operational excellence, reducing costs and inefficiencies, and scaling AI agents intelligently to keep you in the lead in a fast-evolving market. We leverage agentic AI frameworks like CrewAI to assist you with building tailored AI agents that can sync with your business strategy. 

Our AI-driven approach enables autonomous test execution, generating cases and identifying gaps with minimal supervision. We use predictive analytics to detect defects and risks, ensuring high product quality. AI optimizes workflows, reducing operational overhead and accelerating time-to-market. Additionally, self-healing mechanisms refine test suites by eliminating redundant cases and focusing on critical areas. This ensures reliable QA cycles, improved performance, and seamless process optimization. 

Summary 

Agentic AI is transforming businesses by enabling autonomous decision-making and optimizing workflows. In 2025, enterprises need robust Agentic AI frameworks to streamline AI development and enhance efficiency. Leading frameworks like Microsoft AutoGen, Smolagents, CrewAI, AutoGPT, and LangChain empower AI agents to automate tasks, integrate with APIs, and improve decision-making. Tx’s AI-driven approach ensures cost reduction, operational excellence, and scalability, keeping businesses ahead in a rapidly evolving market. To know how partnering with Tx will help you harness the full potential of Agentic AI, contact our ai powered testing experts now.

The post Top Agentic AI Frameworks You Need in 2025 first appeared on TestingXperts.

1. Understanding Artificial Intelligence in Salesforce Testing

2. What are the Benefits of Using Artificial Intelligence

3. Use Cases of AI in Salesforce Testing

4. Challenges & How to Overcome Them Using AI

5. Best Practices for Implementing Artificial Intelligence in Salesforce Testing

6. Conclusion

7. Why Partner with TestingXperts for Salesforce Testing Services?

Salesforce, a leading customer relationship management (CRM) platform, is widely adopted by organizations to manage their sales, marketing, and customer service operations. As Salesforce implementations become more complex and integrated, ensuring the reliability and functionality of these systems through effective testing becomes crucial.

Salesforce testing is critical to ensuring the functionality and reliability of Salesforce implementations. However, traditional testing approaches often need help with time, effort, and accuracy. That is where Artificial Intelligence comes into play. 

AI offers a powerful solution to overcome the limitations of traditional testing approaches in Salesforce testing. AI techniques can significantly enhance the testing process, improving efficiency, accuracy, and effectiveness. 

Understanding Artificial Intelligence in Salesforce Testing

AI in Salesforce testing involves leveraging machine intelligence and automation techniques to improve testing efficiency, accuracy, and effectiveness. It includes various AI techniques and algorithms to analyze data, identify patterns, and make intelligent decisions. 

AI techniques used in Salesforce testing are as follows:

Machine Learning Algorithms 

Machine learning algorithms enable systems to learn patterns and make predictions based on historical data. In Salesforce testing, these algorithms can be trained to analyze large datasets, identify patterns, and predict potential issues. This enables proactive defect detection, reduces manual effort, and improves testing. 

Natural Language Processing (NLP) 

NLP techniques empower systems to understand and interpret human language. In Salesforce testing, NLP can analyze test requirements, user stories, and documentation. It helps automate test case generation, enables adequate test coverage, and enhances the overall efficiency of the testing process. 

Intelligent Test Automation 

Intelligent test automation combines AI and automation to enhance test case creation, execution, and maintenance. AI-powered automation frameworks can learn from user interactions, optimize test execution paths, and provide intelligent suggestions for creating robust test scenarios. This accelerates test execution and reduces testing cycles, enabling faster time-to-market. 

What are the Benefits of Using Artificial Intelligence of Using Artificial Intelligence

AI is the simulation of human intelligence in machines capable of performing tasks that typically require human intelligence. In Salesforce testing, AI can automate and enhance various testing processes, improving efficiency and accuracy. 

Here’s why AI is necessary for Salesforce testing: 

Increased Test Coverage:

Salesforce implementations typically involve complex business processes, integrations, and customization. Generating comprehensive test cases to cover all scenarios can be time-consuming and prone to human errors. AI techniques like machine learning algorithms can automatically analyze historical data, identify patterns, and generate test cases, ensuring higher test coverage. 

Faster Test Execution:

Manual test execution is often time-consuming and resource intensive. AI-powered automation frameworks can execute tests significantly faster, accelerating the testing cycle. Organizations can quickly bring Salesforce implementations to market by reducing testing time and improving competitiveness and customer satisfaction. 

Intelligent Bug Detection:

Identifying and resolving bugs is critical to Salesforce testing. AI techniques enable intelligent bug detection by analyzing testing data, identifying patterns, and detecting anomalies that might indicate potential issues. This proactive approach allows testers to identify and address bugs early in the development cycle, reducing the impact on end-users and minimizing costly rework. 

Predictive Insights:

AI models can leverage historical testing data to provide predictive insights into system behavior and performance. By analyzing patterns and trends, AI can forecast potential issues or performance bottlenecks, allowing organizations to take preventive measures and optimize their Salesforce implementations. This proactive approach improves the overall stability and reliability of the system. 

Enhanced User Experience:

Salesforce implementations aim to provide a seamless and intuitive user experience. AI in testing can simulate user interactions, identify usability issues, and provide valuable insights for improving the user interface. Organizations can enhance the user experience and increase customer satisfaction by addressing usability concerns early in testing. 

Scalability and Efficiency:

Manual testing approaches become increasingly challenging to manage as Salesforce implementations grow in complexity and scale. AI techniques, such as intelligent test automation, can handle scalability requirements autonomously and generate and execute test cases. This automation saves time and effort and ensures consistent and repeatable testing processes. 

Use Cases of AI in Salesforce Testing

The application of Artificial Intelligence in Salesforce testing has revolutionized how organizations validate the functionality and reliability of Salesforce applications. AI techniques offer various solutions to the challenges of testing complex Salesforce implementations. From AI-powered test automation frameworks to intelligent test data generation and predictive analytics, the following use cases demonstrate the transformative potential of AI in Salesforce testing. By understanding and leveraging these use cases, organizations can unlock the full power of AI to ensure the quality and success of their Salesforce applications. 

AI-powered Test Automation Frameworks 

AI can be leveraged to develop intelligent test automation frameworks to learn, adapt, and optimize test scenarios. These frameworks use machine learning algorithms to analyze test data and user interactions, enabling them to provide suggestions for creating robust test cases. They can also optimize test execution paths, prioritize high-risk areas, and identify redundant test cases. By automating repetitive tasks and optimizing test coverage, AI-powered test automation frameworks improve testing efficiency and accelerate the delivery of Salesforce applications. 

AI-driven Intelligent Test Data Generation 

Generating realistic and diverse test data for Salesforce applications is critical to testing. AI techniques can analyze the structure and patterns within the Salesforce data model to generate intelligent test data. By understanding the relationships between different data elements, AI algorithms can create representative test data sets that cover various scenarios and edge cases. This ensures comprehensive test coverage and helps uncover hidden defects that may not be apparent with limited or synthetic test data. 

AI-based Predictive Analytics for Salesforce Testing 

AI-powered predictive analytics can be applied to Salesforce testing to extract valuable insights from test data. AI algorithms can predict potential performance bottlenecks, security vulnerabilities, and system failures by analyzing patterns, trends, and historical data. These predictive insights enable proactive measures to address risks, optimize testing strategies, and make data-driven decisions. AI-based analytics also assist in identifying the root causes of defects, improving the efficiency of testing processes, and enhancing the overall quality of Salesforce applications. 

Challenges & How to Overcome Them Using AI

Salesforce testing poses unique challenges due to the complexity and customization options offered by the platform. Some common challenges include handling large volumes of test data, ensuring seamless integration with external systems, managing frequent updates and releases, and addressing compatibility issues. The introduction of AI in Salesforce testing can help overcome these challenges and streamline the testing process. 

Following are how organizations can overcome challenges associated with Salesforce testing with the help of AI models: 

Data Quality and Availability for Training AI Models 

The success of AI in Salesforce testing depends on the quality and availability of training data. Organizations must have clean, representative, and diverse data for training AI models and must consider data privacy and compliance to maintain the integrity and security of customer data. 

Ethics and Fairness in AI-based Testing 

When implementing AI in Salesforce testing, ethical considerations become crucial. AI algorithms should be trained and tested with fairness in mind to avoid bias and discrimination. Organizations should establish guidelines and policies to ensure responsible AI usage and mitigate unintended negative consequences. 

Adoption and Integration Challenges 

Integrating AI into existing testing processes requires careful planning and implementation. Organizations must assess their AI readiness, identify suitable use cases, and collaborate with AI and Salesforce experts. Clear communication and training are essential to ensure AI’s successful adoption and integration in Salesforce testing. The best option is to partner with Salesforce testing experts who utilize AI practices. 

Best Practices for Implementing Artificial Intelligence in Salesforce Testing

Implementing Artificial Intelligence in Salesforce testing requires careful planning and execution to maximize its benefits. By following the below-mentioned practices, organizations can successfully integrate AI into their testing processes, enhance efficiency, and improve the overall quality of their Salesforce applications. These best practices will guide organizations towards effectively utilizing AI capabilities and integrating a successful AI-enabled Salesforce testing strategy: 

Assessing AI Readiness and Determining the Correct Use Cases 

Organizations should assess their readiness for AI adoption by evaluating their existing testing processes, data availability, and infrastructure. They should identify the areas where AI can bring the most significant value, such as test automation, test data generation, or predictive analytics. Starting with small pilot projects and gradually scaling up can help mitigate risks and ensure successful implementation. 

Collaborating with AI and Salesforce Experts 

Collaboration between AI experts and Salesforce testing experts is crucial for successful implementation. AI experts can provide insights into AI techniques, algorithms, and tools, while Salesforce experts can contribute their domain knowledge and understanding of the platform. This collaboration ensures that AI is applied effectively to address specific Salesforce testing challenges. 

Integrating AI into Existing Testing Processes 

AI should be integrated seamlessly into existing testing processes and tools to maximize its benefits. This integration may involve developing custom AI models, integrating AI-driven tools, or leveraging AI capabilities provided by Salesforce. Organizations should ensure that the AI integration does not disrupt existing workflows and aligns with their overall testing strategy. 

Continuous Learning and Improvement 

AI models and algorithms should be continuously monitored, evaluated, and improved to ensure their effectiveness and accuracy. Organizations should invest in ongoing training and upskilling of their testing teams to understand AI techniques, interpret AI-driven insights, and optimize testing processes. Continuous learning and improvement are crucial to harnessing the full potential of AI in Salesforce testing. 

