Conversational AI in Contact Centers: Why most programs stall, and what it takes to move forward
Most everyone has tried conversational AI in their call centers by now. Enamored by the promise of fewer calls, lower costs, faster service, leaders implemented digital agents to answer questions. The bots handle simple requests and information look ups, but as soon as things get more complex, the digital agent hands the customer off to a queue for a person.
The issue usually isn’t the technology itself. It’s how leaders are thinking of AI when they introduce it. Using AI for frequently asked questions and simple look ups is a great start. that almost guarantees it will remain an “answer bot.” Treating it as an integrated part of your contact center technology stack is what allows a bot to take on meaningful work.
Why “answer bots” are easy and everything else isn’t
Most programs begin with automation that’s fairly safe, controllable, low risk. The bot answers common questions, routes calls, or looks up account details. Makes total sense. When a customer asks for something outside that scope, the bot transfers the interaction to an agent. Often, that transfer happens without context, without all the information the customer has already shared about why they are reaching out.
That’s where many AI implementation efforts stall or stop. It’s also where the greatest opportunities sit for exploration.
Real contact center optimization happens when the process is re-envisioned from start to finish. Rather than look up information then hand off for a transaction, today’s platforms are capable of doing both providing information and completing tasks for customers. Even though changing a service plan, processing a return, or resolving a billing issue requires authentication, access to multiple platforms, and logic that follows a process from start to finish, many organizations are thinking all the way through the process. If we don’t change how we think about the possibilities, AI’s application within the context of customer service will always be limited.
Where most implementations go wrong
Based on our experience in Tech Implementation and Platform Migration, problems usually show up in three specific areas.
Data and Intent Gaps: Many bots are trained on limited data sets or simple keyword matching. They work fine until customers start speaking naturally. Once language becomes nuanced, the bot gets lost. To fix this, you need to apply contact center best practices to your data strategy: training on real transcripts and real customer behavior matters more than theoretical scripts.
The Impact on Agents: When AI does only the easy work, agents are left with the hardest conversations all day long. Forecasting, staffing, skills assessments – savvy organizations are looking at how roles, training, and tools change along with the customer interaction architecture. Successful programs rethink Workforce Management (WFM) alongside technology. You must redefine what agents do and how they’re supported, especially when they’re stepping into interactions that started with a bot.
Technology Isolation: Conversational AI is often bolted on as an isolated application with separate reporting, controls and administration. For pilots and proving in the business case, this is a great place to start. It’s not the way to grow your functionality because it's a disjointed experience for customers and for the internal resources handling calls that exit the conversational AI platform. Forward-thinking organizations are looking at how to preserve customer context from platform to platform within the ecosystem.
How we optimize AI implementation
At Blue Orbit Consulting, we start by looking at how customer issues actually move through the contact center today. Where do agents need help from other systems or teams? Where do interactions slow down or fall apart? That allows us to piece together the customer journey to identify gaps and system interactions and how to fine tune the process. The goal is simple: when AI handles something, it finishes it. When it can’t, the next person has full context and can pick up without starting over.
The result is an end to end customer experience in which your processes utilize the right blend of AI and people components to drive true operational efficiency.