Haroon Rashid

Combining of data and analyzing with different verticals and AI solution based on prompts and flows could help to find hidden cross process trends we can do it as per below put ins. Put ins: 1. Short summaries of problems, complaints, or notes from each staff member (Quality, Human Resource, Finance, Operations, support functions). 2. Metadata: The date, time, department, team, and keywords or tags that are important. 3. Quantitative data, such as the number of events and the time it took to solve the problem. Answer from AI: 1. We can use natural language processing (NLP) to look at text summaries and find main ideas, concepts, and feelings. 2. The Pattern recognition use machine learning algorithms will find patterns that happen over and over again in different areas, such as Problems or causes that are similar and repetitive. Keywords or ideas that come up a lot occurring frequently. Events or escalations that happening at a same time 3. Visualization: we can show ideas in a way that is clear and easy to use. Such as Heat maps or network diagrams that show how problems and areas are linked. Trend research and making predictions Grouped summaries of problems or patterns that are similar with each other. Best Practices and steps to take: 1. Cross-functional patterns: Basis on the problems that happen over and over again in more than one area. This lets leaders get to the bottom of them and make process improvements. 2. Process optimization: To show leaders where processes aren't working as well as they could and stop problems from getting worse. 3. Allocation of resources: we can use data-driven ideas to tell leaders how to better allocate resources. 4. Predictive analytics: we can take action ahead of time to stop problems or problems from getting worse. Showing off: 1. Interactive dashboards: we can give leaders a crystal-clear information in real time and the ability to drill down. 2. Automated reports: Automated reports play vital role making regular reports that show important patterns, trends, and suggestions will give a direction where to move. 3. Alerts and notifications: By Setting up alerts for important problems or situations that get worse, so that they get your attention and action right away. Leaders can learn more about cross-process patterns, make decisions based on data, and improve the company by using a prompt + flow-based AI solution.

Natural language processing (NLP) is used by the AI to look at the claims data by asking a number of questions. The questions will be based on claims this will helps to find a relevant information, possible problems, and decide if the claim raised is true or false. Flow Logic: The flow logic plays a vital role in claims the AI starts a set of steps when it analyzes something, such as Asking for a detailed information or proof giving the claim to a person reviewer to look over again automatically approving or refusing the claim based on the rules and guidelines of the policy Making a choice letter or notice for the policyholder. Handling Exceptions: The AI can also find complicated or unique claims because all claims are different with each other due to which you need to refer to a human help and send them to be experienced adjusters or experts to be looked over, so kind of app stands out because it has the ability to speed up processing an average handling time and lower turnaround times Make choices about claims more accurate and consistent. Cost Reductions: Costs can be cut by automating routine flows and cutting down on manual intervention. It's valuable because it can speed up the claims process, make customers happier, and let BPO providers handle a lot of cases without sacrificing quality or accuracy.

When thinking about ways that technology could improve mental healthcare in South Africa, one possible scenario is the use of therapy robots. Which will improve mental health. The Problem: The availability and affordability of mental health services are severely limited for many South Africans, especially those living in rural areas. Due to which it impact severely. Some of the problems with traditional therapy include stigma, long wait times, and an inadequate number of trained therapists. Understanding different cultural contexts and individual needs is crucial for chatbots to be effective in providing initial support, emotional assessments, and useful tools. My recommendation for fixing this issue is to use a broad language model approach that has been fine-tuned. Because of this: By refining a language model, we can better account for South Africa's rich linguistic and cultural diversity. By training on local information, the chatbot can give answers that make sense and are relevant to the situation at hand. Emotional Understanding: Through fine-tuning, the model gains a greater understanding of the nuances of emotional language. This makes it easy to identify indicators of pain and respond appropriately. Rather than building a model from scratch, fine-tuning uses what is already known. This saves time and money. The goal of this method is to save time and resources without lowering the quality. More and more people will be able to get help with their mental health because this approach is easy to adapt to other languages and areas. If we look at alternate Rule-based systems cannot handle the intricacies of human emotions and cultural contexts. Training that starts from beginning requires a large amount of data and computer power, so this is not the greatest method to use it. Flow and prompt-based design can be effective for structured conversations, but they may be unable to give the empathy and insight required for mental health care. Improving an LLM can contribute to the development of a mental health support system that is culturally sensitive, effective, and fulfills the needs of all South Africans.

An AI agent may be implemented to aid in project management duties, including the identification of potential risks, the monitoring of progress, and the provision of recommendations for optimizing workflows. There are numerous methodologies in use within an organization; however, the six-sigma methodology is the most effective. A project leader can adhere to the DMAIC phase to achieve the desired level of quality. Situation: The AI agent is responsible for monitoring the project's progress. In our organization, we employ the DMAIC approach, which entails the creation of step 0 and the control phase before proceeding to the whole project. Once problem statement and goal statement defined as per operational definition ARMI is created with roles and responsibilities at start of project definitely the AI agent viewed with the doubt by stakeholders and project team members the reason they may be concerned about inaccurate information or make poor decisions. Transparency and Explainability: AI agents should provide explicit explanations for their suggestions and decisions. For example, when proposing a change to the project milestone timeline, the AI should explain the data and reasons behind the idea. This transparency helps team members comprehend the AI's thought process and creates trust in its capabilities. Consistency and Reliability: AI agents should provide correct and reliable information. Consistently updating the team leader with their progress reports, risk assessments, and performance indicators may lead to AI as a reliable resource. Consistent performance will gradually increase confidence within the team in trusting AI. Collaboration between AI agents and humans: Collaboration plays a vital role. AI should be developed as an opportunity and not to replace human decision-making. For example, the AI can deliver data-driven insights in each phase of the DMAIC methodology while allowing the project leader and an MBB to make final decisions on whether to go with data insight or not. This collaborative approach ensures that the AI is viewed as a beneficial collaborator rather than a threat. Feedback system: Project leaders must seek approval from Master Black Belts at each stage of the process. This system allows users to submit feedback on AI performance. The AI can use this feedback to enhance its recommendations over a period of time. By actively seeking and integrating user feedback, the AI shows its commitment to ongoing enhancement and attentiveness to user demands. Example: Considering an example on AI Agent: Hello (Project Leader) I have noticed that the existing resource workforce may cause a delay in completing the improvement phase. Based on statistics from our project management platform and recent team communications, we appear to be short on workforce data for the forthcoming phase. I advocate reallocating the workforce from the process. Here's a full breakdown of work allocation and predicted timelines to complete the phase. The DMAIC method in project management, especially when combined with an AI agent, improves process optimization, risk identification, and progress tracking. Embracing transparency, reliability, teamwork, and constructive input can help the AI earn the trust of skeptics and position itself as a valuable asset to the team.