Conclusion

AI is necessary for Salesforce testing to overcome the limitations of traditional testing approaches. By leveraging AI techniques, organizations can achieve increased test coverage, faster test execution, intelligent bug detection, predictive insights, and enhanced user experiences. Organizations should carefully assess AI readiness, collaborate with experts, integrate AI into existing processes, and prioritize continuous learning and improvement. By following best practices and collaborating with AI and Salesforce experts, organizations can leverage the power of AI and shape the future of Salesforce testing. 

Why Partner with TestingXperts for Salesforce Testing Services?

Partnering with a reliable, experienced provider like TestingXperts regarding Salesforce testing services can offer numerous advantages. With expertise in AI-driven testing, TestingXperts can effectively help organizations harness AI’s power in Salesforce testing. 

Why Partner with Us? 

Deep Expertise in AI and Salesforce Testing 

Our team of skilled professionals combines extensive knowledge of AI techniques, algorithms, and tools with a profound understanding of the Salesforce platform. This unique combination allows us to develop tailored solutions that leverage AI to enhance your Salesforce testing services. 

AI-Driven Test Automation Frameworks 

By leveraging machine learning algorithms and intelligent automation techniques, we create frameworks that can learn from existing test cases, adapt to changes in the Salesforce application, and provide self-healing capabilities. This results in faster test execution, reduced testing cycles, and increased test coverage, enabling you to accelerate your release cycles without compromising quality. 

Intelligent Test Data Generation 

Our AI-based intelligent test data generation techniques go beyond traditional methods, enabling diverse and realistic test data creation. By analyzing existing data and understanding the underlying data model, we generate test data that covers a wide range of scenarios, including edge cases and complex designs. This ensures thorough testing and helps uncover hidden issues, enhancing the reliability of your Salesforce applications. 

Predictive Analytics for Proactive Testing 

AI-based predictive analytics techniques analyze historical test data to identify patterns, trends, and potential risks. It allows us to predict the impact of system changes, identify high-risk areas, and optimize test coverage based on the likelihood of failure. By leveraging predictive analytics, we help businesses allocate testing resources effectively and focus on critical areas, reducing the probability of issues impacting your Salesforce applications in production.

Partner with us to transform your Salesforce testing efforts and deliver exceptional experiences to your customers. Contact us today to explore how we can upscale your Salesforce testing journey. 

The post Importance of Artificial Intelligence in Salesforce Testing first appeared on TestingXperts.

The rapidly evolving digital world is striving to keep up with the increasing demands of the customers. Most consumers want their product or service provider to be available 24/7 and believe an organization’s customer experience (CX) is as important as its product or service quality. AI Chatbot is an efficient solution to these dynamic needs and expectations as these chatbots replace live chat and other forms of contact, such as emails and phone calls.

Contents:

1. An overview of AI chatbot
2. How Does AI Chatbots Work?
3. An Overview of how AI chatbot enhances the CX across industries
4. Future Trends of AI and Chatbots
5. What are the Leading AI Chatbots?
6. Conclusion
7. How can TestingXperts (Tx) help?

A chatbot simulates human conversation through text or voice interactions. Organizations in both business-to-consumer (B2C) and business-to-business (B2B) environments deploy chatbots to handle customer-centric tasks. Adding chatbot assistants reduces overhead costs and enables organizations to provide customer service during hours when live agents aren’t available.

An overview of AI chatbot

AI-based chatbots are trained to have human-like conversations using a process known as natural language processing (NLP). With NLP, the AI chatbot can interpret human language as it is written, enabling it to operate more or less independently.

AI chatbots can understand language outside pre-programmed commands and respond based on the existing database. Customers can lead the conversation by voicing their intent in their own words. AI chatbots are programmed to learn from their conversations. Over time, chatbots adapt to user behavior and optimize their responses to different patterns and situations. Chatbots analyze how a customer feels and make predictions about the customer’s needs. AI chatbots’ ability to adapt and provide customized solutions is leveraged across industries to streamline business processes and enhance the overall customer experience.

How Does AI Chatbots Work?

Chatbots work on 2 major principles, one is adherence to a series of predefined rules, and another is using AI to interpret queries and perform the necessary action.

Rule-based chatbot:

These chatbots are programmed with a limited range of instructions and the conversation depends solely on the predefined rules. Compared to AI chatbots, Rule-based chatbots are easier to program as they are based on a true-false algorithm that enables the bots to understand the user queries and provide the relevant answers.

AI-based chatbot:

AI chatbots are driven by an artificial brain or AI. AI-based chatbots are trained using ML to understand diverse user queries. AI chatbots continuously learn from user interactions and maintain a constant learning curve. AI chatbots can detect user intent, language, and provide recommendations to the users.

An Overview of how AI chatbot enhances the CX across industries

AI Chatbot in Banking

• Chatbots enhance the CX for bank customers by managing requests swiftly and efficiently

• Acts as listening channels for banks to better understand user habits, measure customer actions, and provide personalized offers and services

• Helps to increase bank’s brand value and customer loyalty by ensuring 24x7x365 customer query resolutions

• Through iterative learning from customer engagements, these chatbots enhance CX by helping increase the number of ways customers can ask questions

• According to Juniper Research, chatbots are expected to save banks $7.3 billion per year by 2023, equivalent to more than $862 million man-hours thus improving the customer CX

• Bank of America and Citibank are some of the few market-leading banks that have released voice and text-capable chatbots

AI Chatbot in Fintech

• Chatbots in FinTech assist customers in resolving their requests dynamically

• Modern chatbots enable customers to connect with digital wallets, bank accounts, and more

• Engages with customers over emails, social media platforms, text messages, etc.

• Significantly improve CX by simplifying the online payment process by helping customers find and make payments via their preferred methods

• Gathers sensitive customer data, which is analyzed to understand the customer’s requirements and expectations to enable a great CX

• Fintech businesses can ensure data compliance with chatbots by recording customers’ consent to store data and retrieving the same when required

AI Chatbot in Healthcare

Automated reminders help doctors to free up staff as per the schedule and ensure a reduction in no-show appointments too. Chatbots are another kind of automation that improve the CX or patient experience by answering patients’ questions, providing them with additional information, and booking appointments.

Patient outcomes- related benefits

• Conversational AI chatbots serve as the primary point of contact in the healthcare industry

• Helps with patients’ medicine prescriptions by giving the number of times they need to take it each day and how to use it

• New chatbots also send reminders via Facebook Messenger for patients to take their medicines

• Bots help elderly patients by sending notifications on their regular health checkups and diagnostics needed as per their monthly health evaluations

• Serves as a preliminary diagnostic tool by analyzing patient symptoms through a series of questions and offering health-related recommendations

• Helps to educate patients with doctor-approved resources

• Helps improve digital patient CX

Employee-related benefits

• Medical staff continues to use chatbots for record access

• Helps to Identify areas of staff working schedules that need improvement

• Monitors patient-reported outcomes for the staff

• Improves staff training

AI Chatbot in eCommerce

• Chatbots enable retailers to foster better CX

• AI-based chatbots learn and adapt to complex business queries and offer quick resolutions

• Provides 24X7 support while eliminating the cost of a full-time customer support staff

• Collects visitor data to provide better product recommendations and improve CX

• Understands customer inquiries to personalize product and services pages

• Helps customers find the exact product from a huge catalog and directly move to the checkout page

• Notifies customers when items get out of stock and provide relevant alternates based on their preferences

• Informs customers about their expected product delivery date and time

• Reduces human error and deploys efficient customer service with minimal costs

• Reminds users of their abandoned shopping cart and asks them to proceed towards checkout or clear the cart

• H&M’s chatbot provides great CX by asking customers about their style and offering products accordingly

• eBay’s chatbot ‘ShopBot’ guides customers through the products, asks questions, and gives recommendations like a real sales associate

AI Chatbot in Insurance

• Conversational AI chatbots can offer an excellent CX during onboarding, renewal, and claims

• FAQ bots can streamline onboarding by providing specific information to policy seekers

• Automate the renewal process by guiding policyholders in easy steps

• Help with standard transactions like account management and capturing customer data

• Promote personalized financial products at the right time

• Provide 24/7 technical support

• Collect customer feedback and serve as a virtual agent

AI Chatbot in Travel

• Provide instant response 24/7 across different communication channels

• Recognizes users’ intent to deliver the right response

• Provides updated information about travel prices, destinations, packages, and comparisons

• Helps inquire about critical reservation data such as ID or passport data, choosing flight seats, changing check-in dates, refunds, policies, etc.

• Generate cross-selling opportunities by offering flight + hotel packages, car rental options, discounts on activities, etc.

• Perform reservation cancellations without handovers to live agents

• Manage luggage inquiries and generate luggage claim requests in case of lost baggage.

• Expedia offers a Facebook messenger chatbot to enable users to browse hotels around the world and check availabilities during specific periods

• WestJet’s AI-powered chatbot Juliet offers virtual assistance on chat, WhatsApp, Facebook Messenger, and Google Assistant, resolved 74 percent of customer service tickets without human intervention during the pandemic

Future Trends of AI and Chatbots

Chatbots today are changing the way businesses communicate and understand their customers. With AI, chatbots are able to deliver an optimized and personalized CX while saving companies money through customer service, internal processes, and marketing efforts. According to Juniper Research, chatbots will save $11 billion and over 2.5 billion hours for retail, banking, and healthcare industries solely on customer service inquiries. For all the industries mentioned above, not only will chatbots enable businesses to take customer interactions to the next level, but they will also free up human resources for other business-critical tasks. Here’s a quick look at what the statistics say about the future trends of AI and chatbots:

• According to Cognizant, the market size of Conversational AI will reach USD $1.3 billion by 2025

• Juniper research states that chatbot eCommerce transactions will reach USD $112 billion by 2023

• Gartner predicts that more than 50% of enterprises will invest in chatbot creation

• As per CNBC, chatbots will handle 75-90% of customer queries by 2024

What are the Leading AI Chatbots?
• Alexa for Business
• HubSpot Live Chat
• ProProfs
• Tidio
• Mitsuku – Pandorabot
• Botsify Salesforce Einstein
• WP-Chatbot

Conclusion

Today, customers need on-demand and 24/7 business support. Therefore, businesses across industries are rapidly adopting AI chatbots to deliver quicker and continuous customer support. AI-powered bots understand the user intent and interact with them just like humans.

However, AI being a high-end technology, it is essential to test the chatbots to ensure their reliability and security. Businesses should leverage AI testing from an independent Next-gen QA and software testing service provider for high-quality chatbots.

How can TestingXperts (Tx) help?

TestingXperts (Tx) is a leading next-gen software testing services provider that delivers end-to-end AI chatbot testing services for businesses across domains.

Our AI chatbot testing services overview

• Specialized testing

• Regression

• API

• Conversation flow

• Domain-Specific

• Crow

• Performance and security

• NLP & Cognitive Service

TestingXperts teams leverage an in-house framework ‘Tx-Botomate’ to ensure seamless AI Chatbot testing.

