When the Room Agrees:
AI is Reshaping Finance — But Only If Your Foundation is Ready
There are conferences where you hear ideas you've never encountered before. And there are conferences where the room puts language to things you've been seeing in practice — where the conversations happening on stage mirror almost exactly what you've been building with clients behind the scenes.
Today's Gartner Finance & Accounting Symposium in Sydney was firmly the latter. I attended a standout panel session that brought together three of the sharpest finance leaders in the room: Matt Clifford, GM Finance Systems & Data at News Corp Australia, Winny Puthussery, Head of Group FP&A at Metcash, and moderated with precision by Jason Codespoti, CFO at IBM Australia.
The topic was AI and the future of finance. But the conversation that unfolded went far deeper — into culture, data architecture, transformation sequencing, and what the finance function actually looks like in 2030. What made it particularly resonant for us at Octane: all three organisations on stage are running IBM Planning Analytics (TM1) as their planning and forecasting platform.
This post captures the key themes from the session — and connects them to the work we've been doing with News Corp on their own Planning Analytics modernisation journey.
"It wasn't the technology that held us back. It was our data. We had to go back and rearchitect from the data layer up."
— Matt Clifford, GM Finance Systems & Data, News Corp Australia · Gartner Finance Symposium 2026
Winny opened with a line that drew knowing laughter across the room: he'd already run 15 budget scenarios that week. It was Tuesday.
It landed because every finance leader in that auditorium recognised the feeling. The panel's first theme was clear: the nature of financial planning has fundamentally changed. The question finance teams are now being asked is no longer "what happened last month?" It's "what happens if I change X — and simultaneously, what happens if I change Y?"
This shift from retrospective reporting to real-time, forward-looking scenario analysis isn't a trend — it's the new baseline. And it has enormous implications for both the tools finance teams use and the processes they build around them.
The panel cut through the AI hype quickly. Winny framed it well: there are two distinct buckets of AI value in finance right now, and conflating them is a source of a lot of frustration.
The Two Lanes of AI Value in Finance
Lane 2 — Foundation Required: The larger prize — connected forecasting, automated variance analysis, AI-driven scenario modelling — but only accessible once your data is structured, governed, and connected across systems. This is where the hard yards are. And this is where most organisations are still working.
Matt's point on Lane 2 was the most candid moment of the session. News Corp had early, ambitious AI use cases ready to deploy — but when they tried to turn them on at scale, the platform wasn't what held them back. It was the data. The structure, the shape, and the care given to that data over the years hadn't been sufficient to make it consumable by AI. The response: a deliberate rearchitecting of the data layer from the ground up.
This is something we see consistently across enterprise planning environments. The TM1 platform is rarely the constraint. It's the state of the data beneath it.
Matt's contribution to this panel carried real weight because it wasn't theoretical. News Corp has been on a live modernisation journey — and Octane has been privileged to be part of it.
The challenge News Corp faced isn't uncommon. A large, complex media organisation — multiple mastheads, traditional print alongside digital, broadcast assets — each with their own planning models, data structures, and forecasting rhythms. The legacy TM1 environment had served them, but it had grown organically over time. Connecting it, governing it, and making it ready for the next generation of AI-driven finance capability required a deliberate platform investment.
35–40 Hours saved per reporting cycle post-modernisation
5–10× Force multiplier on FP&A team capacity with AI orchestration
3 → 1 Planning platforms consolidated onto IBM PA
What the modernisation unlocked wasn't just faster reporting — it was the ability to shift the finance team's focus. Instead of spending the majority of their time moving and formatting data, analysts could direct their energy toward the strategic analysis and scenario modelling that actually drives business decisions.
As Matt described on stage, the goal for News Corp's planning function isn't just automation — it's enabling one person to coordinate work that previously required many. Agents are doing multiple jobs in parallel. The human role is shifting from data handler to decision architect.
"We now have the opportunity to have one person coordinating, where we used to have many. AI gives us that scale multiplier — but only if the platform beneath it is solid.
—Matt Clifford, GM Finance Systems & Data, News Corp Australia · Gartner Finance Symposium 2026
One of the most striking observations from Winny Puthussery was about a structural shift in how organisations view data. He noted that Metcash recently combined its data and AI roles into a single Chief Data and AI Officer position a clear signal that the two disciplines are inseparable.
The reason is straightforward: AI is only as intelligent as the data it operates on. You can deploy the most sophisticated planning and forecasting AI available, but if your data lives in silos, lacks governance, or hasn't been structured with cross-system connection in mind, the AI will surface that fragility immediately.
For Planning Analytics environments specifically, this means the investment in data architecture, model design, and dimension hygiene that might once have felt like internal IT housekeeping is now a direct enabler of competitive advantage. The organisations getting the most value from their TM1 environments today are the ones that treated data quality as a strategic priority before AI arrived at the door.
The panel was unanimous on the transformation strategy. The days of the two-year ERP-style rollout are over, not just because the pace of change makes them obsolete, but because organisations that wait that long are already 18 months out of date by the time they go live.
Matt's framing was practical and repeatable: identify the highest-value use cases, execute them well, demonstrate ROI, build trust, and roll that credibility into the next project. This incremental approach isn't a compromise; it's the methodology that actually sticks because it creates internal champions at every stage.
The Transformation Sequencing Principles — As Heard on Stage
Pick use cases where the win is visible, and the value is unambiguous — workforce planning, forecast cycle time, reporting automation
Invest in the people who know your business and make them owners of the change — not passengers through it
Frame AI as a force multiplier, not a cost-cutting exercise. If your team can do 5–10× more, the ROI conversation changes entirely
Stabilise before you modernise — a well-governed, performant foundation is the prerequisite for everything that comes next
Don't wait for perfection. The tools are improving month by month. Start with what you can manage, and grow from there
Perhaps the most important conversation of the session wasn't about technology at all. It was about people.
Both Matt and Winny acknowledged that the emotional reality of AI in finance — the nervousness, the questions about job security — can't be glossed over. It has to be addressed directly, because that baseline concern is the barrier to even having the conversation about what's possible.
The reframe that resonated most: right now, many highly skilled finance professionals spend 80% of their time moving data around and 20% doing the strategic work they were trained and hired to do. AI inverts that ratio. And when finance teams start experiencing that inversion — when they find themselves spending their day on scenario analysis, business partnering, and strategic insight rather than data gymnastics — the culture shifts naturally.
Jason closed the session with a line worth repeating: "Treat AI as a partner, not a threat. The people who do that well will define the next era of finance leadership."
The panel's closing question asked each participant to look five years ahead. The picture that emerged was consistent across all three voices.
Finance becomes the decision-making backbone of the organisation. Not the back office. Not the reporting function. The team that spends almost no time on data preparation and almost all their time providing the scenario analysis, risk modelling, and strategic input that underpins major business decisions.
The path to that outcome isn't mysterious. It runs through a modern, well-governed planning platform. Connected data. An AI layer that can operate with trust and explainability. And a team that has been developed to be business partners first, data operators second.
For organisations running IBM Planning Analytics (TM1), the foundation is already there. The question is whether it's been built, maintained, and evolved in a way that makes it ready to carry that future.
We help finance teams modernise their IBM Planning Analytics / TM1 environments and build the data and platform foundations needed to leverage AI at scale. If you'd like to discuss where your environment sits and what the path forward looks like, we'd welcome the conversation.