When AI employees work in customer service, they often need to find a way to be quick and kind at the same time. While strictly adhering to process rules can expedite responses, it may overlook individual customer needs, potentially leading to customer dissatisfaction. Consider customer delight full first and this are the one of our six values of organization, on the other hand, might mean personalized exchanges that are different from what is usually done, which could slow things down. To help the AI handle we need to follow below approach: Organized success factors that measure how well you're doing in terms of response time and how satisfied your customers are like customer sentiment analysis score. In accordance the AI can judge acts by how they affect both. Next is setting up escalation procedures means teaching the AI to recognize when a person needs to step in, like when a customer's body language shows they are dissatisfied. This approach makes sure that hard problems get the attention they deserve, even if they are difficult to understand or deal with. If we take one example a customer gives an NPS score of 3 and says, "The billing system is confusing, and last month I was charged too much." Score (NPS): Identify as a Detractor (scores from 0 to 6). Feedback in full: Words such as "confusing," "overcharged," and "last month" indicate a specific financial issue. Prioritize immediate communication: It's crucial to address feedback promptly. Communicate with the customer in a manner that suits their preferences. For example, "We're so sorry to hear about the problem that you had with our billing system and the extra charge you got last month." We understand how upset you are. If possible, please provide more details so we can quickly investigate and resolve the issue. Focus on solving the problem instead of advocating right away. The main goal is to resolve the problem and rebuild trust, not to get a better NPS score right away. Possible to escalate to a human agent: Because the problem is a billing error, the AI may escalate the case to a billing expert who can fix the overcharge and explain how the billing system works.

Let's look at a process that a business process outsourcing (BPO) company does in the area of e-commerce: answering customer service questions after a purchase for an online store that sells clothes and accessories. How an AI Agent Could Handle 90% of the Work: An AI-powered Chabot, integrated throughout the retailer's website, app, and social media channels, can efficiently handle a high volume of post-purchase inquiries by: • Provide real-time order status updates, retrieve tracking information from carriers, and answer delivery timeline questions. • Assisting consumers with returns and exchanges, including policy information, automatic label generation, and system workflow. • Answer basic billing inquiries, provide invoice copies, and process small adjustments, such as applying discount coupons missed during checkout (within established restrictions). • Answering frequently asked questions (FAQs) about product maintenance, sizing, materials, and past purchases. • Customers can alter their shipping address, contact data, and payment information prior to dispatch. Scenario Requiring Escalation to a Human Agent: A customer contacts assistance, saying that they received the incorrect item in their order. The AI agent verifies the order information and admits the discrepancy. It tries to start a typical exchange process, but the system says the initially ordered item is out of stock and won't be back in stock anytime soon. AI agents should escalate interactions to humans based on the following criteria and signals: 1. System Limitation in Processing the Request: When the automated system is unable to satisfy the customer's request using regular workflows (for example, initiating an exchange for an out-of-stock item). The AI recognizes its incapacity to continue with the typical procedure. 2. Policy exceptions or deviations are required. The customer may request a solution that goes beyond the typical return/exchange policy. The AI is built to recognize certain policy exceptions. 3. Requires complex problem-solving and negotiation skills. When the circumstance necessitates problem-solving beyond pre-defined scripts (for example, the customer is extremely upset, numerous products in the order are erroneous, the supplied item is drastically different or damaged). This requires human judgment and the capacity to provide individualized solutions. 4. If a customer expresses strong negative emotions or uses aggressive language, the AI should prioritize de-escalation and transfer the interaction to a human agent with strong emotional intelligence and negotiation skills. Sentiment analysis can cause this. 5. If AI is unable to understand a customer's issue despite several clarifying queries, it should escalate to a human for more effective inference. What Happens After Escalation: If any of these escalation conditions are met, the AI agent should politely advise the consumer that their issue needs to be escalated to a human support representative for further assistance. • Estimate when a human agent will contact you (e.g., immediate transfer or callback/email answer within a certain timeframe). • Transfer conversation context, including order details, customer message, and AI troubleshooting procedures, to the human agent. This saves the consumer from having to repeat themselves. • Categorize reasons for escalation (e.g., "out-of-stock exchange," "policy exception requested," "high customer dissatisfaction," "unclear issue") to assist human agents in swiftly understanding the situation and responding appropriately. By utilizing these escalation triggers, AI can quickly handle the bulk of common post-purchase queries, freeing up human agents to focus on more complicated, exception-based, and emotionally sensitive cases, resulting in increased customer satisfaction and operational efficiency.