Tx-Botomate Features Overview:

• Voice and chat automation

• Integration with Test Management and Bug Tracking Tools

• UX testing

• OS independent

• NLP

• CI Integration

• Insights

Get in touch with our experts to know more.

The post How AI Chatbot Enhances CX in Different Industries? first appeared on TestingXperts.

Today’s businesses leverage technologies like Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), Robotic Process Automation (RPA) for automating their tasks and other business processes. Though these next-gen technologies have helped businesses achieve their goals, as we advance, companies need to upgrade themselves by adopting Intelligent Automation(IA), which typically combines the power of AI, ML, RPA, and other advanced technologies.

Content

1. An overview of Intelligent Automation
2. Differences of Intelligent Automation with RPA and Hyperautomation
3. Why should businesses leverage Intelligent Automation?
4. How Intelligent Automation is transforming the BFSI industry?
5. An overview of Intelligent Automation Testing types
6. Conclusion

An overview of Intelligent Automation

Intelligent Automation, also known as cognitive automation combines advanced technologies like: – RPA, – BPM, – AI, – ML, – Analytics, – OCR, – ICR, – Process mining to create end-to-end business processes that think, learn, and adapt on their own. Moreover, this intelligent automation accelerates digital transformation, automates repetitive tasks, ensures freeing up resources, improves operational efficiency, and effectively simplifies various business processes.

Differences of Intelligent Automation with RPA and Hyperautomation

Why should businesses leverage Intelligent Automation?

Streamlines back-office processes and increases efficiency:

Intelligent automation can speed up business processes and streamline tasks per business needs, ultimately increasing process efficiency.

Improves customer service:

This technology helps businesses get real-time access to customer information, accelerate the resolution of customer requests, and help identify additional product/service needs.

Frees-up employees for higher-value work:

Intelligent automation can effectively handle routine and repetitive business tasks without any manual intervention. Thus, it can free up employees from mundane tasks and help them focus on high-priority tasks.

Improves governance and fraud detection:

With the help of intelligent automation, fraudulent or suspicious activities can be identified early in banks and financial organizations. Leveraging this technology helps businesses gain more security and safety of their systems against fraudulent activities.

Interprets big data:

Data acts as a fuel for every business, and its correct interpretation is necessary for business leaders to make informed decisions. However, interpreting big data manually is a challenging task. Intelligent automation can effectively handle and interpret a large amount of data in no time, which would take hours for humans to manage.

Helps in decision-making:

This technology utilizes AI and ML technologies that enable machines to learn, think, and act just like humans. Thus, it augments human decision-making capabilities and helps humans make better-informed decisions.

Reduces cost & saves time:

Another reason for C-suite members to consider intelligent automation is that it helps in reducing costs and saves a lot of time. As most repetitive business processes are automated, it also saves costs significantly. Since most of the repetitive business tasks are automated, employees can focus on other higher-value tasks, which saves high costs for businesses.

How Intelligent Automation is transforming the BFSI industry?

Intelligent automation is now leveraged across industries and impacts the BFSI sector significantly. Since the BFSI sector plays a vital role in the economy, there is constant pressure on BFSI institutions to improve business performance and lower operational costs. Due to the complexities of data, increased regulatory scrutiny, back-office inefficiencies, and outdated legacy systems, the BFSI sector faces many challenges. The BFSI sector needs to leverage intelligent automation testing to overcome these challenges. This technology helps BFSI institutions reduce costs, boost overall business performance, and deliver a great experience to employees and customers.

1. Banking Sector:

• Frees up banking team for higher-value work: Employees in banks have to regularly deal with repetitive and time-consuming tasks such as entering customer details, KYC process, etc. But, with the help of IA, various tasks can be automated and completed with much accuracy and at a faster pace. This frees up employees and allows them to focus on other high-value tasks.

• Allows customers to open a bank account remotely: Gone are the days when customers need to visit the bank to open bank accounts. But, now, with IA in place, customers can open their accounts remotely. Customers need to complete online KYC, submit required documents, fill in the necessary details and open their accounts in no time.

• Eases general ledger: Banking employees have to regularly maintain and update the ledger daily, a time-consuming and critical task. With the help of IA, banks can collect, update and validate large amounts of information from multiple sources faster and more effectively.

2. Finance Sector:

• Eases the application validation process: Employees working in the finance department have to manually validate applications that consume a lot of time and effort. But with IA technologies such as OCR, NLP, Intelligent Document Processing (IDP), etc., documents can be automatically processed easily in no time.

• Helps in detecting fraudulent activities: IA solutions can continuously monitor real-time transactions and can analyze the patterns of these transactions that help in detecting fraudulent activities such as anti-money laundering, frequent deposits of large amounts, frequent withdrawals, etc. It can also alert the concerned party and help prevent financial fraud in Fintech businesses.

• Eases financial audits: Finance audits are very complex and involve evaluating financial statements, tax calculations, checking amounts payable, and more. With IA, these tasks can be effectively automated, and chances of human-prone errors can be removed.

3. Insurance Sector:

• Makes credit risk assessment accurate and reliable: IA in the insurance sector plays an important role. This technology can intelligently screen prospective borrowers and assign them a risk score based on their ability to repay loan amounts. This process further helps various institutions to make an informed decision regarding credit/loan approval/rejection.

• Ensures faster claim processing: Claim processing is a complex and time-consuming task. Typically, insurance agents need several days to gather data and process claims. But with the help of IA, claim processing and even settlements can be done faster and more effectively without any risk or manual errors.

• Provides intelligent insights: IA tracks and records customer journey and makes insightful recommendations to suggest policies suitable to customer needs based on these recommendations. This further helps customers to manage their insurance money better and ultimately enhances the customer experience (CX)

Other industries where Intelligent Automation is making an impact

1. eCommerce:

• IA utilizes the power of AI technology that records the customer data and their shopping journey and makes recommendations each time customers visit the site for shopping.

• It helps businesses to make decisions about procurement by predicting demand and supply.

• It also enables seamless checkout with their shopping carts while smart algorithms calculate the amount to be charged and ensure seamless transactions to users.

2. Manufacturing:

• Physical and digital robots driven by IA are used to perform various tasks and replace/supplement humans in the manufacturing sector.

• Digital bots help manufacturers process a large amount of data and streamline the ordering process, procurement, appointment scheduling, and alerting.

• It also enables predictive analytics, utilizes machine vision technology, and enables quality inspection that is much more reliable and detailed than humans.

3. Logistics and Transportation

• IA helps truck drivers find the fastest and most cost-efficient routes by utilizing the power of analytics and predictive insights.

• IA has made the concept of self-driving cars into reality, and even drone delivery has been made possible due to the AI technology behind IA.

4. Healthcare

• The healthcare industry is rapidly using AI and RPA technology that goes behind IA to provide better patient outcomes and services.

• AI-powered chatbots are used to make appointments with doctors and provide online consultations.

• Intelligent bots are used to perform surgeries on the head, neck, eye, etc.

• Advanced AI technologies are being leveraged to enable accurate analysis of medical images, such as CT scans, MRIs, and X-rays, which significantly reduce the chances of human error.

5. Telecom

• The Telecom sector has always been data-intensive, and IA helps manage large amounts of data effectively.

• Automated call centers powered by advanced technologies reduce wait time for customers, accelerate the customer query resolution process and enhance the CX.

• IA also helps telecom service providers improve the quality of their network operations by automating the testing, addressing, remediation, and maintenance procedures.

• Intelligent Automation plays a major role across industries and AI-powered apps and solutions must be properly tested to ensure greater outcomes for businesses.

An overview of Intelligent Automation Testing types

UiPath Test Suite based Test Automation:

UiPath has come up with its UiPath Test Suite, which has many advanced features to support enterprise test automation. The UiPath test suite consists of Test Manager, Studio Pro, Orchestrator, Robots, and other Test Studio Components. By leveraging UiPath Test Suite based test automation, businesses can achieve up to 30% faster test execution, up to 40% lesser maintenance effort, and up to 70% faster test creation and more

AI Testing:

As businesses continue to adopt AI-powered smart applications, the need for end-to-end AI testing to ensure fully functional and high-performing AI systems is rising. Businesses can ensure proper functionality and effective performance of AI-powered smart apps and bots by leveraging AI testing.

AI-Based testing:

It is an intelligent software testing method in which advanced technologies like AI, ML are used for testing the software. These advanced technologies ensure faster and effective testing outcomes and quality solutions.

RPA Testing:

Today, businesses across industries are leveraging bots to automate repetitive tasks and perform various tasks without human intervention. But, to ensure the proper functioning and reliable performance of these bots, RPA testing is necessary. Bots are tested thoroughly leveraging RPA testing to ensure seamless bot functioning and performance.

Conclusion

Intelligent Automation driven by cognitive and digital capabilities is changing the future of all industries, especially for the banking, financial, and insurance sectors. This technology is one of the significant drivers accelerating business growth and ensures a great customer experience. The combination of AI and RPA’s strength in the form of Intelligent Automation is helping businesses improve the quality, speed, and productivity of processes. While some businesses have already realized its importance, others need to adopt it. If you are an enterprise looking for intelligent automation testing, TestingXperts (Tx) can be your one-stop solution.

Tx is a proud partner of UiPath and has been providing UiPath process automation leveraging various UiPath tools to speed up the testing process. Our teams have hands-on experience with the UiPath test suite and provide next-gen intelligent automation testing services to clients across industry domains. We also have an in-house AI-enabled comprehensive platform – Tx-SmarTest that systematically caters to faster releases enabled with DevOps CI/CD implementation. This platform significantly helps boost the software quality through production, which is enabled by combining AI, ML, Deep Learning, and Neural network algorithms.

The post How Intelligent Automation Is Transforming The BFSI Industry first appeared on TestingXperts.

The rapid advancements in next-gen technologies have significantly transformed the way businesses connect and communicate with customers. Today, businesses leverage advanced technologies like Artificial Intelligence (AI), Machine Learning (ML), Robotic Process Automation (RPA), and Natural Language Processing (NLP) to develop software bots and chatbots. These AI-based conversational bots interact with customers in real-time and enable human-like interactions between systems and humans. For businesses, these AI conversational bots with automation testing continue to be a faster mode of communication with their customers as it delivers a greater customer experience (CX).

Content

1. What is an AI Chatbot?
2. Latest market analyst’s views on AI Chatbots
3. Benefits for Businesses Leveraging AI Chatbots
4. Industry-wise use cases of AI Chatbots
5. What Functionalities should be Tested in an AI Chatbot?
6. 5 Common Challenges of AI Chatbot Testing
7. AI Chatbot Testing Process Overview
8. Major Testing types for Testing AI Chatbots
9. Some of the Leading AI Chatbot Testing Tools
10. How can TestingXperts (Tx) help?

