7 AI use cases that are already transforming the Contact Centre in 2025

AI in the contact centre is no longer a question of if, but where to begin. In our recent webinar with Jimmy Hosang, CEO and Co-founder of Mojo CX, we explored seven practical, high-impact AI use cases that are already delivering returns in real-world operations. From automating wrap-up notes to exploring full voice AI, the conversation cut through the hype to focus on what’s truly working – and what’s coming next.

From productivity savings – and easy wins – to value generation, here we summarise each of the seven use cases, their benefits, pitfalls, and what it takes to make them work.

1. Autowrap / Call Summarisation

This is one of the most immediate and measurable wins for AI – and it’s relevant to every contact centre, whether procedural or regulatory. With AI transcribing and summarising calls, wrap time is reduced by 50% and average handling times by 5–15%. In a 200-seat contact centre, at 10%, that’s equivalent to freeing up 20 full-time agents.

It’s an easy sell for operations leaders: the 2-3X ROI is immediate, the data is clean (and doesn’t need complex integrations, a simple copy/paste will do to start), and the impact on agent workload is obvious. What you do with the benefit is up to you; save the 20 FTE through natural attrition, reduce wait times, improve service. Less typing, less admin, more time for real conversations.

2. Auto QA (Quality Assurance)

Manual QA processes typically only cover 1-2% of calls. With AI-powered auto QA, every conversation can be transcribed and assessed, increasing both coverage and scorecard accuracy, with the potential to reduce QA overhead by 75%. Once the model reaches high accuracy (which can be achieved in four weeks or less), it enables a rethinking of QA resourcing. Where teams can reinvest those hours into value-adding activities like deep-dive analysis or real-time speech insights.

What’s more evaluation consistency is likely to see an immediate uplift, as is agent performance through real time feedback.

3. Auto Coaching

Team leaders spend 60-80% of their buried in fragmented data or playing detective to understand performance issues. Auto coaching can bring together call data, performance stats, and behavioural insights into one view – streamlining prep time and allowing leaders to focus on actual coaching.

From an efficiency perspective, this facilitates a shift in manager-to-agent ratios from 1:12 or 1:15 to something closer to 1:18 without losing effectiveness. But beyond that, coaching quality and consistency improve and agent development is more pointed and expedited. It also unlocks the potential for automated role play both on the job and in grad bays. This provides the basis then for both greater job satisfaction among both managers and agents, as well as delivering higher quality interactions throughout the operation. All of which have an impact on broader measures such as agent attrition, CSAT and brand perception.

SIDE NOTE: While those first three use cases focus a lot on the potential for reduction in headcount, it’s often more about doing better work, not just less work. Think: HITL (Human in the Loop), not human out of the picture.

4. Identifying Vulnerable Customers

This is where AI starts playing a key role in risk management and regulatory compliance. Agents can’t always be relied upon to spot vulnerability signals in real time – especially when they’re under pressure to do many things at one in a short space of time. AI can listen in and flag when it detects signs of vulnerability, alerting the agent in the moment and ensuring the right customer journey is followed.

The benefit? Reduced regulatory risk, better outcomes for vulnerable customers, and more confidence in compliance reporting. This use case also pairs naturally with summarisation – capturing the right context and actions in the CRM.

5. Agent Assist

Beyond risk management and efficiency, AI also enables agents to add value in the moment. Agent Assist tools analyse the live conversation and suggest actions – whether it’s handling a low-value enquiry quickly, spotting a sales opportunity, or guiding a customer toward a better outcome.

This is where things get exciting. AI is no longer just reducing cost – it’s helping unlock customer lifetime value and improving journeys. It’s also a mindset shift: from cost centre to value driver.

SIDE NOTE: The constant push for self-serve may well be eroding brand loyalty, where a great conversation with an agent isn’t only about making a sale or solving a query, it’s an experience that plays into customer brand perception.

6. Hands-Free Conversations

Imagine an agent who doesn’t have to type, click around systems, or juggle tabs – just talk and listen. That’s the promise of hands-free conversations. With AI handling navigation, form filling, and admin tasks, agents can give customers their full attention.

It’s not just about productivity, it’s about truly human interactions that focus solely on the customer. How satisfying would that be? It could change the type of people you hire and shift expectations around what great service looks like.

7. Full Voice AI

Everyone’s chasing the holy grail: fully autonomous AI voice agents. Why? 24/7 customer contact, instant routing, and scalable service without scaling headcount.

But Jimmy’s message was clear – don’t rush it, though do keep your eyes on the prize. Build your maturity and path to value with easier use cases, underpinned by the right data and processes. This isn’t about flipping a switch – it’s about a journey to transformation.

Final Thoughts: Think "value first, tech second"

Across every use case, the AI you deploy is about outcomes. Whether that’s saving time and cost savings, improving job satisfaction or deepening customer relationships, AI only succeeds when it’s introduced with purpose.

Start small. Pick the use case with the clearest ROI. And don’t be afraid to move fast – but move smart.

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