Most AI conversations in customer contact start with deflection. The larger, better-evidenced opportunity sits on the agent's side of the desk — provided the time saved is handed back to the person, not swallowed by the target.
In the first article of this series we argued that the strain in a modern contact centre comes less from conversations than from the hidden work wrapped around them: assembling context, bridging systems, summarising, registering, handing over. That framing changes what AI is for. It stops being a way to keep customers away from people, and becomes a way to give people back the parts of the job that need a human.
Automate the task, not the human relationship
When organisations think about automation in customer contact they reach first for chatbots, voicebots and self-service. Those matter — simple, well-bounded questions genuinely should be resolvable without an agent. But the opportunities on the employee side are broader and, in our experience, faster to prove.
Where AI reduces the load on the agent
Recognise the customer and the reason for contact
Identify who is calling or writing and why, before the agent types a word.
Assemble the relevant information
Pull history, entitlements and open cases from across systems into one view.
Search the knowledge base for them
Surface the applicable article or procedure in context, rather than expecting a keyword hunt mid-conversation.
Propose an appropriate response
Draft the reply or the next question, with the agent editing rather than composing from scratch.
Summarise and register
Write the summary, classify the contact reason and complete the routine fields of the record.
Start and track follow-up
Trigger the follow-up process, flag missing data, book appointments and watch callback commitments.
The pattern in all of these is the same. The agent no longer has to tie the systems and process steps together by hand. Technology takes the predictable work; the person keeps the conversation and owns the resolution.
What can generative AI actually deliver?
This is where evidence helps. A study of more than 5,000 customer service employees, given suggestions from an AI assistant during digital conversations, found the number of issues successfully resolved per hour rose by around 15 percent on average. The largest gains went to less experienced staff, who could suddenly draw on knowledge and phrasing that had previously lived with their most experienced colleagues.
It is worth being honest about the limits of that finding. It covers one organisation and one specific application. The benefit was smaller for highly experienced employees, and in some situations quality dipped slightly. In other words: this is a real effect, not a guaranteed one, and it is sensitive to how the deployment is designed.
- The experience level of the employee being supported
- The complexity of the contact being handled
- The quality of the information the model can draw on
- The reliability and behaviour of the model itself
- How easily an agent can check, override or ignore a suggestion
“The agent must always be able to see what the system is doing, and keep the ability to decide differently.”
When technology creates more work pressure
Automation does not automatically produce better work. It can increase pressure — most commonly when every minute saved is immediately converted into a higher production standard. If AI shortens summarising, that does not mean the agent should simply process more conversations back to back. When the saving is entirely reinvested in pace, the benefit to the employee disappears and the technology has become a speed-up device.
Warning signs that new technology is adding strain
Constant monitoring of AI output
The agent becomes a full-time proofreader of the assistant.
Advice that cannot be explained
Nobody can say why a suggestion appeared, so nobody fully trusts it.
No permission to deviate
The system's recommendation is treated as the only defensible answer.
Yet another application
AI arrives as a fifteenth window rather than inside the existing workflow.
Insufficient training
People are given a capable tool and left to infer how and when to rely on it.
Automated data used against people
Metrics gathered by the system drive assessment, while responsibility for the system's errors lands on the agent.
So the best solution is not the one that automates the most actions. It is the one that demonstrably helps people do the work more easily, more accurately and with more confidence — and that leaves them in charge when the situation calls for judgement.
From principle to programme
None of this is difficult to agree with in a workshop. The difficulty is sequencing it: what to look at first, which tasks to automate before others, what to measure, and how to keep agents in the driving seat while the estate changes around them. That is the subject of the final article in this series.
