The path to AI value starts long before the technology

Every board in the country is asking some version of the same question right now: where is our AI value? I hear it in almost every conversation I have with executive teams navigating transformation; a mix of urgency, hype, and, often, quiet frustration that the returns aren’t materialising as fast as the headlines suggest.

So, when I sat down with Wayne Andrews, CFO of the University of Sydney, and Dan Hart, GM of AI, for this episode of our podcast, Where Value Gets Delivered, I wasn’t expecting a masterclass in patience and people. I was expecting a masterclass in technology and platforms.

What I got instead was one of the most grounded, practical conversations I’ve had on this topic, because Wayne and Dan haven’t been chasing AI hype. They’ve been building an AI capability, quietly and deliberately, since 2019, and it now sits inside the Office of the CFO. That placement alone tells you something important about how they think about value: not as a technology outcome, but as a financial and service outcome that happens to be delivered through technology.

Here is what stood out to me as the lessons every executive team navigating this journey should take away.

Chase service, not dollars; the dollars follow

One of the most interesting insights came from Wayne's approach to investment.

Rather than leading with a traditional business case built around cost reduction, the team focused on improving service. Better experiences for staff and students became the objective, with financial returns viewed as a consequence rather than the primary driver.

That approach has since generated close to $150 million in cumulative savings.

For CFOs under pressure to justify every dollar of investment, this is a genuinely useful reframe: value follows adoption, and adoption follows a good experience. Lead with the ROI case alone, and you risk optimising for the metric instead of the outcome.

When people experience a meaningful improvement in how work gets done, adoption follows. When adoption grows, value becomes measurable.

Start small, and let momentum compound

There's a tendency to think AI transformation begins with an enterprise strategy or a significant platform investment.

The University took a different path; it began with a simple chatbot answering a single, high-volume finance question: Has my invoice been paid? because that was the most common query clogging up the finance team’s inbox. Small, specific, low-risk. From there, five robotic process solutions. Then, years later, a generative AI project that took a dense web of degree handbooks and turned them into machine-readable rules, cutting the time to process a student’s degree transcript from six months to five minutes.

That single use case alone, a project the team calls “complex degrees”, was fixing a backlog that meant one in five students weren’t graduating on time. It’s a reminder that transformation value doesn’t require a single big-bang platform decision. It compounds from a series of well-chosen, narrow bets.

That's something we see repeatedly across transformation programs. Organisations that realise value most consistently aren't necessarily the ones making the biggest investments first. They're the ones building organisational confidence through repeated delivery.

Each successful initiative creates trust, strengthens sponsorship and generates momentum for the next.

The hardest part isn’t the technology; it’s the people

Perhaps the biggest shift discussed wasn't about AI at all, it was about people.

Both Wayne and Dan were candid that the biggest shift in their thinking has been about capability, not code. The University originally hired developers and engineers. Today, the team is built largely from university graduates trained as much in consulting and relationship-building as in technical delivery; because as AI increasingly writes the code itself, the scarce skill isn’t technical execution. It’s the ability to build trust with the people whose jobs are being changed.

Their operating principle is disarmingly simple: don’t ask people why they do things the way they do. Just build what they ask for, play it back to them, and let them decide whether to change it. It removes the instinct every technical team has to walk in and “fix” a process; an instinct that, as Dan pointed out, usually ignores the context and history behind why a process exists in the first place. That single discipline is doing more to drive adoption than any tooling choice.

Redefine what “value” means before you measure it

Many AI business cases still rely on a familiar formula: time saved multiplied by labour cost. While useful, that model tells only part of the story.

Dan described how AI enables capabilities that simply weren't practical before, such as reviewing every transaction rather than sampling a small percentage during audit. The value isn't simply greater efficiency. It's stronger governance, greater assurance and better organisational decision-making.

If organisations only measure productivity gains, they'll consistently undervalue some of AI's most significant opportunities.

That conversation naturally extended to governance itself. One of the University's biggest shifts came through its relationship with internal audit. Initially, audit expected AI solutions to be perfect before they were deployed. The AI team reframed the discussion. The objective wasn't perfection; it was demonstrable improvement supported by appropriate governance, robust controls and continuous refinement.

That simple change transformed audit from an approval gate into an active partner in innovation.

It's a lesson many organisations could benefit from. Governance shouldn't be something that sits outside transformation, slowing progress through checkpoints and approvals. Done well, governance is part of the value creation process. It enables organisations to experiment responsibly, manage risk transparently and build trust as capability matures.

As AI becomes more deeply embedded across organisations, executive teams will need broader value frameworks that recognise improvements in governance, risk management, resilience, customer experience and organisational capability alongside traditional productivity measures. Those that embed governance into delivery, not around it, will move faster, make better decisions and realise more sustainable value over time.

AI fills the white space; it doesn’t replace the platform

Perhaps the most useful strategic framing in the whole conversation: even a well-invested enterprise system will only ever cover a fraction of what a genuinely complex organisation needs; Wayne put the university’s coverage at around 50%, even with heavy investment in core platforms. The opportunity for AI and automation isn’t to replace that investment. It’s to fill the white space between enterprise systems, and between those systems and the humans who use them. That reframing matters for any executive team currently debating a multi-year platform re-implementation versus a faster AI-led layer: it’s very rarely an either/or.

Build the demand engine, not just the capability

One of the most overlooked challenges in AI transformation isn't building capability, it's creating sustained demand for it.

Many organisations invest heavily in establishing a central AI or automation function, only to find the business slow to engage. The technology exists, the team is in place, but the pipeline of meaningful opportunities never materialises.

The University of Sydney approached this differently.

Rather than waiting for projects to emerge, finance operates much like an internal consulting practice. Relationship quality between finance and each part of the University is actively measured, creating a continuous flow of conversations about business challenges and opportunities for improvement. That close partnership ensures the AI team is solving real problems, not simply promoting technology.

At the same time, Dan's team invests in building capability across the organisation. Staff are encouraged to develop their own simple automations and gain hands-on experience with AI. Counterintuitively, this hasn't reduced demand for the central team, it has increased it. As people's understanding grows, so does their ability to recognise more complex opportunities where specialist support adds the greatest value.

There's an important lesson here for every transformation leader. Sustainable AI capability isn't something organisations build within a single team. It's something they cultivate across the business.

The organisations that create lasting value don't simply invest in technical capability. They invest in curiosity, relationships and problem-solving capability throughout the organisation. In doing so, they create a self-sustaining cycle where greater understanding generates better ideas, better ideas create greater demand, and that demand drives the next wave of innovation and value.

The takeaway for every transformation leader

What struck me most after our conversation was how familiar the underlying lessons felt.

The organisations creating the greatest value from AI aren't necessarily moving the fastest, they're moving deliberately.

They define value before selecting technology. They build capability before scaling it. They invest as much in relationships as platforms. And they understand that transformation is rarely delivered through one breakthrough initiative, but through hundreds of well-executed decisions that build on one another over time.

AI hasn't changed the fundamentals of transformation.

If anything, it has reinforced them.

Value still comes from solving meaningful problems. Bringing people with you. Measuring what matters. And building capabilities that continue creating value long after implementation.

That's exactly why we created Where Value Gets Delivered: to share practical lessons from leaders who are doing the work, so other organisations can move beyond AI experimentation and build lasting organisational value.

You can listen to the full conversation with Wayne Andrews and Dan Hart below.

Written By

Louise Langridge

APAC Managing Director, Org Advisory and Technology Services

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