In a recently released episode of In the Black, CPA Australia's podcast for finance and accounting professionals, I joined innovation specialist Dr Amantha Imber and host Patrick Viljoen to unpack the forces reshaping the profession across Asia Pacific.
What struck me most wasn't the statistics — though there were plenty of arresting ones. It was the gap. The yawning, widening gap between the organisations that are genuinely redesigning how work gets done with AI, and the ones that have installed Copilot, typed a few bad prompts, produced what Amantha rightly calls "AI slop," and declared the job done.
That gap is about to become very expensive.
The New Moore's Law: Why Sitting on the Sidelines Is a Compounding Problem
Most people think of AI adoption as a strategic choice. Adopt now or adopt later — the outcome is roughly the same, just shifted in time.
It isn't.
There is a new Moore's Law for agentic AI. The original Moore's Law described computing power doubling roughly every 18 to 24 months. Agentic AI — AI that doesn't just respond to prompts but pursues goals, executes tasks, and adapts to obstacles — is doubling in sophistication every three to seven months. The maths of that compounding are brutal.
While you're sitting skeptical on the sidelines, the competitor adopting agentic AI will 2x you in the next quarter. 4x you in the next two quarters. In a year's time, they will have 16x'd you in terms of capacity and productivity output.
You're not falling behind. You're being 16x'd.
This is not science fiction. It is arithmetic.
92% of accounting professionals are now using AI in some form, according to the State of AI in Accounting 2026 Report. 63% believe their firm's value drops if it doesn't use AI. 55% of firm owners cite failure to adopt AI as their number one risk to firm valuation. The consensus has arrived. The question is no longer whether. It's how well — and that's where most organisations are still falling short.
AI Literacy vs AI Fluency: Most Organisations Are Stopping One Step Short
Amantha made a distinction on the podcast that I think is one of the most practically useful frames for any CPA thinking about their firm's AI strategy right now.
There's AI literacy — knowing how to have a useful conversation with an AI tool, getting output that's more than generic slop, understanding the basics of prompting. Most organisations are focusing their capability building here. And then they stop, tick the box, and move on.
But literacy is not leverage.
Leverage is when you think like a workflow architect. When you look at everything you do — daily, weekly, monthly — and you ask: where could AI play a role in redesigning this from the ground up? That's where the real productivity gains are. Not writing better emails. Rebuilding entire workflows.
Gartner's research shows AI delivers an average of 5.4 hours per week in gross time savings at the literacy level. Some accounting firms are already reporting over 80% automation of individual tax return preparation at the leverage level. Automated reconciliation tools are reducing month-end closing cycles by 40 to 60%. AI-led data entry alone saves the average accountant 120 hours per year.
The gap between literacy and leverage is the gap between marginal improvement and transformation. Most organisations are parked at literacy, congratulating themselves for getting there, while the transformative gains remain uncaptured.
Taking the Robot Out of the Human
The promise of AI in finance and accounting isn't replacement. It's liberation.
For too long, some of the sharpest financial minds in any organisation have spent the majority of their time on work that is repetitive, rules-based, and frankly beneath the level of thinking they were hired to do. Journal entries. Reconciliations. Data extraction. Variance reporting. These are things AI can now handle — not perfectly, not without oversight, but competently enough to free up the humans in the room for the work that actually requires human judgment.
The meaningful work. The strategic work. The advisory work. The work that builds client relationships, identifies risk before it materialises, and shapes the financial direction of an organisation.
As I've written on the culture barrier to AI adoption, the organisations that unlock this shift fastest are not necessarily the ones with the best technology. They're the ones with leaders who frame AI as a tool for human elevation — not a threat to headcount — and who redesign their workflows accordingly.
Generative AI is projected to boost productivity in financial services by up to 30%. But that 30% only materialises when organisations stop thinking about AI as something that sits alongside existing workflows, and start thinking about it as something that redesigns them entirely.
AI Is Not Magic — But Expertise Still Is
Here's the counterpoint Amantha made on the podcast, and it's an important one.
AI is not magic. And it doesn't replace expertise.
The research is clear: an expert working with AI still produces significantly better output than a non-expert working with AI. AI raises the floor — it allows people with less experience to produce higher quality work than they could unaided. But it doesn't raise the ceiling. The ceiling is still set by human expertise, judgment, and domain knowledge.