What is an AI Chatbot?

A chatbot is a text or voice-based interface that is built and deployed on the website or apps to simulate a conversation with users and seamlessly support users. These are self-learning bots and are programmed with AI, NLP, and ML technologies. There are many benefits with these bots; they can automate tasks, understand words and phrases, frame appropriate responses, and can learn from the received inputs. Thus, these conversational bots are effectively being used to replace a repetitive task that a human would do. Moreover, AI conversational bots differ from traditional rule-based bots and can understand language outside pre-programmed commands.

Some of the predominant AI conversational bots include Alexa, Siri, Mitsuku – Pandorabot, Botsify, etc. These bots can perform various tasks such as conducting sentiment analysis, predicting consumer likes and dislikes, assisting the customer with the right product/service, etc. As businesses across industries continue to leverage AI conversational bots or agents, their dominance in the market is increasing rapidly in recent times.

Latest market analyst’s views on AI Chatbots

According to Grand View Research, the global chatbot market size was estimated at USD 430.9 million in 2020 and is expected to reach USD 525.7 million in 2021, exhibiting a CAGR of 24.9% during the forecast period of 2021 to 2028.

According to Markets and Markets, the global conversational AI market size is expected to grow from USD 4.8 billion in 2020 to USD 13.9 billion by 2025, at a CAGR of 21.9% during the forecast period 2020-2025.

According to Deloitte Digital Report titled “Adopting the Power of Conversational UX Chatbot,” it is predicted that chatbots will save businesses $8 billion per year by 2022.

According to a Capgemini Research Institute Report with the title “How Organizations and Consumers are Embracing Voice and Chat Assistants,” there were significant facts about chatbots delivered. The report states about 74% of respondents use conversational assistants for researching or buying products and services, 58% of respondents use them to play music, check directions, and 53% use AI chatbots for learning about banking/ insurance products, checking account balance, and for transferring funds.

Benefits for Businesses Leveraging AI Chatbots

1. Delivers immediate responses:

Chatbots can respond quickly as soon as it receives customer queries. It helps businesses meet the ever-rising customer demand for quick query resolution and deliver immediate responses in real-time.

2. Ensures 24/7 availability:

Today, customers want business services to be available 24/7 and need their queries to be solved at the click of a button. AI conversational bots resolve customer queries in real-time 24/7, which ultimately enhances CX.

3. Records valuable data:

Conversational bots record various types of data when it interacts with the users, such as customer likes, dislikes, preferences, and this information is then used by businesses to increase user engagement.

4. Delivers personalization:

AI-based bots are backed by ML and NLP technology, which helps bots learn information explicitly from past interactions. Thus, they provide a more accurate and personalized response to users.

5. Increases customer satisfaction:

AI conversational bots ensure faster delivery of responses and customized or personalized responses, which ultimately increases customer engagement and leads to customer satisfaction.

6. Ensures data Security:

AI chatbot ensures data security as it is end-to-end encrypted, authenticated, and authorized. It follows specific regulatory policies/protocols and ensures no data leak happens when a user interacts with it.

7. Uplifts brand image:

A conversational agent or bot helps businesses deliver accurate and timely responses to customers. This not only increases CX but also uplifts the brand’s image.

8. Reduces costs:

Chatbots help in reducing overall costs by reducing manpower. By leveraging these bots, businesses can automate many business processes, which ultimately leads to reduced costs and enhanced CX.

Industry-wise use cases of AI Chatbots

Healthcare:

• Healthcare service providers are leveraging AI chatbots to provide faster responses to patient queries and assist patients in booking appointments with doctors.

• These bots can provide medical information, collect patient data, manage medical insurance queries, claim processes, etc.

Telecom:

• Telecom companies are leveraging conversational bots to resolve technical issues seamlessly and provide faster responses to service-related queries.

• These bots can also gather user information and can help telecoms to offer personalized plans and services to customers based on their profiles.

BFSI:

• The BFSI sector uses AI-based conversational agents to assist customers in finding the correct product or service, handle customer queries, handle KYC processes, etc.

• These bots can also assist customers in filing claims, submitting payments, and significantly improving the online banking experience for users.

Retail & eCommerce:

• Conversational bots assist the customers in finding the right product, respond to customer queries, offer personalized shopping advice based on pre-recorded customer records, and assist in transactions and more.

Media & Entertainment:

• AI conversational agents are leveraged by media and entertainment companies to gather customer insights, offer personalized and unique content, assist viewers in navigating through the content list, and choose the right content.

• These bots are now being combined with AR and VR technologies to provide a next-level gaming experience to gamers.

Sales and Marketing:

• The sales and marketing sector is leveraging AI-based interactive bots to connect with users, increase brand awareness, pitch services, generate leads, acquire customers, and more.

• These bots also help sales and marketing professionals gain customer insights, offer personalized services, and significantly enhance the CX.

Smart home and IoT:

• AI-based interactive agents or bots help businesses provide a connected smart-home experience for users. Customers can now control and monitor any smart-home connected device or application with the power of text or speech features enabled by these bots.

• The users can also interact with smart vehicles to lock or unlock the car, set car temperature, play music, set routes, etc., by leveraging these interactive agents

• Since these bots are leveraged by almost all industries, they must be tested thoroughly to ensure their high-quality, seamless performance and robust security.

What Functionalities should be Tested in an AI Chatbot?

1. Response time:

Ideally, the AI conversational bot must reply fast as soon as it receives inputs from the user. Thus, it is essential to test the response time taken by the bot to deliver a great CX

2. Answering/Response accuracy:

The accuracy of the responses or answers that conversational assistants provide against inputs/queries must be tested.

3. Error management:

Chatbot’s ability to deal with the errors and its ability to recover from those errors must be tested. If a bot fails to understand user inputs, it must smartly ask alternative questions to the customer or should connect the user with a live agent.

4. Personality:

It is essential to ensure that the chatbot’s voice and tone fit with the users and with the ongoing conversation to deliver a great UX.

5. Navigation:

The navigation flow of the conversational bots should be tested to ensure the customer does not feel lost while speaking with the chatbot.

6. Intelligence:

Chatbot’s intelligence must be tested to ensure it remembers things and provides correct responses based on recorded or stored data.

7. Understanding:

Chatbot should be able to understand all requests, small talk, idioms, emojis sent by the user to frame appropriate responses.

8. Multi/cross-platform and device compatibility:

Chatbots must be able to perform seamlessly on all devices, platforms, and OS versions. Therefore, it is essential to test the multi/cross-platform compatibility of chatbots to ensure it functions properly and delivers a great CX.

5 Common Challenges of AI Chatbot Testing

1. Domain-specific validations:

Typically, chatbots are designed to cater to domain-specific queries from humans. But validating the chatbot’s ability to handle domain-specific queries is a challenging task.

2. Multi-channel user experience:

AI conversational assistants should ideally deliver a great multi-channel UX. But checking the bot’s compatibility with various available platforms and channels is a challenging task.

3. Uncertainty of user conversation:

Ensuring 100% test coverage is a challenging task as there are various uncertainties related to the ways users interact with these bots. Logically it is not possible to test all queries that can be raised by a user.

4. Performance of bot with the increased number of solicitations:

Testing the bot’s performance under various conditions especially with increasing solicitations is a challenging task.

5. Bot security:

The rising number of cyberattacks also poses a great risk to the security of bots. Thus, checking the security of the bot to ensure no compromise to business or customer data is again a challenging task.

Clearly, there are various challenges that testers face while testing an AI conversational bot. The need of an hour for all testers is to adopt an effective AI chatbot testing process.

AI Chatbot Testing Process Overview

1. Identify the use cases:

To start with the testing process, first identify the use cases for the chatbot. It is essential to list all the questions and responses to cover every scenario and prioritize them accordingly.

2. Determine the testable requirement and set KPIs:

Once the use cases are identified, it is essential to see what needs to test based on the identified use cases. Also, set the KPIs such as self-service rate, performance rate, usage rate per login, escalation rate, etc.

3. Understand the architecture and technology stack of the chatbot:

Once all the use cases are identified and KPIs are set, it is essential to understand the underlying architecture and technology used by the chatbot for each use case.

4. Prepare test scenarios to test the functional aspects of the chatbot:

Prepare the test scenarios such that it covers functional aspects of the AI chatbot testing. Test scenarios should cover the conversation and voice testing. It is necessary to prepare scenarios to see how the chatbot responds to the same inputs and handles errors.

5. Test the non-functional aspects of the chatbot:

From the non-functional viewpoint, the various factors to be tested include performance, load handling capacity, security, and accessibility of the chatbot.

Major Testing types for Testing AI Chatbots

1. RPA testing:

It is an advanced software testing technique that allows bots to mimic human actions and automate tasks. By leveraging RPA testing, businesses can effectively test the functional and non-functional aspects of AI chatbots.

2. Security testing:

Data security is a major concern for all enterprises. By leveraging security testing methods, businesses can test the security levels of chatbots. This testing method helps to remove security loopholes or vulnerability issues.

3. User Acceptance Testing (UAT):

User satisfaction matters a lot for businesses today. Therefore, to ensure the bots deliver a seamless CX, businesses must leverage user acceptance testing. By including end-users in the testing process, businesses get real-time feedback from users, which helps to improve product quality and CX.

4. A/B testing:

It is an important software testing technique in which two different versions of the conversational bot are showcased to the user. The versions can vary in their UI, visual factors like color, text size, style, etc. User feedback is then collected to find out which version is more liked by the users. This form of testing ultimately helps businesses to release products that match the user’s expectations.

5. Adhoc testing:

It is an informal, unstructured software testing type performed to see if any loopholes or bugs are present in the chatbot. This software testing type aims to find out any possible defects or errors in the bot performance.

Some of the Leading AI Chatbot Testing Tools

1. Botium:

It is a quality assurance framework for testing and training conversational AI. This testing framework contains two main modules, i.e., Botium Core and Botium Box. Botium Core automates conversations with a virtual assistant whereas, Botium Box makes test automation faster and easier. This tool has an easy-to-use interface and can be used to perform various tasks such as test management, maintenance, and reporting.

2. Botanalytics:

It is a leading conversational analytics tool for bots. It helps businesses improve human-to-bot communication by identifying bottlenecks, segmenting conversations & users, and measuring engagement. This tool helps businesses assess every key aspect of the chatbot conversation.

3. Chatbottest:

It is an open-source guide that helps businesses identify chatbot’s design issues under seven categories, i.e., personality, intelligence, navigation, error-management, onboarding, answering, and understanding. This tool assesses the bot using three levels of testing, which include possible testing scenarios, scenarios that are expected, and impossible scenarios.

