Sarveshvar

In a contact center, things rarely go exactly as planned. You might have a forecast that looks solid, but during the day it can change fast — a few people don’t show up, call times go higher than expected, or the client suddenly says ,for example - “focus on collections more than sales.” If I were building an AI agent to handle this, I wouldn’t want to rebuild it every time something shifts. I’d do three simple things: First, keep the backbone steady. The AI’s main job — spotting when SLAs are at risk, comparing forecast vs. actual — doesn’t need to change. What does change (like priorities or staffing numbers) should just be stored as data that’s easy to update. Second, plug it into live inputs. The AI should read from WFM or call systems so it knows today’s reality, not last week’s plan. And it has to be smart about limits — you can’t just move anyone into collections if they’re not trained for it. We can move only cross-skilled agents. Third, keep humans in the loop. The AI can suggest overtime, break shifts, or call-backs, but supervisors see the impact before hitting “go.” Over time, the system learns what works without being retrained from scratch. That balance — stable logic but flexible levers — is what keeps the AI useful even when the floor keeps changing.

"From what I’ve seen in contact centres, the toughest part of employee development is the gap between classroom training and real calls. Agents may clear induction, but when a frustrated customer is on the line, the theory doesn’t always stick. Trainers are stretched thin, so feedback usually comes much later in QA reviews. That’s where AI can actually help—not as a bossy tool, but as a coach. It could drop a quick hint in the middle of a call (“check the refund script in KMS”), line up a short refresher module after a shift, or give instant feedback once the call is done. Even practising role-plays with AI could make agents feel more confident before they face live traffic. To make sure it’s supportive and not controlling, I’d keep it transparent, highlight positives first, and frame suggestions as options, not orders. If the agent feels guided—not judged—the AI becomes a mentor rather than a monitor."

In BPO, the client usually gives the forecast based on which the vendor plans the agent HC requirement and schedule. From the client perspective, AI can blend historical patterns such as seasonality with external actions like marketing campaigns, macro events, weather, and product changes that cause an increase in customer interactions to improve forecast accuracy. This also greatly helps when the client shares the intraday hourly forecast, which shares how many agents/volume are expected in each hour for the day. This will result in fewer calls getting abandoned, less waiting time, etc. Many times, the monthly forecast shared 2 months in advance doesn't match the intraday forecast received a few days before the given day. For example, a locked forecast of 60 given 2 months back by the client, for which the vendor hires the HC to meet the demand. But the client themselves send the intraday forecast a few days before the given day, which shows we need 70 as locked HC. This led to unwanted issues for the client's customer and the client as well. On the vendor side, AI can help to schedule the best agents in peak hours. Schedule the agents based on their preferences, like weekday, weekend, day shift, or night shift, considering their personal time. In Western scores, many BPO agents are college goers who work in BPO part-time; identifying them and scheduling the shift as per requirements makes this a better place to work.

AI can be used to understand customers by creating a profile based on their past interactions. This can be done by not treating each customer interaction in isolation. This info helps the human agents/chatbots to start the conversation where it was left off in the past. Doing so also avoids asking unnecessary questions. AI can also do real-time personalisation by listening to the calls, understanding the issue, and understanding the tone and mood of the customers and suggesting the next possible steps based on the interaction. E.g., chatbots and virtual assistants can adjust tone, language, and content based on customer mood, demographics, and intent. These can also listen to the calls and suggest offers to the customers, send texts to customers regarding cart abandonment, etc.

Any tool is as effective as the user. AI can analyse massive data and find actionable insights for the business to act on. The sooner the business takes this path, the quicker it can gain an advantage over its competitors. AI can help the leadership with speed, precision and foresight to make a strategic decision. The advantage can be in any of operational efficiency, customer experience and continuous learning and innovation, or all of these, depending on the direction the leadership takes.