This has a practical implication for every CPA thinking about their career right now: the value of deep expertise is not declining in an AI age. It is increasing. Because expertise is what determines how well you use the tool, how accurately you interpret its outputs, how effectively you catch its errors, and how much genuine value you add on top of what AI can produce.
2025 was the year of AI experimentation. 2026 is the year of accountability — where finance leaders are demanding hard, auditable impact from AI investments, not just productivity estimates. That accountability requires human expertise at the centre of every AI-assisted process. There is no shortcut around it.
The Black Box Paradox: Interrogating Your AI
One theme that came up in conversation with Patrick Viljoen — particularly relevant for CPAs working in audit, assurance, and sustainability reporting — is what he called the "black box paradox."
AI can now run climate scenario analysis, produce financial forecasts, and generate audit-ready reports. But when assurance professionals want to interrogate the assumptions behind an AI-generated output, they often find themselves staring at a conclusion with no clear visibility into the reasoning that produced it.
The practical solution is simpler than most people think: ask the AI. Ask it directly what assumptions it made, what data it drew on, where the uncertainties are. A well-designed AI system will tell you, in plain language. Many tools now show you the reasoning in real time — a dropdown of the working, visible if you look for it.
The deeper principle is this: the AI growth mindset — a nod here to Carol Dweck's foundational work on growth vs fixed mindset — applied to financial professionals means treating every AI output as a starting point for interrogation, not a conclusion to be accepted. Curiosity is the antidote to the black box.
The Cybersecurity Threat Nobody on Your Finance Team Is Prepared For
Here's the story I've been telling in keynotes that consistently stops rooms cold — because it happened to a finance professional doing exactly what they were supposed to do.
In early 2024, a finance worker at engineering firm Arup in Hong Kong received an email from someone claiming to be the company's CFO, requesting authorisation for a confidential transaction. The employee was suspicious. So they did the right thing — they requested a video call with the CFO and several senior colleagues to verify the request before authorising anything.
Everyone on that call looked and sounded exactly like the people they claimed to be. The CFO. The colleagues. The entire meeting was a deepfake — AI-generated versions of real Arup executives, built from publicly available video and audio from previous online conferences.
The employee made 15 wire transfers totalling $25.6 million USD into Hong Kong bank accounts controlled by the fraudsters. The fraud was only discovered when the employee followed up with Arup's actual head office afterward. As of 2025, none of the funds have been recovered.
This was not a cyberattack in the traditional sense. No systems were compromised. No data was breached. As Arup's CIO described it: technology-enhanced social engineering. The attack exploited the most fundamental human trust mechanism — seeing and hearing a familiar face — and defeated it entirely with AI.
AI-generated fraud losses are projected to reach $40 billion by 2027. For finance teams in particular — who sit at the intersection of authority, trust, and large transaction volumes — this is not a theoretical risk.
Three principles I'd encourage every finance leader to embed in their AI governance framework:
Explain it. Every AI system deployed must be explainable to the people using it and the stakeholders affected by it. If you can't describe how it reached its conclusion, you can't defend it.
Defend it. If your AI-assisted process ended up on the front page of the Financial Review tomorrow, could you justify every decision it made? If not, the governance isn't there yet.
Sustain it. AI investments require ongoing maintenance, monitoring, and updating. A model that was appropriate six months ago may be producing subtly wrong outputs today as the world has changed around it.
The Succession Planning Crisis Nobody Is Talking About
Amantha raised something on the podcast that deserves more attention than it typically gets in the AI conversation.
Many organisations are responding to AI's ability to automate graduate-level tasks by cutting their graduate intake. Why hire five grads when AI can do what three of them would have done?
The short-term maths is seductive. The long-term maths is catastrophic.
Because the senior finance leaders, partners, and CFOs of the next decade are the graduates of today. The tacit knowledge, the judgment, the client relationships, the institutional memory — all of that is built over years of progression through a firm. Cut the pipeline now, and you don't just save salary costs in 2026. You create a leadership vacuum in 2034 that no AI can fill.
The number of accounting job listings requiring AI skills jumped from 18% to 30% in a single year. 91% of accounting professionals believe graduates are more likely to join firms that actively use AI. The talent war for AI-fluent finance professionals is already underway. The organisations investing in their people's AI capability — rather than using AI as a justification to reduce headcount — are the ones building genuine competitive advantage.
