AI Meets CFO

News Corp's first try at putting AI on its finance data came back untrustworthy. Matt Clifford explains what they rebuilt, and what finance should focus on once AI clears the build bottleneck.

Matt Clifford leads finance systems and data at News Corp. He shares why an early AI trial failed on unlabelled data, how rebuilding a planning model that had been running since 1998 sped up scenario planning, and why the real challenge is now choosing which AI ideas to fund.

What happens when you switch on AI over finance data that was never designed for it? At News Corp, the answers came back patchy and hard to trust. Matt Clifford, who leads finance systems and data there, tells host Amendra Pratap what the team learned and how they fixed the foundations.

Matt came into finance from technology, starting in data and reporting at Australia's financial regulator. He explains how News Corp rebuilt a planning model that had been in place since 1998, ran old and new side by side, and brought users along by asking why they kept jumping to Excel.

He then looks ahead: the "business partner in your pocket" idea, why data governance has become the foundation for AI, why locking into one model for more than 12 months is a risk, and how AI changes the question from "what can we build?" to "what should we build first?"

In this episode:

• From technology to finance: starting in data and regulatory reporting
• Why the first attempt at AI on finance data failed
• Rebuilding a decades-old planning model without disrupting the business
• Getting users to trust the platform before they jump into Excel
• Executive sponsorship and making the platform the users' own
• Keeping a lean core stack but staying flexible on reporting tools
• The "business partner in your pocket": instant answers from trusted data
• Why data governance now underpins every AI move
• Token costs and the real problem: prioritising ideas when build is no longer the constraint
• Avoiding lock-in to a single model or vendor
• Onboarding AI agents like team members
• AI forecasting, from complex models to quick baselines for everyone
• Has finance reached its peak size?
• Advice for graduates and for leaders frozen by the pace of change

About the guest: Matt Clifford leads finance systems and data at News Corp Australia. He has worked in data and technology since the 2000s, including leading a business analysis practice at Australia's financial regulator.

Host: Amendra Pratap

Timestamp and Chapters:

00:00 - Welcome and introductions

01:23 - Data and regulatory reporting beginnings

02:08 - From tech into finance

02:38 - Matt's role at News Corp

04:11 - The first attempt at AI, and why it failed

05:35 - Rebuilding a planning model in place since 1998

07:48 - Excel's place in the new platform

08:43 - Bringing people along on the change

10:13 - Support and learning from others

12:10 - The tool stack

13:10 - Being willing to change tools

14:32 - Finance as a strategic partner

15:58 - Moving past the AI hype

17:50 - Use cases: a business partner in your pocket

19:01 - Data governance comes first

20:53 - Security, cost and prioritisation

23:04 - How finance teams feel about AI

25:27 - Avoiding lock-in to one model

27:11 - More agents than people?

28:53 - AI forecasting

30:39 - AI in legacy and digital businesses

31:42 - Has finance reached its peak size?

32:43 - Advice for graduates

33:35 - Advice for leaders who feel stuck

34:47 - Will AI replace finance tools?

36:18 - The cost of AI as hyperscalers seek returns

37:44 - What to focus on next

Isabella Susilowati, CFO at O'Brien Glass, has spent more than 20 years in finance, including 14 at O'Brien. She explains why good actuals come before any forecast, how her team used TM1 and Power BI through a demand shock, and the simplest way for finance people to start with AI.

Where should a finance team actually start with AI? For Isabella Susilowati, the answer is simple: download a tool and use it. In this episode, the O'Brien Glass CFO talks with host Amendra Pratap about the technology that excites her most, TM1, Power BI and AI, and how she brings her team along.

Isabella explains why clean historical data is the base for every forecast, and why she believes no business can fully get rid of Excel. She shares how TM1 gave her team one source of truth when demand dropped sharply and decisions had to be made daily.

She also covers AI policy for finance, keeping a human in the loop, and the leadership lessons that shaped her, from giving people more than they think they can handle to always explaining the why.