4. Dimon:

It is a proprietary solution that enables chatbot testing automation. This testing tool decreases testing time from hours to several minutes. It helps to generate multiple test scenarios, allows effective monitoring and faster test execution.

5. Qbox:

It is an NLP training data optimization platform that offers two options for chatbot testing; the standard test for NLP classifiers and bring your own test set. This tool uses pre-defined utterances and follows a five-step procedure that makes chatbots better.

Conclusion

Today, customers need high-quality products with seamless 24/7 business support. Therefore, businesses are rapidly adopting AI chatbots to deliver faster responses and continuous support to customers. These interactive bots are backed by advanced technologies like AI, ML, RPA, and NLP that help bots understand the user intent and interact with them just like humans. Since high-end technologies back these bots, it is essential to test them to ensure their proper functioning and data security. Businesses should leverage AI testing from an independent QA and software testing service provider for high-quality chatbots.

How can TestingXperts (Tx) help?

TestingXperts (Tx) is a leading and next-gen software testing services provider that delivers end-to-end AI chatbot testing services for businesses across domains.

Our AI chatbot testing services overview

• Specialized testing • Regression • API • Conversation flow • Domain-Specific • Crowd • Performance and security • NLP & Cognitive Service

Our team’s expertise with industry-leading testing tools:

• BOX,

• Test My Bot

• Botium,

• Chatbot Test, etc.

Tx teams leverage in-house framework ‘Tx-Botomate’ to ensure seamless AI Chatbot testing.

Tx-Botomate features Overview:

• Voice and chat automation

• Integration with Test Management and Bug Tracking Tools

• UX testing

• OS independent

• NLP

• CI Integration

• Insights

Get in touch with our experts to know more.

The post AI Chatbot Testing – A Detailed Guide first appeared on TestingXperts.

1. Pain Points of The Insurance Industry in UK
2. What Is The Role of AI in The Insurance Industry in the United Kingdom?
3. Benefits of AI in the UK Insurance Market
4. Some AI applications in UK Insurance Industry
5. Conclusion
6. How Can TestingXperts Help Insurers in UK With Its Intelligent AI And RPA Solution?

Pain Points of The Insurance Industry in UK

Insurtech investments have doubled from 2019 to 2022, and insurers have been focusing on enabling a digitally enhanced platform to deliver a great customer experience (CX). According to statistics, the global market size of AI in the insurance industry is projected to surpass around USD 141.44 billion by 2034, representing a healthy CAGR of 33.06%. However, many insurance companies in the United Kingdom still use traditional data collection methods, which hold their businesses back from accurately understanding customer requirements, assessing the risks involved, and predicting the required and progressive trends.

A Deloitte study stated that while almost all industries have succeeded with AI or started investing in AI, the insurance industry seems to lag behind substantially. Only 1.33% of insurance companies have invested AI in insurance compared to 32% in software and internet technologies. Moreover, many impounding factors continue to impact the CX for insurers in a significant way. Some of the insurance industry pain points have been given below:

Legacy systems:

Most of the insurance systems are based on old technology and need to embrace the latest technologies (AI & ML) to upgrade themselves to deliver a greater CX.

Industry regulations:

Different states across countries change their laws, which needs to be handled by insurers effectively.

Inaccurate claim processing:

As claims are dependent on data unification, their accuracy is critical and needs proper assessment, processing, and management.

Redundant claim processes:

The insurance lifecycle is highly complex and needs seamless management, while redundant & wrong claims affect the insurers business bottom line.

Security of integrated insurance systems:

Insurance systems are well integrated with many third-party apps, and security testing is essential for web services & underlying APIs.

Ever-changing customer expectations:

Today’s insureds want to be more involved and need intuitive and easy-to-use tools and platforms that facilitate quicker & easy communications, payments, and claim settlements. They need quick and easy processing methods to save time and gain an improved CX.

Thus, in the ever-changing Insurance industry arena, to satisfy their customers by delivering a superior CX, the insurers need to embrace and embed AI technologies into their solutions to encash the vast benefits of leveraging this next-gen technology. Forbes, in one of their latest Sept 2022 article named “How AI is changing the Game in Insurance,” has clearly stated that the use of technology, data, artificial intelligence (AI), and modern design has created a powerful combination, changing what was once a very policy-centric industry to one that’s customer-centric.

What Is The Role Of AI in The Insurance Industry in the United Kingdom?

Artificial Intelligence (AI) has been a leading technology to be leveraged across different industries such as healthcare, Banking, FinTech, and eCommerce, along with the Insurance industry. AI has the potential to influence the insurance industry in multiple ways, such as with claims processing, underwriting, fraud detection, and customer service, to name a few. For example, to improve customer experience, many insurers are investing in virtual assistants like chatbots.

The UK’s Financial Conduct Authority (FCA) emphasizes AI’s role in reshaping insurance operations. It highlights potential risks, such as algorithmic bias and data privacy concerns, requiring strict oversight. FCA’s regulations encourage fair AI use, ensure compliance, and safeguard customer interests. Understanding local regulatory frameworks helps insurers navigate risks while facilitating innovation responsibly.

McKinsey, in their article titled “Insurance 2030 -The Impact of AI on the future of Insurance,” states that as AI becomes more deeply integrated into the industry, carriers must position themselves to respond to the changing business landscape. Insurance executives must understand the factors contributing to this change and how AI will reshape claims, distribution, underwriting, and pricing. According to a Deloitte report, over 40% of CIOs plan to increase their spending on AI use cases and pilot projects in 2021.

Benefits of AI in the UK Insurance Market

Saves time and costs:

Insurers can save time and reduce costs by implementing AI into their processes and thus deliver an improved CX.

Accelerates claim processing:

Several ML data models can help insurers determine the claims and rapidly forecast the potential costs. These models can analyze images, sensors, and the insurer’s historical data. Further, the insurer can look at these AI results to verify the claim amount and finally share them with the customers. Thus, leveraging ai-driven insurance industry solutions helps customers get faster claim estimates and quicker resolutions

Fraud detection:

The neural network models help insurers to evaluate fraud patterns and help to identify fraudulent claims quickly and in the prevention of such claims. According to the FBI, non-health insurance fraud in the US is estimated at over $40 billion per year, which can cost families between $400–700 per year in extra premiums.

Chatbots:

AI chatbots are the main modes insurers use to get quick answers to their insured’s questions through emails, chats, and voice messages. Some of the use cases of AI chatbots include getting a personalized quote, signing up a policy, facilitating payments, and managing entire claims through proper resolutions without human intervention, thus enabling a seamless and delightful experience.

Faster and accurate underwriting:

Earlier, the underwriting process was mainly employee-dependent, but AI and ML systems help insurers to enable a better and more seamless underwriting experience with quicker analysis of historical data to enable quicker underwriting processes.

Assess risks more efficiently:

With automatic text processing enabled with ML and NLP models, these help insurers to analyze large amounts of data to assess risks within data.

Improved CX

Today’s insureds need quick and prompt service, and most insurers are including AI tools such as chatbots to help guide their customers through numerous queries, thus enabling a great CX. Moreover, with the help of a chatbot that interacts with customers and collects the required information, which is captured in a structured format, a first-level validation can be carried out during the claim initiation process, thus enabling quicker claim adjudication.

Some AI applications in UK Insurance Industry

Claim management agents and adjusters can help to enhance CX by integrating advanced AI and ML technologies into their claim-processing operations.

Key AI use cases in Health Insurance:

• Helps with quicker disease detection.

• Helps in quicker and more accurate diagnosis.

• AI chatbots guide patients to get effective healthcare based on the diagnosis

• Customers also get personalized health insurance policies

• Billing is also being automated by leveraging AI to streamline operational processes.

Key AI use cases for Auto Insurance

•Supports best predictive analysis for the manufacturing industry.

•Delivers best vehicle maintenance recommendations and also driver performance monitoring by analyzing behavioural data from the connected car systems leveraging AI.

•Many AI-based auto apps deliver good options with cameras and IR sensors to detect driver’s behaviour and alert them with prompt signals and notifications to avoid untimely accidents.

•ML algorithms improve the speed and accuracy of the forecast for individual claims.

Conclusion

In recent years, the insurance industry has expanded in leaps and bounds, and more customers are opting to avail of different types of insurance for health, motor, vehicle, etc. Today’s insureds need faster and quicker methods of taking a policy or getting their claims done quickly. Ai is the next-gen technology that insurers should embrace and embed in their solutions to get faster processes without much human intervention and deliver a greater CX. AI, ML, and NLP models help in fraud detection and speedier claim processing, while AI chatbots continue to help customers solve their queries, thus delivering a greater CX which is the need of the hour.

How Can TestingXperts Help Insurers in UK With Its Intelligent AI And RPA Solution?

TestingXperts developed an Intelligent QA Validation software for one of the largest independent Insurance adjusting firms in the U. TestingXperts automated the claims estimation software input process using Artificial Intelligence (AI). Tx used AI for image recognition and Intelligent Robotic Process Automation (RPA) for validating claims and loss reports generated by the Claims estimation and Validation software

Some of the benefits delivered include:

• Improved Claims accuracy to 99.99%

• Improved Customer Experience by 95%

• Improved Demand management by 85%

• Improved Operational efficiency by 60%

• Reduced customer conflicts by 95%

TestingXperts automated solution solved their three main business challenges:

• Streamlined and automated the claims validation process, which was a human-driven process.

• Improved accuracy of the claim loss reports using Artificial Intelligence.

• Optimized demand management cycle for independent adjusters in peak and non-peak seasons.

TestingXperts Range of Digital Testing Services for the Insurance Sector

Our Differentiators for Insurance digital testing services

•Successfully delivered insurance testing projects to Fortune 2000 clients with 50+ insurance domain experts.

•Tool, technology, and device-agnostic testing with AI accelerators and RPA frameworks.

•Dedicated Test Center of Excellence (TCoE) for Performance, Security, & Mobile.

•Reusable test repositories enable jumpstarting projects with faster testing outcomes.

•Rich pool of resources having E2E functional knowledge of insurance business processes.

•Flexible resourcing models

•Seamless customer support

Unlock the full potential of AI in your insurance industry with TestingXperts. We empower organizations to enhance efficiency, accuracy, and customer satisfaction.

The post AI in Insurance Industry – Role, Benefits, and Use Cases first appeared on TestingXperts.

The evolution of the artificial intelligence (AI) domain is progressing at a rapid pace, and just when we are wrapping our heads around GenAI, another game-changing technology has come into the picture: agentic AI. It’s not just another buzzword; it has the capabilities to transform business processes by automating workflows and decision-making and even predicting customers’ needs. This technology brings the versatility and flexibility of LLMs and the accuracy of traditional programming together.  

