Small Change, Big Return: How a £500 AI Triage Cut £6,000 from Annual Support Costs

This was an SME support environment combining hardware and software work where customer tickets landed in a shared inbox and triage was a distributed, manual task. Team members read each ticket, tried to identify the product stream and responsible function, and then reassigned or nudged it to the right person. That routine felt small in isolation, but it happened a lot and therefore generated hidden labour, constant context switching and occasional delays when the person who normally covered triage was distracted or occupied.

Beyond the individual cost, there was also the ongoing task of managing the triage rota — a managerial chore that required someone to stay on top of who was triaging and when, which again is not directly value adding and created friction for the business. The organisation wanted to experiment with AI in a measured way, mindful of the implications of rolling technology in at scale. That made a narrowly scoped, reversible pilot attractive.

We used Freshdesk for ticketing and Zapier to connect an AI assistant into the flow. The goal was conservative: automate the non‑value work of capturing context and suggesting routing, keep humans central to validation, capture structured metadata, and measure the outcome. The initial wiring took a few hours; most time was spent refining how the assistant summarised context and mapped categories to internal functions.

The Challenge

Two practical problems made this worth doing. First, triage was hidden, recurring labour that quietly ate time and attention. Second, the business wanted meaningful, low‑disruption experiments with AI that produced measurable results quickly. Operationally the pain was straightforward. Reading and reassigning tickets took time, agents lost flow when switching between inbox triage and deeper work, and response times depended on who was available. When rota management lapsed, tickets piled or landed with the wrong team. The brief deliberately excluded resolution: we would automate data capture and assignment suggestions only, not replace human judgement.

The Approach

We used a simple pattern: diagnose, automate a focused non‑value task, keep humans in the loop, measure, iterate.

• Diagnose: mapped the triage workflow, sampled tickets, and measured the average time spent reading, deciding and reassigning. We modelled annualised cost from that sample to set a baseline.
• Narrow scope: the assistant’s remit was limited to extracting context, proposing a product stream and category, and suggesting the likely owner. It appended a concise summary and rationale to the ticket rather than acting autonomously. That made the decision visible and contestable.
• Fast build and refine: connected the AI to Freshdesk via Zapier so new tickets could be read, classified and then summarised back into the ticket. The technology was quick to wire up; most effort was in tuning the instruction set and validating outputs against real tickets.
• Human in the loop: the assistant posted a one‑ or two‑sentence summary explaining why it recommended a route, giving the receiving agent immediate context and the chain of reasoning. Agents could accept the assignment, reassign it, or flag an error. Validation was therefore low friction and built into normal working practice.
• Tagging and measurement: each AI‑acted ticket received a tag; agents could flag incorrectly triaged tickets which added a second tag. Those tags fed simple reporting showing number of AI interactions and agent‑flagged error rates, providing measurable signals to reinforce ROI and to focus prompt refinement.
• Iterate from real work: we used live tickets to reveal edge cases rather than theorising from examples, allowing prompt adjustments to be grounded in actual practice.

The Payoff

The results were proportional and tangible.

• Time saved: on average about five minutes per ticket was reclaimed. That included reading, deciding and reassigning, plus the reduced context switching overhead. Five minutes isn’t dramatic per ticket, but at scale it mattered.
• Financial: annualised savings were roughly £6,000 against a cash outlay under £500, with payback measured in months. This included direct time saved and the secondary benefit of faster first responses because tickets landed with the right person sooner.
• Operational: distribution of workload became more consistent, single‑person bottlenecks eased, and agents spent less time on admin and more time on customer work. The AI’s structured metadata also created a tidy dataset for future improvement work — what had been opaque manual effort became queryable input.
• Low risk and visibility: because humans retained final decision authority, errors were visible and reversible. Tagging and agent feedback made failure modes observable and prioritised prompt updates.

The Reflection

This project shows what gets missed when leaders treat AI as only a strategic, heavyweight exercise. Useful AI work often looks small and tactical: focus on routine, high‑frequency admin tasks and you get two things at once — operational lift and structured data for learning. In this case the combination of a narrow remit and human validation reduced risk while delivering immediate benefit.

Importantly, the integration kept customer service human centred. The AI acted as an orchestrator, not a replacer. It surfaced context and a rationale so the agent who ultimately dealt with the customer understood why the ticket had been routed that way and could decide whether the triage was correct. That alignment with the company’s intention to keep people at the heart of service was critical — automation made the agent’s work cleaner, it didn’t distance them from the customer.

This wasn’t tidy automation that eliminated all manual work. Edge cases remained and required manual handling. That’s expected. The aim was to shift time from administrative friction into value‑adding customer work and to create data that makes the next set of improvements easier.

The Takeaway

Start small. Pick a routine, repeatable, non‑value task, automate the data collection and recommendation piece only, keep humans central for final decisions, and add a simple feedback loop that tracks AI interactions and error flags. Low‑cost, rapid experiments reduce organisational risk and accelerate learning far more effectively than waiting for the “perfect” project.

If you’re curious about how this pattern might apply in your organisation, get in touch for a chat.