In this episode:

• Isabella's path from FP&A manager to CFO over 14 years at O'Brien Glass
• Why AI is like the internet 20 years ago, and the risk of being left behind
• The first step into AI: download a tool and use AI to teach you about AI
• The seven levels of AI mastery, from asking questions to agents that run in the background
• Setting an AI policy: what data can go in, and which platforms are safe
• Why human in the loop still matters
• Getting actuals right in TM1 before you forecast
• Why some forecasting still lives in Excel, and how to keep it tidy
• Using TM1 for daily reforecasting through a sudden drop in demand
• Building Power BI on a clean data warehouse layer
• Leadership: stretch assignments, honest feedback and explaining the why
• Why teaching others makes you better at what you do

About the guest: Isabella Susilowati is CFO of O'Brien Glass, part of the global Belron group. She has worked in finance for more than 20 years, including roles at BOC Gas and Origin Energy, and shares Power BI, Excel and AI tips on her YouTube channel, Lighthouse Analytics.

Host: Amendra Pratap

Timestamp & Chapter:

00:00 - Welcome and introductions

01:48 - Isabella's journey to CFO

02:51 - What O'Brien Glass does

03:19 - The tech stack and the ERP journey

04:28 - TM1, Power BI and the seven levels of AI mastery

06:25 - A message for AI sceptics

08:04 - How finance teams should get started

10:03 - AI governance and company policy

11:25 - More agents than people?

12:21 - Why human in the loop matters

12:48 - AI for forecasting

14:05 - Bringing the team along: sharing AI wins

15:36 - How the team uses TM1

18:03 - Reforecasting through a demand shock

19:27 - Power BI and the data warehouse

20:52 - Motivating a finance team

27:17 - Why she teaches: the teachers who shaped her

31:44 - The YouTube story

34:50 - Finance, fractional CFO work and what's next

AI Meets CFO is where finance leaders talk about what actually works. Host Amendra Pratap sits down with CFOs and finance-technology leaders about AI in finance.

In this trailer, Nick Renotte from IBM on:

- Why LLMs on their own are "kind of fancy chatbots" until you give them access to tools

- Writing month-end commentary late on a Friday

- IT changes that take three months when finance wanted them done in three minutes

- What happens when AI gets really expensive

- Why you start small to de-risk AI projects

No scripts, no sales pitches. Just practitioner-to-practitioner conversations for people who make real budget and technology decisions.

Follow the show so you don't miss the first full episode.

Website: https://aimeetscfo.com

Be a guest: https://aimeetscfo.com/be-a-guest/

Nick Renotte from IBM joins Amendra to talk about broken spreadsheets, late-night month-end reports, and what AI can actually do for finance teams.

Welcome to the first episode of AI Meets CFO. Host Amendra sits down with Nick Renotte, who leads AI developer work at IBM, runs a YouTube channel with 320,000 subscribers, and is also a Chartered Accountant who learned Excel macros at eight years old.

They skip the sales pitch and talk about what actually works:

• Why ChatGPT is just a chatbot until you connect it to your ERP and spreadsheets

• How to get AI to draft month-end variance notes so you do not work late on Friday

• Why asking giant AI models simple questions gives you a massive surprise bill

• Why staff secretly use outside tools when IT takes three months to change a report

• Why being scared of AI is like being scared of high school math formulas

• How Nick connects AI directly to TM1 and IBM Planning Analytics

Chapters:

00:00 - Meet Nick Renotte: 320k YouTube subscribers and IBM AI lead

02:24 - How a Chartered Accountant ended up building AI at IBM

04:11 - What an AI agent actually is (and why ChatGPT is just a chatbot)

07:20 - Why month-end reports are painful and how AI helps

09:34 - How simple AI questions turn into massive monthly bills

14:45 - The difference between personal AI tools and company tools

17:20 - Why Microsoft Copilot still has not fixed your job

21:00 - Why being afraid of AI is like being afraid of algebra

25:20 - Connecting AI directly to TM1 and Planning Analytics

31:44 - Where does your company financial data actually go?

37:51 - Why staff secretly use ChatGPT when IT is too slow

42:50 - Do you really need the most expensive AI model?

45:20 - The single easiest place for finance teams to start