What is Agentic AI, and What is the Buzz Around it?

Agentic AI is one of the innovative advancements in the AI industry. It is the combination of different AI approaches, techniques, and models that create a new series of autonomous agents to analyze data, establish goals, and create action plans to achieve them. And the plus point is that businesses do not even require a lot of human input to manage it. Compared to traditional AI models that simply execute predefined prompts or tasks, agentic AI can make decisions and plan actions on its own and can even learn from its experiences to fulfill the goals set by its users.  

Agentic AI employs advanced AI techniques, such as reinforcement learning, ML algorithms, and LLMs, to constantly learn and improve with every interaction. For example, LLMs leveraged by OpenAI’s ChatGPT, Meta Llama, and Google Gemini assist in making autonomous systems analyze, understand, and respond to NL (natural language) commands. These systems can also analyze data and identify patterns between different datasets. By doing so, they can learn from their actions and improve their decision-making capabilities.  

Agentic AI is navigating a new era by supporting autonomous agents with independence in non-rules-based processes and decisions, which will transform industries and the way we interact with technology. But it’s not just about automation; it’s about upskilling machines so that they can become partners of humans in solving complex problems, support businesses with critical thinking and decision-making capabilities, and take action and learn from them.  

Why Are Tech Leaders Focusing on Agentic AI?

Following are some of the reasons why tech leaders are focusing on agentic AI solutions: 

• In fast-paced and ever-evolving processes like supply chain management, cybersecurity, customer support, and finance/banking, agentic AI would allow businesses to adjust their strategies in real time and facilitate quick decision-making. This would allow businesses to remain adaptable and resilient to changing market conditions. 

• Agentic AI solutions can solve complex problems by leveraging ML with goal-oriented behaviour to analyze data, identify data, and autonomously make decisions with optimal outcomes. It will also offer real-time problem-solving solutions in the dynamic business ecosystem. 

• By leveraging agentic AI, businesses can streamline their decision-making processes and respond to changing market conditions in real time. The autonomous nature of agentic AI will allow this technology to quickly process large amounts of data, significantly speeding up workflows. This time efficiency helps businesses stay competitive by enabling them to act faster. 

• Agentic AI systems can adapt to rapidly changing market conditions, making them highly scalable. As business demands grow or evolve, these systems will seamlessly adjust and continue functioning without requiring substantial new resource investments. This level of scalability will ensure businesses can expand operations without encountering the usual bottlenecks of scaling traditional processes. 

• Some of the key characteristics of agentic AI are autonomy and handling tasks with approximately zero supervision. This level of independence would allow systems to handle complex workflows and challenges in real time, allowing human employees to initiate important business-development strategies and reduce operational bottlenecks. This is another benefit that tech leaders are looking for in terms of efficiency. 

• Agentic AI can significantly lower operational costs by automating complex workflows and eliminating manual tasks. With reduced dependency on human supervision, businesses can save costs while improving accuracy and minimizing costly human errors. This cost-efficiency makes it an attractive solution for organizations aiming to optimize their resources. 

• Agentic AI offers robust risk management capabilities by autonomously analyzing data and making decisions based on predefined goals. This technology has the ability to detect potential risks in real time, allowing businesses to mitigate them proactively. Understanding emerging threats or inefficiencies would allow tech leaders to make informed decisions that minimize risk and safeguard business operations. 

6 Real-World Applications of Agentic AI

Streamlining Insurance Claim Processing:

The insurance industry has a lot of paperwork and manual processes, but agentic AI can change all that. Insurance businesses can utilize agentic AI solutions to automate claim processing completely. They can instantly assess the claim validity, gather relevant data from multiple sources, and communicate better with respective customers clearly and professionally. The result? Faster processing times reduced administrative burden, improved customer experience, and reduced errors.  

Improving Business Operations:

Agentic AI would change how businesses handle their daily tasks/operations/processes. The AI agents can autonomously manage demand forecasting, handle complex logistics, optimize the supply chain, and check inventory levels. Businesses can easily process huge datasets and free themselves from real-time decisions, which would reduce costs and improve operational efficiency. 

Enhancing Cybersecurity Measures:

As digital threats have become a big problem for businesses, agentic AI can guard network security tirelessly. AI agents can autonomously monitor network traffic, identify loopholes, detect anomalies, and run remediation measures to attack against cyber threats in real time without human supervision. It can help organizations enhance their security infrastructure and address complex security challenges.  

Transforming Customer Support Service:

Every business across all industries wants to deliver an expectational customer experience. Agentic AI can enhance customer support service by assisting software agents in providing personalized and 24/7 service beyond simple FAQs and automated responses. AI-enabled customer support agents would be able to understand written and oral queries, predict customer requirements, and resolve complex issues on their own. This level of hyper-personalization can help build brand loyalty and upscale customer experience. 

Integrating Agentic AI and IoT:

It’s been noticed that various use cases are possible with the integration of Agentic AI and the Internet of Things (IoT). For instance, a network of interconnected sensors and devices equipped with an agentic-AI-powered agent can monitor, analyze, and optimize operations in real time. This can completely transform and take industries like healthcare, transformation, and manufacturing to a whole new level of improved efficiency, safety, and reduced costs.  

Upscaling Software Development and QA:

What would it be like to have AI agents that can autonomously generate code and manage the entire development lifecycle? On top of that, they can also design system architecture, write/debug code, and supervise the quality assurance (QA) process. The result would be a dramatic improvement in the software production cycle and will transform how businesses design, develop, and maintain digital products. 

Ethical AI Considerations that Require Attention

Although Agentic AI may have so many perks across industries, it also brings some of its challenges. Considering ethical dilemmas like ensuring the decision-making aligns with human values, the challenges are bound to occur. Because of the complex nature of AI models, businesses would face obstacles in understanding or interpreting their decision-making. Another ethical issue is ensuring accountability and trust in high-stake applications. Who will be held accountable if the agentic AI makes a blunder? 

Another ethical issue is data privacy and security. These systems will turn business processes autonomous and independent, so enterprises will need robust security measures to ensure protection against breaches and human misuse. Businesses would also have to consider the potential impact of AI agents on a respective job. It’s true that Agentic AI can create potential opportunities and optimize productivity, but it would also suddenly shift workforce roles, education, and skills. 

Challenges in Implementing Agentic AI

Implementing AI agents comes with several challenges. Integrating Agentic AI into existing systems brings technical complexities, especially for businesses using legacy applications. They will need careful planning and technical expertise to ensure AI agent’s compatibility with their existing workflows and infrastructure. On top of that, companies have to invest in workforce training to manage the shift in operational processes and interpret AI-driven insights.  

Another challenge is the implementation cost. The initial investment in developing and integrating AI systems can be high (depending on business requirements). The cost includes training expenses, system upgrades, and maintenance. In addition, the top management must address the data privacy and security concerns regarding Agentic AI. Compliance with data protection standards and strong security protocols would be necessary to address risks.  

Best Practices for Implementing Agentic AI

Businesses should have clear AI governance frameworks and compliance measures consisting of pre-defined roles and responsibilities of each stakeholder. These stakeholders will be responsible for the development, deployment, and QA of agentic AI systems. Establish clear guidelines, include relevant compliance and regulations, and create monitoring and auditing processes. Next is to ensure the implementation of robust security protocols like encryption, regulatory and compliance vulnerability assessments, and access controls, which would help protect sensitive data and maintain Agentic AI integrity.  

Businesses should also conduct E2E testing and validation of agentic AI systems to ensure their safety and reliability. When conducting testing, consider both expected and unexpected scenarios to identify and mitigate flaws before deploying the final product in the real world. Businesses should also continuously update the agentic AI systems to ensure their security and effectiveness. Continuous monitoring and improvement will maximize the rigidity and value of agentic AI investment. 

How does TestingXperts (Tx) integrate Agentic AI with the software testing and QE Services?

At Tx, we know the benefits Agentic AI can bring to the business environment. We further enhance automation and efficiency by integrating AI into our software testing and quality engineering practices. Here’s how we can help you: 

Autonomous Test Execution: With AI’s decision-making capabilities, we at Tx entirely automate the QA process to adapt to the dynamic nature of software development. AI agents automatically generate test cases, identify gaps, and comprehensively cover test scenarios with minimal supervision. The result is faster QA cycles and reliable outcomes. 

Predictive Analytics: AI can quickly analyze past data and detect patterns to improve predictive analytics capabilities in testing. We prioritize test cases by identifying defects, risks, and bottlenecks, focusing on product quality and performance parameters.  

Process Optimization: AI streamlines testing workflows by autonomously optimizing test execution, resource management, and test environments in real time. This allows our clients to reduce operational overhead, accelerate time-to-market, and minimize human errors without degrading product quality. 

Self-healing and Optimization: AI agents detect redundant tests or test cases that do not effectively cover risks during execution. Leveraging AI, we optimize the test suite by expelling irrelevant tests and focusing more on critical areas. 

Summary

Agentic AI will transform the business industry by integrating the adaptability of LLMs with the precision of traditional programming. Unlike conventional AI, which follows predefined tasks, agentic AI autonomously makes decisions, plans actions, and learns from experiences to achieve user-defined goals with minimal human intervention. It has the potential to transform various business sectors by enabling real-time adjustments and efficient decision-making. 

Ensuring the quality and reliability of agentic AI systems is crucial, and this is where TestingXperts’ AI testing services come into play. Our AI-based accelerators, Tx-SmarTest and Tx-HyperAutomate, are designed to enhance software testing and automation processes. We assist businesses validate and optimize their AI solution implementations, ensuring they operate effectively and securely. By leveraging our expertise in QA for AI systems, you can confidently navigate this complex technology and unlock its full potential. To know how we can help, contact our experts now.

The post Why Agentic AI is the Next Big Thing in the Business Industry first appeared on TestingXperts.

Content
1. AI – Its Significance in Healthcare
2. Benefits of using AI in the Healthcare Domain
3. Some of the prominent AI Healthcare applications
4. 9 Significant use cases of AI in healthcare”
5. Conclusion

AI – Its Significance in Healthcare

Artificial Intelligence (AI), Machine Learning (ML), and its related technologies are today used in various business applications across industries of healthcare, pharma, automobile, banking, finance, insurance, etc. These AI applications not only deliver great results for businesses by simplifying business processes but also improves the human experience as a whole. Specifically, though AI is being leveraged across many industries, its dominance is more prominent in the healthcare sector.

Since the pandemic, the healthcare sector has been expanding in leaps and bounds with more demand for quality healthcare. Undoubtedly, though AI is touching every sphere of human life, the way it is transforming healthcare is truly phenomenal. Analysts believe that AI is poised to become a transformational force in the coming years for the healthcare domain.

