Finding the ROI on AI in your contact centre

How to overcome AI adoption barriers in your contact centre

Over the past few years, AI has moved quickly from experimentation to expectation across Australia and New Zealand. What began as innovation pilots and exploratory trials is now an ongoing boardroom agenda item. In 2026, the focus has shifted from what AI could do to what it has done, and the tangible returns it can demonstrate.

We’re currently living in the ROI paradox phase of adoption. Organisations are adopting AI to improve customer and employee experiences at record rates, yet many struggle to report measurable impact. Globally, only one in four AI projects deliver the ROI initially expected (IBM).

In Australia, the pressure to show ROI is growing. 97% of CFOs now require regular reporting on AI-driven cost savings and productivity improvements (CFO Tech), while 62% of CEOs expect returns within one to three years (KPMG).

Senior business leaders in New Zealand face a similar challenge, with 97% reporting increased pressure to deploy AI, largely driven by executive leadership (Cisco). However, 43% of New Zealand CEOs believe it may take three years or more for returns to be realised (KPMG).

At Synergy, we believe waiting years to show ROI is too long. With the right approach to your contact centre AI strategy, you can bypass the long wait and identify quick wins that show value in months, not years.

The reasons behind the disconnect

Moving AI from experimental pilots to operationalising AI is rarely a straightforward process. Most organisations hitting the ROI wall are facing three common hurdles:

The talent trap

Over half of Australian businesses cite a shortage of AI-ready talent as their primary barrier. They have the tools, but not enough people who know how to weave them into a business process.

The data trap

AI is only as good as the data it uses. Fragmented, siloed, and inconsistent data prevent AI from operating with the single source of truth it needs for reliable, autonomous decision-making.

The feedback

Most AI systems today are limited in their ability to learn and retain. As MIT’s State of AI report highlights, AI struggles to improve over time without human intervention.

At the same time, organisations face intense pressure to demonstrate ROI quickly, creating a strong push to move fast. But speed can introduce significant implementation risks.

For example, when global fintech brand Klarna replaced customer service roles with AI agents, the company faced a backlash over declining service quality. By May 2025, CEO Sebastian Siemiatkowski admitted that cost-cutting had been prioritised too heavily over quality, and the company began rehiring human agents for remote support.

So, how do you meet the pressure to show results without falling into the lower quality trap?

Start where you can measure

The mistake we see many leaders make is to approach AI as a significant digital transformation from day one. While it may eventually lead to that, it doesn’t need to begin on a large scale, especially when you need to show measurable AI results quickly.

A practical starting point is to choose an area where you can show a clear, measurable before-and-after. “Start somewhere where you have strong baseline metrics. That way, you can measure the improvement and your ROI,” recommends Simon Shanks, General Manager at Synergy Enterprise Solutions. “Use cases with defined scope and measurable impact move much faster through approval and funding cycles.”

For many organisations, the contact centre is the perfect place to start. It is one of the most metric-heavy parts of any business, already tracking agent productivity metrics such as Average Handle Time (AHT), wrap-up time, and First-Contact Resolution (FCR). Because performance is already quantified, improvements are easier to validate.

Example 1: Reducing after-call admin

Traditionally, once an agent ends a call, they spend several minutes writing notes, summarising the conversation, and manually directing the next steps in the CRM. With AI, much of this process can be automated.

• The AI capability: The AI listens to the conversation, summarises the call, and suggests (and even executes) the next actions based on defined procedures.
• The improvement: A task that used to take several minutes now takes seconds.
• The ROI: Multiply the minutes saved by the number of calls a day, across the number of agents, at your average salary rate. Now, you have a tangible dollar figure to show your board.

Example 2: Accelerating new agent ramp-up

New agents often take weeks or months to reach full productivity. During this period, they rely heavily on supervisors, place customers on hold while searching for information, and escalate more complex interactions. AI-enabled real-time guidance changes this dynamic.

• The AI capability: Relevant knowledge articles, suggested responses, and next-best actions are identified during live conversations, based on context.
• The improvement: Agents build confidence faster, reduce errors, and handle interactions independently much sooner.
• The ROI: Even a modest reduction in time to competency — for example, two weeks per new hire — can produce measurable gains. When applied across annual recruitment volumes and combined with reduced supervisor support time, the productivity gains are immediate and measurable.

Example 3: Automating quality assurance

Quality assurance is traditionally manual and sample-based. QA teams review only a small percentage of interactions, limiting visibility and requiring substantial effort.

• The AI capability: With AI, every interaction can be automatically analysed against defined quality and compliance criteria. Risks and coaching opportunities are identified in real time.
• The improvement: This shifts coverage from a small sample to 100% of interactions, while reducing manual review time. Coaching becomes more consistent and targeted.
• The ROI: The return comes from time saved in manual reviews and reduced exposure to compliance risk. Even small lifts in quality scores or customer satisfaction, when applied across total interaction volumes, can translate into meaningful financial returns.

Tap into the AI capabilities you already have access to

To see those early wins, you don’t need to build an AI system from scratch. Many of the systems you already use — and others readily available in the market — now come with built-in AI capabilities that can be quickly activated to cover common, repeatable tasks.

If you need customisation, a partner like Synergy Enterprise Solutions can build the specific APIs and workflows that allow that AI to work within your existing systems. “Turnkey capabilities can cover up to 80% of the work for some common processes,” says Simon. “We can take care of the 20%, and this is often much easier and more cost-effective than building solutions from scratch.”

The path forward

Operationalising requires a structural shift, but it doesn’t have to be overwhelming. You don’t have to wait 28 months to see a return.

By focusing on high-volume, metric-rich areas like the contact centre and leveraging existing turnkey tools, you can move past the experimentation phase and start making a real impact.

Find your baseline, apply the right tool, and the return will follow.

Ready to stop experimenting and start measuring? Contact the team at Synergy Enterprise Solutions to identify quick ROI wins with your existing systems.