Kapil Girme

In a workplace, an AI tool—like a sales assistant helping a sales team—needs to earn people’s trust by showing it is useful, clear, and reliable. To do this, the AI should explain why it gives certain suggestions, share how sure it is about its advice, and be open about where it gets its information from. It should be introduced step by step so people don’t feel overwhelmed, let users decide how they want to use it, and give personalised tips that suit each person’s style and situation. Making the AI easy to talk to and understand, with simple language and clear pictures, also helps people feel comfortable using it. Also, listening to user feedback and improving over time shows that the AI is dependable and wants to help. Trust grows more when the AI is seen as a helpful partner, not a replacement for people. By encouraging teamwork between humans and AI, sharing examples of success, and letting users adjust how the AI works for them, the tool becomes something people want to rely on. When the AI consistently gives useful advice and works along with people, sales reps, team leaders, and clients will start to trust it and use it as an important part of their daily decisions.

Scenario: Handling Customer Support Tickets In customer support, agents deal with tickets (requests or complaints) from customers. Sometimes, the issues are more complicated than usual. These are called "exceptions." Currently, these exceptions are treated as one-off cases, but they can teach the system how to improve over time. What to Track: Ticket Categories: Keep track of the types of issues agents mark as exceptions (e.g., bugs, service outages). Response Time and Outcomes: Note how long it takes to fix the issue and if the resolution was successful. Customer Feedback: Track how satisfied customers are with how their issue was resolved. Agent Behavior: Record what steps agents take to solve issues and any special procedures or workarounds. Patterns in Exceptions: Identify what causes these complex issues (e.g., certain actions by the customer or specific errors). How the AI Decides What to Learn: Detecting Exceptions: The AI needs to spot when something unusual happens, like: Too many tickets in an uncommon category. Tickets that get escalated in strange ways by agents. Recognizing Patterns: Once the AI spots an exception, it should: Look for similar issues or solutions in past tickets. Check if resolving the issue quickly leads to higher customer satisfaction. Learning and Improving: When the AI finds patterns, it can: Suggest better ways for agents to handle similar issues. Use the new information to improve how it automatically handles tickets in the future, without needing much human help. Updating Models: Based on the patterns, the AI could create new rules or systems to handle these exceptions more efficiently. Human Oversight: Even though the AI is learning, humans should still validate the AI's decisions to ensure it’s improving in the right way.

One area that i think AI can handle in BPO industry is Creative Thinking & Innovation. AI generates ideas by analyzing existing data and recognizing past patterns. While it can remix or optimize known concepts, it doesn't truly originate novel ideas. Genuine creativity often requires breaking away from established norms, taking risks, or challenging the rules—something AI isn't naturally inclined to do. AI also lacks the deeper cultural, emotional, and market insights that are essential for meaningful innovation. It might offer technically sound suggestions that fall flat in real-world contexts. For instance, an AI chatbot might propose a marketing promotion during a service outage—logically consistent, but clearly tone-deaf from a human perspective. In the BPO space, creativity is often tied to a broader business vision or brand philosophy—areas where AI falls short in grasping the "why" behind the "what." Humans are better equipped to align innovation with strategic goals such as long-term growth, social impact, or brand identity. So, while AI can potentially handle 90% of the creative and innovative process, human intervention is still crucial to ensure the final output is contextually appropriate and aligned with broader business objectives.

As per my experience and knowledge, one task that i feel is too human is the 1-2-1 connects between the team members or the agents being done where there is a personal touch is i feel that is too human as of now and will be a difficult task to handle it through AI. Having said that, AI can be implemented in this area with the specific rules and guidelines however we will still miss the personal touch.