According to MarketsAndMarkets, “The global AI in healthcare market size is expected to grow from USD 4.9B in 2020 and reach USD 45.2B by 2026 and is projected to grow at a CAGR of 44.9% during the forecast period.” The major factors driving the AI in healthcare market growth are the increasing volume of healthcare data, growing complexities of datasets, the intensifying need to reduce towering healthcare costs, the eventual need to deliver better patient outcomes, etc.

According to reports by Frost & Sullivan, the healthcare AI market is likely to experience a CAGR of 40% by 2021, and it has the potential to change healthcare outcomes by 30-40% and cut treatment costs in half.

As outlined by many leading research analysts, there has been more complexity and rise of data in healthcare which essentially demands the use of AI within this field. There are various ways in which AI is being employed by different healthcare providers and various life science companies which have been cited vividly in an article on the topic ‘Potential for Artificial Intelligence in Healthcare’ published by Future Healthcare Journal. Many of these publications state that the usage of AI in healthcare has been increasing in recent times and also state many advantages of leveraging AI in healthcare.

Benefits of using AI in the Healthcare Domain

1. Better patient outcomes:

Leveraging AI helps to bring-in better treatment methods and helps improve patient outcomes and their survival rates. There are various AI applications used to detect and analyze huge volumes of data and make effective predictions through ML algorithms.

These predictions continue to better the patient health predictions with the AI’s new computing power. Typically, all these predictions have been helping healthcare providers to deliver better patient outcomes.

2. Simplify healthcare with better data-driven decisions:

ML algorithms have the potential to provide data-driven clinical support decisions to both the doctors, physicians, and also to hospital staff. Evidently, this continues to simplify healthcare as AI uses algorithms and data to give automated insights that can be best used by healthcare providers to derive real-time decisions.

3. Reduce healthcare costs:

It has been stated that AI in healthcare helps identify inefficiencies within the systems and streamlines administrative processes and ensures healthcare cost reduction.

4. Screen diagnostic reports on par with radiologists:

Some of the AI imaging tools can be used to screen chest x-rays for signs of tuberculosis and they provide more accurate readings. Moreover, in places with low resource areas, many AI tools can be deployed to reduce the need for trained diagnostic radiologists and can be used to screen patient radiology reports.

5. Deliver customized treatment plans:

By leveraging AI and ML algorithms, doctors can deliver customized treatment plans after a thorough study of the AI patterns being produced. These patterns help deliver customized treatments based on analyzing various patterns of similar patient’s data

6. Reduce mortality rate:

There are certain AI applications being used by healthcare providers that have real-world use cases. These real-world use cases greatly help to make the diagnosis, treatment, and monitoring of patients more accurate, reliable, and also help reduce the patient mortality rates in certain cases

7. Enhance primary care and triage with chatbots:

Patients can use AI-powered chatbots to avoid trips to their doctor, as these bots provide instant answers to all patient health-related queries. These bots guide them with minimal medical instructions. These chatbots are available 24×7 and can deal with multiple patients at the same time

8. Reduce re-admission rates drastically:

AI tools are being leveraged to monitor respiratory rate, oxygen levels, pulse, blood pressure, and body temperature of patients. Eventually, it is helping to reduce the re-admission of patients effectively. According to a report, the readmission of patients cost US hospitals $40 billion annually. The Grady Hospital, the largest public hospital in Atlanta reduced readmission rates by 31% over a period of two years by adopting AI tools

Some of the prominent AI Healthcare applications

9 Significant use cases of AI in healthcare

1. Derive new treatments based on patterns:

Many AI apps are being used for early disease diagnosis, customized treatment protocol development, etc. Some of the AI-based apps analyze the relationships between treatment techniques used and the patient outcomes and help to derive new treatment processes by analyzing old patterns.

2. Cure neurological disorders:

Neurological diseases and trauma to the nervous system can take away some of the patient’s abilities such as speech, movement, and even patient’s interaction with their surrounding environments. Specifically, to treat such conditions, AI-based Brain Computer Interfaces (BCIs) are found helpful to cure such issues.

3. Discover new drugs:

Many healthcare and pharma companies are leveraging AI to help with the discovery of new drugs. AI solutions help to discover and improve the earlier lengthy timelines and processes that were used with any new drug discovery and in a way helping to get new drugs to the market faster.

4. Identify high-risk patients early:

By analyzing vast amounts of historic patient data, AI-based solutions can provide real-time support to doctors and clinicians to help identify high-risk patients faster than before and help to save them with better medical care.

5. Deliver better radiology predictions:

Radiological images obtained by MRI machines, CT scanners, and x-rays offer non-invasive visibility into the inner working patterns of the human body. There are various deep learning platforms that are being leveraged by healthcare doctors to analyze unstructured medical data of blood samples, radiology images, etc. and these can give doctors better insights into a patient’s real-time needs directly by using various AI tools.

6. Simplify Electronic Health Records (EHRs):

Leveraging AI in EHRs helps to create more intuitive user interfaces that simplify workflows. Especially, some of the AI solutions are being used to automate patient data entry routine processes that usually consume a lot of human effort and time, thus easing the overall process and is helping to simplify EHRs.

7. Prioritize clinician’s tasks:

AI applications are also helping to process routine requests from the inbox, like medication refills and various other result notifications.  These AI apps also help to prioritize tasks that truly require the clinician’s attention, said Adam Landman, MD, Vice President, and CIO at Brigham Health, making it easier for users to work through their to-do lists.

8. Improvise cancer treatment:

There are many types of cancer being identified early by leveraging various AI apps. AI apps can spot subtle patterns that are likely to be missed by humans and helps guide through better cancer diagnosis of different stages of cancer and the probable risk in some patients.

9. Detect harmful bacteria in blood:

AI-enhanced microscopes are being used to scan for harmful bacteria in patient’s blood samples. It becomes easy to identify and predict harmful bacteria in the blood with up to 95% accuracy by using these AI-based microscopes.

Conclusion

AI is the technology that is being leveraged by every industry segment to enhance their business outcomes and also to deliver a better human experience. The impact of AI in healthcare has been much more and wider than when compared to other industry domains.

Today, AI applications and especially bots are used in various treatment plans, diagnose diseases faster, protect patients from diseases, help them with customized treatment plans, etc. AI-powered solutions are predicted to play a leading role in how healthcare systems of the future operate and augment clinical resources.

These AI apps are likely to influence patient outcomes greatly in the coming years. But, the healthcare providers need to make sure their AI-enabled apps work seamlessly and continue to deliver a great patient experience. Hence, to get fully functional AI apps, and chatbots, leverage AI-based testing by next-gen testing service providers to get the desired high-performing AI apps.

The post Role of AI in The Healthcare Sector first appeared on TestingXperts.

1. Recent Tech Innovations Highlighting AI Regulations Importance
2. AI Regulations Around the Globe
3. Key Ethical Considerations for AI Development
4. Why Partner with Tx for AI Testing and Auditing Services?
5. Summary

The introduction of artificial intelligence (AI) is impacting everyone’s lives faster than we can comprehend. Its rapid adoption across businesses has started an international discussion regarding AI regulations, governance, and interoperability. As it becomes an integral part of human lives, changing business sectors and facilitating innovation, it is undoubtedly transforming how we live and work. Although AI and its subset technologies are becoming popular, they raise social, moral, and legal concerns. Deep fake, data breaches, AI theft, etc., there are plenty of reasons to question this technology’s moral capabilities and security.

According to a study, 92% of organizations think they need to make more efforts to assure customers about the fair usage of their data in AI. Business leaders, from CIOs to CEOs to project managers, want to implement generative AI tools to upscale their operations while fully harnessing technology to streamline processes, improve processes, and drive efficiency. According to a survey, around 77% of US executives believe that AI will greatly impact their businesses in the next three to five years compared to other emerging technologies. So, how can businesses stay ahead in an AI environment while assuring its integrity, security, and fairness? The answer lies in the AI regulations.

Recent Tech Innovations Highlighting AI Regulations Importance

Generative AI, one of the greatest innovations in artificial technology based on ML algorithms and capable of creating new and original content in text, audio, or video, became available for public usage. Businesses started analyzing, understanding, and implementing OpenAI’s ChatGPT-4 and other LLMs and algorithms within their processes. Another tech innovation that came to light was the introduction of AI in autonomous vehicles. Although this innovation promises to reduce human errors on roads, it certainly has some safety and liability standard challenges. Furthermore, AI-driven biometric and face recognition systems are increasingly used in security and personal verification processes. However, they also raise the privacy risks that could promote the misuse of personal data.

One cannot deny that AI is a dual-edged sword. Although it offers substantial benefits, it can harm individuals or businesses in various ways. This is why governments are focusing on strictly regulating the usage of AI and its sub-applications. Concerns such as user protection, fair business practices, civil liberties, safe virtual space, intellectual property rights, etc., clearly explain why governments might be interested in AI.

The US government is working at every virtual level to implement new regulatory protections, frameworks, and policies to cultivate secure AI development and prevent societal harm. The European Union’s Artificial Intelligence Act governs AI development, deployment, and implementation. Its main objective is to require developers who work or create AI applications to test their systems for associated risks. They must document usage and mitigate risks by taking appropriate actions. Chinese Cyberspace Administration is asking for public opinion on proposed Administrative Measures for generative AI services to regulate services offered to mainland China’s residents. The Canadian Parliament has already debated AI and the Data Act. It is a legislative proposal to seamlessly implement AI laws across the Canadian territories to mitigate AI risks and promote transparency.

AI Regulations Around the Globe

Governments worldwide have worked on drafting and passing laws specific to AI technology. As we approach mid-2024, businesses should expect broader and sector-specific AI regulations to impact all industries using AI technologies. Let’s take a look at some of the new AI regulations around the globe:

United States of America:

In October 2023, the Biden administration issued an AI executive order asking US government departments and agencies to analyze and make a report on the safety and security of AI technology and its sub-applications. It must also include associated risks and AI adoption implementation procedures and processes. The US government has also established multiple sector-specific AI-related bodies to address the evolving challenges associated with AI. For example, the FTC (Federal Trade Commission) focuses on consumer protection issues in AI-based applications and asks for fair and transparent business practices. Similarly, the NHTSA regulates the safety aspects of AI-enabled technologies, such as autonomous cars powered by AI. Then there’s CCPA, which implemented strict requirements for AI usage in business practices that involve consumer data.