The EQ + UQ + CQ Formula: Future-Proofing Your Career
If you're a CPA asking the practical question — what do I actually do to make myself indispensable in an AI-augmented profession — here's the formula I shared on the podcast.
Three types of intelligence that no AI can replicate, stacked together with deliberate digital augmentation:
EQ: Emotional Intelligence. The capacity to regulate your own responses, read a room, navigate difficult conversations, and build the kind of client trust that sustains relationships across decades. This is something AI can inform but not replace. And it can be developed throughout your life — through practice, through coaching, through the kind of 1% daily improvement that compounds into something extraordinary.
UQ: Unique Intelligence. Your specific combination of skills, experiences, and strengths that makes you irreplaceable in a particular context. Know what your superhero skill is. But also know where your weaknesses are — because those are the gaps where you want AI to augment you, not the gaps you want to hide.
One of my colleagues in the Entrepreneurs Organisation built a negotiation agent in Claude, trained on Chris Voss's Never Split the Difference, because he knew negotiations were his weakness. He didn't try to become a better negotiator. He built an AI that makes him one. That's UQ working with digital augmentation.
CQ: Curiosity Quotient. The commitment to continuous learning, experimentation, and openness to being wrong. Taking just 15 minutes a day to experiment with AI tools puts you ahead of 99% of professionals in your field, because most people are not doing it. If your organisation isn't investing in your AI upskilling, take matters into your own hands. Research from Inventium suggests around 81 hours of formal AI training per year is what it takes to genuinely level up.
EQ plus UQ plus CQ, with AI layered on top as digital augmentation: that is the formula for the most empathetic, most productive, and most future-proof professional brand in finance.
The Twin Transformation: Digital and Sustainable, Together
One final thought — because for CPAs, AI and sustainability are not separate agendas. They're converging.
Technology is geology. Every AI query has a physical footprint. The power consumption of AI data centres is projected to quadruple by 2030. Proposed data centres in Australia alone could consume up to 25% of Sydney's water supply for cooling. For finance professionals now working inside mandatory sustainability reporting frameworks across Asia Pacific, this is not a peripheral concern. It is a material risk that belongs on the balance sheet.
The organisations that will navigate the next decade most effectively are not the ones that pursue digital transformation and sustainable transformation as separate initiatives. They are the ones pursuing both simultaneously — investing in AI capability while building the renewable infrastructure and governance frameworks that make that capability sustainable over time.
The smart CPA is already thinking about both.
The future will reward curiosity, judgment, and continuous learning. The professionals who thrive in the AI age won't be the ones who waited until the technology was proven. They'll be the ones who built the fluency, the frameworks, and the human intelligence stack that makes AI genuinely powerful in their hands.
See you in the future.
About the Author
Anders Sorman-Nilsson is a Swedish-Australian futurist and keynote speaker who helps leadership teams across the world decode the signals reshaping finance, technology, and the future of work. With clients including Apple, Google, Microsoft, McKinsey, LEGO, and CPA Australia, Anders speaks at conferences globally on AI adoption, agentic technology, conscious leadership, and the human skills that matter most in a machine-augmented world. To enquire about booking Anders for your next finance, accounting, or technology conference, visit anderssorman-nilsson.com.
Frequently Asked Questions: AI, the Future of Finance, and Booking a Futurist Keynote Speaker
Who is Anders Sorman-Nilsson and why is he the right keynote speaker on AI for finance and accounting events?
Anders Sorman-Nilsson is a Swedish-Australian futurist, keynote speaker, and author with over 20 years of experience helping organisations decode the signals reshaping business, technology, and human performance. His client roster includes Apple, Google, Microsoft, Meta, McKinsey, LEGO, Dyson, BMW, Citi, and Zoom, as well as professional bodies including CPA Australia. He holds a Law degree and an EMBA, giving him a rare combination of legal, business, and foresight credentials that resonates deeply with finance and accounting audiences. Anders speaks on AI adoption, agentic technology, the Digilogue framework, conscious leadership, and the future of professional work. His keynotes are known for combining rigorous data, personal storytelling, and immediately actionable frameworks — making complex futures feel navigable rather than overwhelming. To check availability for your conference, summit, or leadership event, contact Thinque at anderssorman-nilsson.com.
