AI has revolutionized how modern businesses approach customer service, so much so that now imagining a workflow without it seems almost impossible.
According to a report by the Nielsen Norman Group, AI-led customer support agents can handle 13.8% more queries per hour than non-AI agents. Another research by Gorgias found that AI automation boosts First Response Time (FRT) by 37%. For a business, these advantages translate to better Customer Experience (CX).
However, there’s a catch. According to Forbes, 75% of customers still feel AI chatbots aren’t fully equipped to handle complex queries because they lack accuracy and the capability to establish a connection. In such cases, AI-driven journeys end up feeling slower than human support, hurting customer service and CX.
So, in today’s blog, we’ll break down the causes behind this AI-induced friction in customer service and how you can overcome them to create seamless workflows.
Where AI Adds Friction Instead of Removing It
The core reason why you implement AI in customer service is to get things moving quickly. But sometimes, it does just the opposite and creates friction, defeating the whole purpose. Here’s how:
1. Over-Validation of Queries
Suppose you contact a company’s customer support to get assistance regarding an order. But you never reach that point because their AI chatbot keeps verifying (and reverifying) your details. This is a classic example of how AI can sometimes add unnecessary steps to a journey, making the process annoying for customers.
2. Poor Escalation Logic
When it comes to resolving complex customer queries that require personal judgment, empathy, or decision-making, AI chatbots rarely hit the mark. This is why they’re trained to immediately escalate such queries to human agents. However, due to poor escalation logic, the handover can get delayed and customers have to wait longer. In some cases, it’s so slow that the agent’s assistance doesn’t even make sense anymore.
3. Slow Turn-Taking in Responses
In both voice and chat conversations, it’s often noticed that AI chatbots sometimes take too long to actually respond to a message. This stands especially true for queries that are complicated or involve layers of dependencies.
Common Design Mistakes That Create AI Bottlenecks
If AI automation is worsening your CX, the problem isn’t the technology itself: it’s how you’re implementing it.
Here are a few design blunders that are generally responsible for the issue:
1. Sequential Automation Instead of Parallel Execution
Most enterprises that report poor CX due to AI automation have one thing in common: a sequential automation framework instead of a parallel one. It processes each step (even the most trivial ones like checking customer identity) after the other, taking more time and increasing AI latency.
2. Overuse of Heavyweight Models
Heavyweight models handle complex tasks well. But using them for every request, including simple ones like order status checks, puts unnecessary strain on servers. This increases system load, slows response times, and turns what should be quick interactions into frustrating delays.
3. Too Many Uncoordinated AI Tools
If you implement different AI tools to handle every task involved in a query, your workflow is bound to get slow and complicated. Silos between tools cause redundancy and too much to and fro, which leads to delays, inconsistent responses, and confused handoffs.
4. No Orchestration Layer
Without a central orchestration layer, there’s no clear control over how AI tools work together. The system lacks logic around when to act, pause, or escalate to a human agent. This leads to tasks firing out of sequence, delayed escalations, and support journeys that feel chaotic rather than seamless. It’s almost like the workflow lacks a brain.
Why Latency Breaks Trust Faster Than Inaccuracy
Suppose your enterprise is struggling with two AI automation issues: response latency and inaccuracy. What will you focus on fixing first? If your answer is response accuracy, here’s something you should know:
According to research by Harvard Business Review, 33% of customers prefer brands that reply quickly, even if the response itself is ineffective. It’s not hard to understand why.
Response effectiveness, although crucial, is secondary. At first interaction, customers judge AI’s intelligence by its speed. Fast responses show the AI is capable of understanding and processing their requests, which ultimately helps build a sense of trust and reliability.
On the contrary, if the responses are slow and sluggish, customers lose their trust in the brand. They also become more likely to drop off. In fact, latency is often the “make or break” factor during voice and live chats. Various studies show that 38% of customers abandon chats if the response doesn’t arrive within 30 seconds!
Latency doesn’t just affect your customers; it also creates internal friction across your enterprise. When your AI responds slowly, your systems are forced to escalate queries to human agents sooner to avoid customer frustration. This increases agent workload, raises support costs, and reduces deflection rates. At that point, automation stops delivering efficiency and becomes an expensive bottleneck.
Real Examples of Automation Gone Wrong
Numerous instances prove that AI automation has backfired:
- Chatbots Delaying Agent Assistance: When AI chatbots keep adding steps, like user verification or order verification, before transferring the conversation to a human agent. This shows that the model’s escalation logic needs fixing.
- Long Pauses During Conversations: When AI chatbots take too long to reply or frequently pause during the chat. It signals a lack of coordination between the tools at play.
- Workflows that Take Forever to Move:When AI workflows move slowly, and every step waits for the previous one to finish. It points to weak or missing orchestration, and that the workflow depends heavily on backend systems.
Designing AI That Actually Accelerates CX
As mentioned above, most AI automation issues that hurt CX arise due to design complications that can be fixed. Here’s how:
1. Choose Parallel Task Execution
As opposed to sequential automation that processes one step at a time, parallel task execution handles multiple tasks simultaneously. For instance, the AI chatbot verifies a customer’s ID while checking their order status, payment history, etc. This saves time and speeds CX.
2. Opt for Lightweight AIs for Simple Queries
Instead of implementing one bulky AI model for addressing every query request, reserve it for complex tasks only. Introduce lightweight AI models in your workflow for handling simple, routine queries. They are fast and efficient. Besides, this combination strikes the right balance between speed and accuracy.
3. Pick Smart Early Escalation Framework
A poor escalation logic is one of the biggest design issues in AI that hampers CX. To overcome it, implement a smart early escalation framework in your workflow. It uses confidence scoring and minimal validation steps to detect when the AI can’t handle a query and immediately hand it off to a human agent.
4. Ensure a Latency-Aware Design
Simultaneously fetching multiple details can take time. But a latency-aware design helps ensure the AI chatbot responds immediately with some information first, while waiting for the rest to arrive. This speeds CX and keeps the customer engaged.
How Kapture Avoids AI-Induced Bottlenecks
Kapture’s Self-Serve platform is designed for enterprises struggling to effectively leverage AI automation. While it has a range of useful features, here are three of them that specifically address AI-induced bottlenecks slowing down CX:
1. Orchestrated AI Agents
Eliminate the need for multiple disconnected AI tools. With Kapture, you can employ smart AI agents that handle both chat and live conversations across all platforms. Best part? They are coordinated by a central system that ensures every task, escalation, and resolution is executed smoothly.
2. Fastlane-Style Parallel Backend Workflows
Kapture’s AI agents are efficient by design. Powered by fastlane-style parallel backend workflows, they remove sequential request chains that cause visible delay and handle multiple tasks at the same time, from running identity checks to looking up order details. This significantly boosts CX.
3. Speed-First CX Design
With parallel API calls and instant partial answers, Kapture’s AI agents are built on a speed-first design approach. They prioritize response speed by anticipating delays and minimizing wait times at every step of the workflow.
Faster CX Is a Design Choice
AI automation slowing down CX isn’t a rarity. Enterprises that are in the initial stages of implementing the technology often face this challenge. But luckily, overcoming it is just as simple.
Whether it’s lower deflection rates or poor FRT, a few design tweaks are all you need to remove automation inefficiencies and build seamless workflows. The result? A team that moves faster and a CX that keeps pace.
Want to build a smoother CX? See how Kapture’s AI agents work—request a demo today!