European Union:

The EU AI Act and the Artificial Intelligence Liability Directive (AILD) are some rules the European Union sets for using AI. On December 8, 2023, EU policymakers reached a conclusion for the EU AI Act. The act will be made mandatory two years after its introduction, with some expectations for specific provisions. The EU is also changing its Product Liability Directive and adopting a new AILD to promote civil liability for AI among EU member states. Driven by measures like GDPR and the AI Act, the EU is adopting a proactive approach to AI legislation. AI systems collect and utilize data from multiple sources; thus, strict rules must be implemented to ensure individual privacy. The AI Act supports GDPR and intends to give EU members significant control over AI development, deployment, use, and regulations. Using these acts and principles, one can see that the EU is trying to become the global leader in regulating ethical standards and promoting competitiveness and innovation in AI deployment.

United Kingdom:

In comparison, the UK follows a sector-based and scalable approach to AI regulation (a statement released in its 2023 whitepaper). The UK government undertook feedback and consultations from AI industry leaders to construct its AI practices regulations. Businesses expect to receive high-level guidance and a regulatory roadmap containing sector-based regulators. The regulators will offer customized recommendations for the competition, healthcare, banking, financial, and employment sectors. The UK government will assess whether it is necessary to implement specific AI regulations or assign an AI regulator to inform businesses about the practices of implementing AI systems in their operations.

Canada:

Canada is taking a proactive approach to crafting and implementing AI regulations, balancing supporting innovation and facilitating societal interests and ethical standards. The Canadian Administration has launched various govt.-led programs, such as the Canadian AI Ethics Council and the Pan-Canadian AI Strategy, to highlight the responsibilities of developing AI solutions and addressing any legal or ethical issues that may arise in the AI industry. It plays a key role in assisting the stakeholders in collaborating to advance technology and develop policies that align with ethical values. The Canadian government has also drafted the Personal Information Protection and Electronic Documents Act to monitor the collection, transfer, usage, and disclosure of personal information using AI solutions. This Act covers the individual’s privacy rights and ensures they are preserved and that AI solutions meet strict data protection standards.

AI regulators vary from country to country, so cooperation among international organizations and countries is pivotal. Integrating regulatory compliances and utilizing AI for a good social cause is only possible through seamless communication and collaboration.

Key Ethical Considerations for AI Development

Below is the list of some ethical considerations when engaging with AI development to ensure the seamless integration of this innovative solution within the business processes:

Bias in AI Algorithms:

AI systems can unknowingly perpetuate and promote social inequalities if not carefully monitored. Developers and the businesses that fund them must use diverse datasets and robust testing methodologies to mitigate bias issues and ensure fairness across all user groups.

Transparency:

Understanding AI’s decision-making process is important to ensure trust and accountability. For sectors such as healthcare, finance, and banking, where decision-making is at the core, having clear documentation and communication about AI systems’ usage is crucial.

Data Privacy and Security:

As AI systems process a huge amount of data, businesses need to ensure stringent measures are in place to protect individual’s personal data and prevent its misuse. Advanced security protocols and regulatory compliances are important to protect user data.

Accountability and Autonomy:

AI systems are gaining more autonomy in decision-making, making implementing explicit accountability guidelines necessary. It will ensure that any damage or security incident arising from AI decisions can be addressed promptly and responsibly.

Why Partner with Tx for AI Testing and Auditing Services?

In the rapidly transforming AI landscape, ensuring AI implementation’s integrity, security, and reliability is a critical business challenge. Partnering with Tx will offer your business a robust solution to ensure secure and responsible AI integration. We follow the latest AI regulatory and ethical standards to ensure your business complies with every international and regional AI regulation. Here’s why you should partner with Tx:

• We bring years of experience in implementing AI in testing, conducting AI testing, and testing AI-based systems.

• Our team has highly certified security professionals who understand the complexities of AI security, from data integrity to threat mitigation. We ensure your AI deployments are protected from every security threat.

• We utilize our in-house accelerators, Tx-SmarTest, Tx-PEARS, Tx-HyperAutomate, etc., to ensure every aspect is tested and evaluated before being pushed into production.

• We always stay up to date on the latest AI regulations and ensure all your implementations comply with global AI standards.

• We recognize that each business has its own requirements. We provide customized AI auditing and security solutions to align with your business-specific requirements and ensure optimal functionality and performance.

Summary

The rapid integration of AI across industries has catalyzed an international dialogue on necessary regulations to address ethical, social, and legal concerns, such as bias, data privacy, and misuse of technology. As AI shapes future business operations, governments worldwide are drafting regulations to ensure safe, fair, and secure AI usage. Regulatory frameworks like the AI Act are being developed from the US to the EU to align AI advancements with societal values and business ethics. Partnering with companies like Tx, which adhere to these regulations, can help businesses navigate this evolving landscape securely and responsibly.

Contact our experts to find out how Tx can help with AI implementation.

The post AI Regulations Unveiled: Insights for Businesses in 2025 first appeared on TestingXperts.

1. What is Explainable AI and Why Does it Matter?
2. How does Explainable AI Function?
3. Business Benefits of Explainable AI
4. Examples of Explainable AI
5. Why Partner with Tx for AI Implementation?
6. Summary

The world is experiencing a massive technological shift, and businesses rely heavily on artificial intelligence (AI) solutions to optimize their service delivery. This significantly affects critical business operations, individual rights, and online safety paradigms. Most organizations treat AI as a black box exercise, ignoring how this technology works for them. They just want to work it correctly, and that’s it. Unfortunately, this approach is incorrect as it will create issues with the trust and reliability of AI systems in the long run.

That’s why experts are exploring Explainable AI (XAI) to improve the AI model’s trust rates. It will help in answering the questions like:

• How do these models use data to derive results?

• What type of approach does these models follow?

• Can we trust the results?

Answering these questions is the purpose of “explainability,” enabling enterprises to unlock the full value of AI.

What is Explainable AI and Why Does it Matter?

XAI is a set of methods/processes that enable users to analyze and comprehend the results/output achieved by ML algorithms. This allows users to improve their trust in AI/ML models and identify their accuracy, transparency, fairness, and outcome quality. AI explainability enables organizations to implement a dedicated and responsible AI development approach. Since this technology is becoming more complex daily, humans will find it difficult to analyze and retrace how AI algorithms work and produce results. Also, not every data scientist or engineer who creates algorithms can identify and explain how AI algorithms produce specific results and what’s happening at the backend.

That’s why understanding how AI works and produces results is necessary. The explainability concept enables businesses to understand the overall idea of AI systems and ensure they meet regulatory standards.

Why it Matters?

As ML models are impossible to interpret, and humans find it hard to understand, there could be high chances of bias based on gender, location, race, or age. Explainable AI enables human users to analyze, comprehend, and explain ML models, deep learning, and neural networks. It allows organizations to have complete details of the AI decision-making process with model monitoring and accountability. Businesses can continuously monitor and manage these models to facilitate AI explainability and measure its business impact. It also assists in mitigating any security, compliance, and reputational risk related to AI usage.

How does Explainable AI Function?

XAI’s working is based on the basic AI system designing and development approach. Here’s how the process works:

Supervising:

Organizations create an AI governance team to set standards and guidelines for AI explainability. This assists the development team in developing AI models and makes explainability a key component of an enterprise’s responsible AI guidelines.

Training Data Usage:

The quality of training data is a critical factor when designing an explainable AI model. Developers need to closely supervise the use of training data to ensure no bias enters the system. Any irrelevant data should also be kept out of training.

Result:

AI systems are designed to explain the source of the information.

Algorithms:

A model that leverages explainable algorithms to produce explainable predictions must be designed. It will have a layered design showing the overall path to its output and clearly defining the model’s predictions.

Techniques Used

There are multiple techniques for describing how explainable ML models use data to produce results:

• Visualization tools and data analytics explain how models predict specific outcomes through metrics and charts.

• Decision trees map the model’s decision-making process in a tree-like structure where inputs produce multiple outputs as branches.

• Counterfactual explanation technique creates a what-if scenarios list to display how a minor change in the model creates different outputs.

• Partial dependence plot (PDP) technique displays model outputs on a graph based on slight input changes.

Business Benefits of Explainable AI

Explainable AI’s value is its ability to deliver transparent and interpretable ML models that humans can understand and trust. This value offers various business benefits, such as:

Improved Trust and Acceptance of AI Systems:

Explainable AI helps build trust and acceptance in ML models and allows businesses to overcome the limitations of traditional ML models. This, in return, accelerates the adoption and deployment of ML models and offers valuable insights into different applications and domains.

Better Decision-making:

XAI offers valuable insights and details to support and improve business decision-making. It can provide insights into the areas relevant to the model’s predictions and prioritize the strategies to deliver the desired results.

Reduced Liabilities and Risks:

XAI helps mitigate the risks and liabilities of ML models and crafts a framework to address ethical and regulatory considerations. This helps negate the potential consequences of ML and delivers benefits in multiple applications and domains.

Examples of Explainable AI

• In the healthcare industry, explainable AI accelerates image analysis, medical diagnosis, and resource optimization. It also assists in improving traceability and transparency in the patient case decision-making process and streamlining the medical approval process.
• In financial services, XAI helps improve CX by facilitating credit and loan approval process transparency. It also speeds up credit and financial crime risk assessment and supports wealth management. This increases insurers’ confidence when deciding pricing, making product recommendations, and suggesting investment services.
• In autonomous vehicles, XAI clarifies driving-based decisions, especially concerning driver and passenger safety. Helping drivers understand how and why an autonomous vehicle makes its decisions gives them a clear picture of what scenarios it can or can’t handle.

Why Partner with Tx for AI Implementation?

Explainable AI offers deeper insights into AI/ML models through advanced analytics and drives innovation by identifying patterns impossible for humans to discern. Tx services in AI and ML development enable businesses to create bespoke solutions tailored to their objectives and challenges. Our E2E solutions, from model selection to training and deploying, ensure that the solutions are aligned with your business vision. Our AI implementation services cover:

AI Consultation:

Advising businesses on dedicated AI/ML solutions development strategies that sync with their business requirements and objectives.

ML Model Development:

Designing and training ML models that can address your business operations challenges.

AI-powered Automation:

Assisting in routine tasks and process automation with AI while improving efficiency and reducing manual supervision.

Predictive Analytics:

Developing models that accurately analyze past data to make predictions about valuables in areas like risk management, customer behavior analysis, and sales forecasting.

Summary:

Explainable AI (XAI) enhances transparency in AI-driven decision-making, addressing concerns about trust and reliability. Unlike traditional black-box models, XAI enables businesses to understand how AI processes data, ensuring fairness, accountability, and regulatory compliance. It improves decision-making, mitigates bias, and reduces risks in sectors like healthcare, finance, and autonomous systems. Partnering with Tx for AI implementation ensures tailored solutions, from consultation to predictive analytics, empowering businesses with responsible, explainable AI for sustainable innovation and growth. To know how Tx can help, contact our contact our AI experts now.

The post Why Explainable AI is Critical for Business Decision-Making first appeared on TestingXperts.