Turning AI ambition into business impact

Insights from the CommBank Accelerate AI Conference, ICC Sydney.

The conversation around AI has shifted. For many organisations, the question is no longer whether to adopt AI, but how to move beyond experimentation to measurable business impact. The risk is not a lack of ambition, but the challenge of translating that ambition into practical, scalable outcomes that are supported by trusted data, clear governance and operating model change.

The recent CommBank Accelerate AI Conference in Sydney brought together business leaders to explore how organisations are progressing from early AI adoption to more scaled, practical application. Discussions across banking, retail, legal and technology leaders highlighted a consistent theme - AI advantage is no longer linked to access to tools, but to how organisations apply these capabilities to their own data, workflows and customer problems. As adoption matures, the focus is increasingly on execution, alongside the governance, capability and organisational alignment required to support it.

Moving beyond pilots to scaled adoption

Across discussions, there was a noticeable shift away from isolated pilots and organisations are now rolling AI out more broadly and in a measured way.

Progress is coming from embedding AI into core processes early, rather than keeping it at the edges and the organisations moving fastest are using early deployments to learn, strengthen controls and build confidence in where AI can be safely scaled.

Examples shared, including fraud rule automation and customer-facing tools, showed how AI is being built into core processes. This includes supporting decision-making, managing risk and improving interactions with customers, and are increasingly becoming part of how organisations operate day to day.

A consistent point raised by speakers was the value of starting early and learning through use, with early deployments helping organisations build capability, understand limitations and establish governance and delivery models that can support scaled adoption.

Competitive advantage depends on data, context and execution

The technology itself is no longer the differentiator. Advantage is increasingly coming from how organisations apply AI to their own business context, proprietary data and customer or operational problems.

Several examples pointed to the role of proprietary data. For example, some retailers are using store-level data to improve operational decisions and customer experience, while professional services firms might be seeing AI influence how work is delivered and how performance is assessed.

This is leading to changes in how work is structured, as organisations simplify workflows and remove unnecessary steps, rather than layering AI on top of existing processes. At the same time, there is a deliberate focus on where people still play a critical role, particularly in more sensitive or complex situations such as complaints, advice or risk decisions.

This reinforces a broader point across BDO’s data and AI insights: organisations need to know, govern and use their data effectively before they can expect AI to deliver repeatable value. Without trusted data foundations, AI initiatives are more likely to remain fragmented, difficult to scale and harder to defend.

Leading organisations are treating this as an operating model shift, rather than a technology uplift, while less mature approaches continue to layer AI onto existing processes, limiting the value realised.

Balancing speed with trust and governance

While there is strong momentum behind AI adoption, there is equal focus on trust as governance, privacy and accountability are being addressed alongside deployment.

Leading organisations are operationalising this through formal governance frameworks, rather than relying on informal controls or post-deployment review. This is particularly important in advisory and customer-facing use cases, where expectations around privacy and responsibility are high.

Communication is also critical, as organisation spend more time explaining how AI is being used, both internally and externally, to build confidence among both employees and customers.

The tension between speed and assurance can be a challenge, as moving too slowly risks falling behind, while moving too quickly can undermine trust but organisations managing this well are doing so by focusing on lower-risk areas first, while progressively strengthening governance as adoption expands.

Workforce capability and change management

The workforce impact of AI was a recurring theme, with organisations investing in capability so teams can work effectively with AI-enabled systems.

This often involves breaking roles down into tasks and identifying where AI can support or augment the work being done as well as accelerating a shift away from role-based structures towards more task and capability-based workforce models.

Speakers acknowledged the level of uncertainty across the workforce, and highlighted the importance of communication, clarity and empathy, particularly as roles evolve. AI can take on more repetitive and persistent tasks, while there is greater focus on oversight, system management and the application of judgement.

This is also raising questions about how performance should be measured, particularly in business models that have traditionally been based on labour metrics, with increasing pressure to align performance with outcomes, quality and decision impact rather than effort alone.

Reframing value creation

A question that came up repeatedly was where AI will generate long-term value and revenue.

The organisations seeing the most progress are focusing on specific problems, rather than broad programs without a clear purpose. Examples discussed included fraud prevention, improving customer outcomes and supporting faster, more informed decisions. These are targeted applications aligned to business outcomes - reducing cost, improving risk management and enhancing customer experience - rather than AI deployment for its own sake.

This is also starting to influence business models, particularly in service-based organisations as some of the assumptions around labour, cost and productivity are being challenged. At the same time, there is growing interest in how AI could support new types of service delivery over time, rather than simply improving existing ones.

What this means for executives

For executives navigating AI adoption, the shift from experimentation to execution creates a more practical question: where will AI create measurable advantage, and what needs to change across data, governance, workflows and capability to support it?

The answer starts with identifying where value sits across internal data, operating context and customer understanding, then prioritising use cases that can be deployed, governed and measured in a disciplined way.

This also places greater scrutiny on the controls, evidence and frameworks required to support trust as AI becomes more embedded in decision-making and customer interaction.

In practice, this is translating into a growing range of applications across both front and back-office functions. From supporting proposal development and enhancing fraud detection, through to enabling more personalised customer experiences, scenario planning and internal communications, AI is being applied in ways that are practical, scalable and increasingly integrated into core business activity.

For boards and executive teams, this means AI should be treated as a business transformation agenda, not a standalone technology program. The organisations that make the strongest progress will be those that align data foundations, risk settings, workforce capability and commercial priorities from the outset.

How BDO can help

BDO’s digital advisory team works with organisations, including those in financial services, to take a practical, outcome-focused approach to AI.

We support boards, executives and teams to identify where value sits, prioritise use cases that can be scaled, and put in place governance and ways of working that support consistent use.

Our focus is on helping organisations move from AI ambition to business impact, supported by clear accountability, risk settings and organisational alignment.

Contact our team to discuss how your organisation can take a more practical approach to AI adoption.