The rise of autonomous agents: How AI is reshaping the banking sector in Australia and New Zealand

By Jeremy Thomas, Regional Sales Director at Backbase

The banking sector in Australia and New Zealand is undergoing a transformation driven by Artificial Intelligence (AI), with organisations rapidly integrating AI-powered solutions into their operations.

According to consulting firm Bain, 59% of banks globally are already exploring generative AI for customer service applications, while 55% are leveraging it for software development. However, the industry is now moving beyond initial experiments, embracing agentic AI, which promises to redefine banking operations.

From experimentation to implementation

The banking sector’s initial foray into AI began with coding assistance tools such as GitHub Copilot, primarily for internal use. Cautious adoption led to the introduction of chatbots designed to handle routine customer queries.

However, the landscape is shifting. The advent of autonomous AI agents is enabling banks to streamline operations, enhance customer interactions, and increase revenue through personalisation and automation.

Initially viewed as a tool to improve call centre efficiency, AI has now evolved into a strategic asset. Banks are finding that AI agents can significantly reduce labour costs, simplify banking processes, and enhance customer service. By minimising administrative burdens and accelerating data processing, AI is poised to transform financial services. 

Unlocking AI’s full potential

Despite AI’s promise, its effectiveness hinges on a bank’s ability to integrate AI agents into its broader digital infrastructure. Engagement banking platforms are crucial in this regard, providing a unified environment where AI agents can access data and workflows in real-time.

These platforms allow seamless AI integration across multiple banking functions, from customer service to mortgage lending.

For example, AI-powered agents can collect and verify customer documents, assess creditworthiness, and connect all relevant stakeholders, thereby streamlining the lending process.

Without a centralised platform, banks may struggle to harness AI’s full potential, leaving them at a competitive disadvantage. 

The ‘build vs. buy’ debate

A key question for banks is whether to develop proprietary AI tools or invest in pre-built solutions. The answer often lies in a hybrid approach. Organisations can start with ready-made AI solutions to accelerate deployment while simultaneously developing customised tools to address unique business needs.

An engagement banking platform plays a crucial role in this strategy. It provides access to pre-configured AI capabilities such as Customer Lifetime Orchestration to enhance customer acquisition and product activation. Enabling banks to build specialised workflows, this flexible approach allows organisations to innovate rapidly while maintaining control over their AI-driven operations. 

The cost of inaction

While early adopters are already reaping the benefits of AI, laggards risk falling behind. Though some organisations may not yet feel the impact, the competitive gap is widening. Banks that fail to invest in AI now may struggle to keep pace with technological advancements and shifting customer expectations.

The window for experimentation is closing. Institutions that do not develop a structured AI strategy will find it increasingly difficult to compete with frontrunners who are already refining their AI-driven processes.

Fortunately, the cost and complexity of AI implementation have declined, making this the ideal moment for banks to take decisive action. 

Addressing internal resistance

Despite AI’s advantages, some banking professionals remain sceptical. Concerns about job displacement and reduced human interaction persist. Overcoming these challenges requires a proactive approach from leadership.

Executives should familiarise themselves with AI technologies before guiding their teams through the transition. Encouraging staff to experiment with AI in their workflows can demystify the technology and highlight its benefits. By fostering a culture of innovation, banks can ease the adoption process and maximise AI’s impact. 

Key considerations for AI adoption

For banks looking to implement AI effectively, several factors must be addressed. These include:

1. Cultural readiness: Leadership must promote an open-minded approach to AI. Employees should view AI as an enabler rather than a threat, focusing on its potential to enhance productivity and customer experience.
2. Unified digital infrastructure: Implementing an engagement banking platform will ensure seamless AI integration across departments, enabling real-time data access and process automation.
3. Incremental implementation: While the potential of AI is vast, banks should take a phased approach to adoption. Starting with targeted use cases allows for smoother implementation and greater long-term success.
   

The Road Ahead

AI’s role in banking is no longer speculative – it is a necessity. The industry is shifting from AI experimentation to full-scale deployment, and institutions that hesitate risk obsolescence.

By embracing AI-driven solutions, banks can enhance operational efficiency, provide superior customer experiences, and maintain a competitive edge in an increasingly digital financial landscape.

As AI continues to evolve, so must banking strategies. A well-executed AI roadmap will not only future-proof institutions but also unlock new opportunities for growth and innovation in the financial services sector.