The average support team spends 60–70% of its time on tickets that follow the same resolution pattern, over and over. Password resets. Order status queries. Basic how-to questions. Billing clarifications.

These tickets are not challenging. They are not interesting. And they are not a good use of your agents' skills.

When AI handles this volume, something interesting happens: your team does not just get faster — they get better. Here is what that looks like in practice.

Where the 38 Hours Come From

Across TR-AI deployments, teams with 5–10 agents consistently report saving 38 or more hours per week once automation reaches 80% of L1 volume. Here is the breakdown:

Ticket handling time saved: If each agent handles 40 L1 tickets per day at an average of 6 minutes each, that is 4 hours per agent per day spent on automatable work. Across a 5-person team, that is 20 hours per day — 100 hours per week.

At 80% automation, 80 of those 100 hours are reclaimed. Some of that time goes to handling the remaining 20% of L1 tickets more thoroughly. The net saving is typically 35–45 hours per week for a team of five.

Queue management time saved: Agents spend significant time triaging, categorising, and routing tickets. AI handles this automatically, saving 30–60 minutes per agent per day.

Context-switching time saved: Constantly switching between simple and complex tickets is cognitively expensive. When agents work primarily on complex cases, they maintain focus and resolve them faster.

What the Best Teams Do With the Time

Reclaimed time is only valuable if it goes somewhere useful. The support teams that see the biggest business impact from AI automation are intentional about how they redeploy agent capacity.

Proactive Customer Outreach

Instead of waiting for customers to report problems, agents can monitor for early warning signals and reach out before issues escalate. A customer who has not logged in for 30 days might be at churn risk. An account with three failed payment attempts might need a quick call.

Proactive support dramatically improves retention — and it is only possible when agents are not buried in reactive ticket volume.

Deep-Dive on Complex Cases

Complex tickets — the ones that genuinely require human judgement, technical expertise, or relationship management — deserve more time than they typically get. When agents are not rushing through a queue of simple tickets, they can spend 30–45 minutes on a genuinely difficult case and actually solve it properly.

This reduces escalations, improves first-contact resolution rates, and builds customer trust.

Knowledge Base Development

The agents who handle tickets every day know exactly where the knowledge base is incomplete. They know which questions come up that are not well documented. They know which articles are outdated.

With time freed from routine tickets, agents can contribute to knowledge base development — improving the AI's accuracy and reducing future ticket volume. It is a virtuous cycle. Our guide on building a knowledge base that powers AI ticket resolution covers exactly how to structure that contribution for maximum impact.

Customer Success Activities

For B2B support teams especially, agents can shift toward customer success activities: check-in calls, product adoption reviews, feature education. These activities directly impact renewal rates and expansion revenue.

Support becomes a revenue-generating function rather than a cost centre.

The Agent Experience Transformation

Beyond the business metrics, AI automation changes what it feels like to work in support.

Repetitive work is a primary driver of agent burnout. When every day is a queue of identical tickets, motivation drops, quality suffers, and turnover increases. The average support agent tenure is 12–18 months — a reflection of how unsustainable the current model is. We cover the full data behind this in our post on why support agent burnout is a structural problem — and how AI fixes it.

When agents spend their days on genuinely interesting problems — complex troubleshooting, relationship management, proactive outreach — job satisfaction improves. Tenure increases. And the institutional knowledge that experienced agents carry stays in the organisation longer.

Managing the Transition

The shift from reactive ticket-handling to higher-value work does not happen automatically. It requires intentional management.

Redefine success metrics. If agents are measured purely on ticket volume, they will feel threatened by automation. Shift metrics toward quality: CSAT scores, complex case resolution rates, customer retention in their accounts.

Invest in skill development. Agents who have spent years on L1 tickets may need coaching to handle complex cases confidently. Invest in training during the transition period.

Communicate the vision clearly. Agents need to understand that AI is handling the work they found least rewarding — not replacing them. The teams that communicate this well see much smoother transitions.

Measuring the Impact

Track these metrics before and after AI deployment to quantify the productivity impact:

• Agent utilisation rate — percentage of time spent on value-adding activities vs. routine tickets
• Complex case resolution time — are agents resolving difficult tickets faster?
• CSAT for complex cases — is quality improving on the tickets that matter most?
• Agent satisfaction score — are agents happier in their roles?
• Voluntary turnover rate — are you retaining your best people longer?

The teams that track these metrics consistently report significant improvements across all five within the first six months of deployment.

Ready to see what your team could do with 38 hours back? Book a demo and we will show you exactly how it works.