Every agent has a limited amount of attention in a shift, and most of it is spent on work that does not require judgment. Searching for information, documenting cases, completing paperwork after each call. As volume increases, this repeatable work does not just add time. It takes attention away from the interactions that actually need it.

That workload affects where teams can direct their attention. Time spent searching for answers or completing documentation leaves less capacity for situations that require context and judgment. At scale, the added demands can affect efficiency and overall performance.

For IntouchCX, the question is not how to make agents faster. It is where their attention creates the most value, and how to protect it.

When Repetitive Tasks Compete for Agent Attention

A knowledge search that adds time to a single conversation may seem minor. Across a high volume of contacts, those extra seconds accumulate. Case notes and after-call work (ACW) add further administrative demands, increasing the effort required to complete each conversation as volume grows.

According to research on average handle time, 47% of contact centres use Average Handling Time (AHT) as a formal target that individual agents are measured against, rather than simply tracking it as a general operational metric. When AHT functions as an individual performance benchmark, every component that feeds into it directly shapes how an agent is evaluated.

The effect extends into the conversation itself. Moving between systems can interrupt an agent’s concentration, while manual documentation creates more opportunity for inconsistency. Every minute spent on a system switch or a note is a minute the agent is not fully present with the customer in front of them. These inefficiencies can affect quality across operations.

That was the case for an education publisher serving colleges and universities across North America. As end-user expectations increased, manual processes were affecting handle time and quality across the program. Reducing the administrative effort surrounding each conversation became a priority for improving performance. 

The next step was deciding, deliberately, which work belonged to a system and which work belonged to a person.

What Does Sidd Spark Look Like in Practice?

Sidd Spark is an agent-assist tool that mirrors an agent’s existing workflow in real time. Instead of asking agents to adopt a new process or a separate tool, it works inside the process they already follow, surfacing information and generating documentation as the conversation happens rather than after it.

That required a clear view of where AI could reduce repetitive tasks without removing human judgment from the conversation. The publisher partnered with IntouchCX to deploy Sidd Spark against this exact need. Sidd Spark was embedded directly into the workflow to take on specific tasks, allowing teams to focus on the complexities of each interaction.

The deployment focused on four areas: 

1. Relevant information in real time. Teams can access knowledge base content without manually searching across systems. The agent stays on the call instead of switching screens to look something up.
2. Case documentation generation. Interaction details are captured without requiring agents to take manual notes. The agent’s attention stays on the customer instead of splitting toward what to write down.
3. After-call work reduction. Automated documentation limits the administrative tasks required once the conversation ends. That time returns to the agent instead of disappearing into the queue.
4. More time for complex needs. Teams can direct more of the conversation toward resolving situations that require their expertise. This is the point of the other three. Every second Sidd Spark reclaims from searching, documenting, and after-call work is a second an agent can spend reading a frustrated customer correctly, instead of rushing past them to hit a handle time target.

Integration was also central to adoption. As usage increased, the program began to see measurable changes in performance.

The Performance Impact of a Focused Workflow

The deployment produced measurable improvements across the program:

• 25% AHT improvement: From 863 seconds to 645 seconds, beating the program goal by 60.
• 88% QA performance: Quality performance finished eight points above the program benchmark.
• 94% Sidd Spark adoption: Utilization exceeded the 90% program target.
• Improved documentation accuracy: Accuracy increased across the team as manual administrative tasks decreased.

Read together, these numbers tell a specific story. AHT dropped, and quality rose at the same time, which does not happen when agents are simply rushed. It happens when the time saved on repetitive work is spent where it counts. The quality score eight points above benchmark is the clearest evidence that reclaimed attention, not just reclaimed time, was what moved the program.

The Right Balance Between AI and Human Expertise

For CX leaders, finding the right balance starts with looking at the nature of the work. Consistent, process-driven activities are strong candidates for automation, while situations shaped by nuance and judgment benefit from human involvement. This distinction provides a practical way to prioritize AI investment without compromising service quality.  

As a CX transformation partner, IntouchCX works with brands to connect AI strategy to the realities of their operations. That means understanding how work moves through the organization and identifying where technology can support stronger performance.

The result is a clearer role for AI within CX: create capacity where technology can improve the outcomes and give teams more room to apply their expertise where it has greater business impact.

See how Sidd Spark can give your agents back the attention your most complex conversations need.