AI CX ambitions outpace readiness for Australian firms

Australian organisations are increasing investment in artificial intelligence for customer experience, but many are still early in maturity and face constraints in data, skills and workforce readiness, according to research commissioned by systems integrator Kytec.

The study surveyed 306 Australian employees involved in customer experience, customer relationship management and AI initiatives at medium and large organisations. It highlights a growing gap between ambition and operational readiness as businesses pursue faster service and lower costs.

Most respondents said their organisations had established, or were developing, clear AI goals for customer experience. In total, 85% reported that direction of travel. Budget expectations also pointed upward, with 69% saying their customer experience technology budgets were likely to increase in 2026.

Confidence in outcomes was also high. The research found 58% of respondents were very confident they would achieve their AI ambitions, and 78% expected AI systems to deliver accurate, reliable results.

Readiness gap

Despite that optimism, many organisations are still laying the groundwork. Almost half of respondents (49%) said their organisations were in the early stages of AI maturity. Only 16% reported having a unified view of the customer, a common prerequisite for consistent personalisation and service across channels.

Workforce preparedness emerged as a key constraint. Only 20% of employees said their workforce was very prepared for increasing AI use, while a similar share said it was not prepared at all.

Sentiment was also mixed: more than 60% reported concerns about the growing use of AI in their roles. Only one in four said they were very confident their organisation had the capability to fully leverage AI in customer experience.

Kytec CEO David Okulicz said organisations are adopting tools that assist frontline teams, but more autonomous approaches remain uncommon.

"We are seeing - and the research supports this - strong demand for AI-based productivity capabilities that assist human agents supporting their customers. However, the implementation of true agentic AI capabilities is still very limited, and that's where the major benefits will ultimately be unlocked," Okulicz said.

Efficiency focus

The survey suggests organisations often frame AI in customer experience around operational targets. Speed and efficiency was the primary goal for 56% of respondents, while 49% prioritised reduced operational costs. Enhancing service quality and consistency ranked behind at 46%.

That emphasis is reflected in reported deployment patterns. Businesses are using AI to reduce call handle times and improve operational metrics, while placing less weight on revenue-led uses. Only 31% said their businesses aimed to differentiate their brands through deeper personalisation.

Concerns about trust and risk also featured. Some 51% of employees cited fears that AI use in customer experience could damage customer trust and confidence, while 41% pointed to the risk of harm to brand and reputation.

Training and communication

The research linked part of the confidence gap to internal communication and training. Just over a third of businesses had communicated their AI strategies to teams and updated job descriptions and responsibilities to reflect AI adoption.

Employee readiness was cited by 44% as the chief barrier to scaling AI, but fewer than half currently provide AI training. That mismatch suggests some organisations may struggle as they move from planning to delivery.

Okulicz urged organisations not to wait for perfect foundations before deploying, while recognising data and integration constraints.

"Unfortunately, many organisations are still caught up in building the foundational elements required to reach their AI ambitions. Our view is that more focus should go into implementing possible use cases now, while the foundations for more advanced capabilities continue to be developed."

He pointed to contact centre technology platforms as a source of pre-built functionality, while noting that data conditions would determine which projects were viable first.

"Within CX - particularly in the contact centre - CCaaS platforms already have the capability to deliver agentic AI 'out of the box'. The key is identifying and building the right use cases to take advantage of it," Okulicz said.

He added: "Not every use case will be ready immediately due to challenges around data unification and integration. However, organisations should focus on simpler use cases first. These can help drive progress on data unification while also building confidence in AI and the capabilities of their teams.

"Ultimately, success will depend on execution rather than aspiration. The challenge now isn't ambition - it's identifying the right use cases that will drive the integration and readiness activities needed to scale," Okulicz said.