Dear friends,
This week I noticed a simple but strong signal: the Australian Financial Review reported the PwC Australia CEO personally dedicates 6–8 hours a week to AI learning and has asked his executive team to join the journey. It tells me AI literacy is becoming part of leadership’s toolkit, not a “nice to have.” (AFR link) PwC
On the startup side, Airwallex’s leadership has been vocal about pushing AI into core finance workflows and has already rolled out AI tools in onboarding/KYC with more to come—proof that the frontier is moving from “assistants” to embedded finance capabilities. airwallex.comOpen Banking Expo
Those headlines nudged me to shift focus. Over Weeks 1–3 we explored AI for individuals—facts, prompts, and language. This week, I’m asking: what should organisations actually prioritise to make transformation real?
AI is a tool—use it wisely.
A recent Diary of a CEO episode with Dr Daniel Amen and Dr Terry Sejnowski warned that over-reliance on tools can dull our thinking. It’s a good reminder: AI accelerates speed, but professional judgement still comes from continuing development and experience. Apple PodcastsSpotify
With close to two decades experiences in finance, also from my research, observation and interviews, I documented my learning notes below and share it openly here for open discussion.
What leadership should prioritise
- Strategy first. Be clear on the business problem, desired outcomes, and “what we will not do.”
- Role clarity. Who owns which process? Who decides? Where do people go for answers? Without this, even clean data won’t stick.
- Governance by design. Bake controls, accountability and change paths into the solution—don’t bolt them on later.
- Data & systems—then sequence. Fix foundations before fancy features; plan integrations and master-data rules early.
- Metrics beyond cost savings. Track decision speed, error rates, close cycle time, user adoption and audit findings—not just dollars.
- Aftercare (keep the brains!). Retain key project people post go-live to stabilise, tune, and extend. Cutting them immediately is a false economy.
Aftercare (the piece that’s easy to miss)
- Keep a stabilisation squad (solution owner + data lead + architect + 1–2 SMEs per tower) for 6–12 months.
- Fund a small continuous-improvement backlog; measure and publish the wins.
- Refresh controls as processes change (SOX, ITGC, segregation of duties).
SME resourcing: BAU vs Project
- For critical tracks (record-to-report, order-to-cash, source-to-pay), second your best SMEs 50–100% into the project during design/build; backfill BAU with temps or cross-training.
- Create formal handover back to BAU: playbooks, shadowing, and “day-in-the-life” runbooks.
- Keep at least one embedded SME in the platform team after go-live—your “institutional memory.”
Measuring success (simple, not simplistic)
- Cycle time: monthly close days trending toward top-quartile.
- Quality: first-pass yield, posting error rates, reconciliation breaks.
- Decision velocity: time from question to answer for key dashboards.
- Adoption: % of target users actively using new tools 90 days post-go-live.
- Controls: audit points closed on time; automated control coverage ↑.
- Value realisation: business-owned benefits register with quarterly validation (finance + ops sign-off).
(For context: McKinsey notes ~70% of transformations fall short—alignment and execution matter as much as tech.) McKinsey & Company
External partners (use them well)
- Bring partners in for architecture, accelerators, and change muscle, but keep product ownership internal.
- Ask for industry benchmarks and reference designs (close, cash, working capital, planning).
- Insist on knowledge transfer: co-build, co-document, co-run training.
- Use partner tooling for controls “assurance by design” so governance keeps up with delivery. Deloitte's perspective
Reality check on timelines
Large finance transformations often run 12–18 months for first value in big organisations; more in listed environments. (PwC) And yes, many stall without role clarity and governance. (McKinsey) Plan like a slow stew, not fast food. PwCMcKinsey & Company
Where this is heading
While “AI-CFO” headlines grab attention, the more immediate story is practical: firms are embedding AI inside finance workflows (onboarding, anomaly detection, reconciliations, forecasting) and upskilling their people.
AI speeds us up. Judgement—built through learning and experience—keeps us safe and commercial. That balance is the transformation.
Until next time —
Lydia
P.S. If this letter found you at just the right moment, I’d love to hear about it. Join my weekly letter list and let’s figure it out together — one AI-shaped step at a time. Join the weekly letter list.40+ and Figuring It Out
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