What topics does Anders Sorman-Nilsson speak on for finance and accounting audiences?
Anders speaks on: AI adoption and agentic AI for professional services; the future of finance, accounting, and the CPA profession; AI fluency and workflow redesign for finance teams; cybersecurity in an AI age including deepfake fraud risk; sustainability reporting and the twin transformation of digital and ESG strategy; the EQ, UQ, and CQ framework for future-proofing professional careers; and the new Moore's Law for agentic AI. His work with CPA Australia, internal audit conferences, payments organisations, and financial services firms globally means his content is grounded in the specific challenges finance professionals face — not generic AI commentary.
What is AI fluency and why does it matter for CPAs?
AI fluency goes beyond AI literacy — which is the ability to use tools like ChatGPT or Copilot to produce useful outputs. AI fluency means understanding how to redesign workflows and processes around AI capability, identifying where in your daily, weekly, and monthly work AI can play a substantive role, and developing the skill to orchestrate human and AI contributions for maximum impact. For CPAs, fluency is the difference between marginal efficiency gains and genuine competitive transformation.
Will AI replace accountants and finance professionals?
Not professionals who develop genuine AI fluency. AI is automating the repetitive, rules-based, data-heavy tasks that have historically consumed a disproportionate amount of a finance professional's time — data entry, reconciliation, basic reporting, invoice processing. But the judgment-intensive, advisory, client-facing, and strategic work that defines value in the profession is becoming more important, not less. 92% of accounting professionals are now using AI — the question is not whether to engage with it, but how deeply.
What is agentic AI and what does it mean for finance teams?
Agentic AI refers to AI systems that don't just respond to prompts but pursue goals autonomously across multiple steps — executing tasks, adapting to obstacles, and interacting with other systems without constant human instruction. In finance, this means AI agents that can manage procurement, process invoices, monitor compliance, flag anomalies, and even execute payments autonomously — as Mastercard and Banco Santander demonstrated in March 2026 with Europe's first live AI agent payment within a regulated banking framework. As I've explored in depth in my writing on the new Moore's Law for agentic AI, the compounding pace of this technology means the window to prepare is shorter than most organisations think.
What is the deepfake threat for finance teams specifically?
Finance professionals sit at the intersection of authority, trust, and high-value transactions — which makes them a primary target for AI-enhanced fraud. The Arup case in 2024 — where a finance employee was deceived into transferring $25.6 million after a deepfake video call with AI-generated versions of the CFO and colleagues — is the clearest demonstration of what this threat looks like in practice. AI fraud losses are projected to reach $40 billion by 2027. Every finance team needs a verification protocol for large transactions that goes beyond visual and audio confirmation alone.
What is the "black box paradox" in AI-assisted finance and how do you address it?
The black box paradox describes the challenge of AI systems that produce conclusions — forecasts, risk assessments, audit findings — without making their underlying assumptions visible or interrogable. The most practical solution is to build interrogation into your AI workflow as standard practice: ask the AI directly what assumptions it made, what data it drew on, and where uncertainty exists. The deeper solution is building a culture of AI curiosity — where outputs are always treated as starting points for human judgment, not conclusions to be accepted.
How should CPAs think about sustainability and AI together?
Mandatory sustainability reporting is creating a new category of AI opportunity for CPAs — AI can dramatically accelerate the data collection, analysis, and scenario modelling that sustainability reporting requires. But the environmental footprint of AI itself is also a material consideration. AI data centres are projected to quadruple their power consumption by 2030. For CPAs advising on ESG strategy or preparing sustainability disclosures, understanding the AI footprint of their organisation's technology stack is becoming a professional obligation, not an optional extra.
How do I book Anders Sorman-Nilsson as an AI keynote speaker or futurist speaker for my finance or accounting conference?
Anders is available for keynotes, conference presentations, leadership offsites, and panel appearances globally. He regularly speaks for professional bodies, financial services firms, accounting networks, and corporate leadership teams across Australia, Asia Pacific, North America, South America, The Middle East and Europe. His keynotes are customised for each audience — drawing on current research, personal storytelling, and specific industry context. To check availability, discuss your event brief, and receive a speaker kit, contact Thinque at anders@thinque.com or via the enquiry form at anderssorman-nilsson.com.
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