From Spreadsheets to Skills: How AI-Powered Accountants Are Redefining Finance

The final session of the Future of Finance Lab brought the series to a practical and forward-looking close, featuring real-world student feedback and hands-on demonstrations of AI in action. Rather than theory, this session focused on lived experience—how finance professionals are actually applying AI tools, where they struggle, and what’s working. Through Eva Tsang’s case studies and reflections, the discussion highlighted a clear shift: finance is no longer just about technical execution, but about orchestrating intelligent systems, structuring data, and applying judgment in an AI-augmented environment.

Fortnightly Nugget

AI doesn’t replace finance professionals—it amplifies those who know how to structure problems, context, and data.

The New Finance Stack: Beyond Tools to Frameworks

A central theme of the session was that becoming an “AI-powered accountant” is not about adopting tools blindly—it’s about building capability across a structured framework.

Lance Rubin introduced the “HACK” framework:

• Hygiene (clean, structured data)
• Automation (streamlining repetitive workflows)
• Capability (developing technical and analytical skills)
• Knowledge management (capturing and reusing insights)

This framework reinforces a critical point: jumping straight to AI tools without strong data foundations and governance limits value. As Rubin noted, “If we’re just focusing on the tool, where are we going to add value?”

Interestingly, despite rapid innovation, Excel remains central. Rather than being replaced, it is being enhanced—integrated with AI copilots, automation layers, and visualization tools like Power BI.

Key Insight #1: The Real Bottleneck Is Context, Not Technology

Eva Tsang, drawing on over a decade in M&A advisory, highlighted a surprisingly common challenge: controlling AI outputs.

While many professionals are experimenting with AI agents, the issue is not access—it’s effectiveness.

“A lot of times I have a hard time controlling them… it just kind of goes in loops.”

This aligns with broader industry findings she cited: 95% of AI practitioners fail to deliver measurable value due to a learning gap

The takeaway is clear: success with AI depends on how well you frame problems and provide context, not just which tools you use.

Key Insight #2: Data Visualization as a Thinking Tool

One of the most practical demonstrations in the session focused on using AI to overcome the “blank canvas problem” in dashboards.

Eva showed how AI can:

• Generate structured data tables for Power BI
• Recommend appropriate chart types
• Suggest dimensions and metrics to visualize

This transforms visualization from a manual design task into a guided analytical process.

“AI tools are a great sounding board… to translate data and build visualizations.”

Instead of starting from scratch, finance professionals can now rapidly prototype dashboards, iterate faster, and focus on interpreting insights rather than assembling charts.

Key Insight #3: Skills > Prompts — The Rise of Repeatable AI Workflows

Perhaps the most advanced concept discussed was the use of AI “skills”—structured, reusable workflows that encode processes.

Eva demonstrated a “company benchmarking” skill that:

• Identifies peer companies
• Pulls financial and market data
• Performs benchmarking analysis
• Generates structured outputs (financials, trends, strategy insights)

What previously took 1–2 days of manual work can now be executed rapidly with a single prompt

“Once you set up your skills correctly, you can pretty much repeat the same process.”

This represents a major shift:

• From ad hoc prompting → to systematic automation
• From one-off analysis → to scalable, repeatable intelligence

Practical Takeaways for Finance Professionals

1. Treat AI as a Junior Analyst—But Train It Properly

AI can accelerate workflows, but only if given:

• Clear instructions
• Structured context
• Defined boundaries

For example, specifying which data tabs should not be altered or requiring source citations improves reliability.

2. Start with Data Hygiene Before Automation

Messy data leads to poor AI outputs. Prioritize:

• Clean, structured datasets
• Consistent definitions
• Controlled data flows

Without this foundation, automation will amplify errors rather than efficiency.

3. Use AI to Overcome Skill Gaps

AI enables professionals to work beyond their technical comfort zone.

Eva, for instance, avoided complex Power BI measures by:

• Performing calculations in Excel
• Using AI to guide visualization design

This approach allows finance teams to deliver outcomes without needing deep specialization in every tool.

4. Build Reusable “Skills” for High-Value Tasks

Focus on processes that are:

• Repetitive
• Time-consuming
• Structured

Examples include:

• Benchmarking analysis
• Financial modeling workflows
• Reporting templates

Once encoded, these workflows can be reused across clients, industries, and scenarios.

5. Always Validate AI Outputs

Human judgment remains essential.

“You have to cross-check the work of the AI agents.”

Best practices include:

• Requesting source transparency
• Flagging contradictions
• Reviewing assumptions

AI accelerates analysis—but accountability still sits with the finance professional.

Case Study: Automating M&A Benchmarking

Eva’s benchmarking workflow provides a compelling example of AI’s impact.

Traditional process:

• Manual peer selection
• Data collection from multiple sources
• Spreadsheet modeling
• Iteration based on feedback
  → 1–2 days per analysis

AI-enabled process:

• Prompt triggers predefined skill
• AI identifies peers and gathers data
• Outputs structured analysis (financials, trends, strategy)
• User validates and refines
  → Minutes to hours

The result is not just speed—it’s consistency, scalability, and improved decision support.

Notable Quotes

• “95% of AI practitioners actually fail to deliver measurable value… because there is a learning gap.”
• “AI tools are a great sounding board.”
• “Once you set up your skills correctly, you can pretty much repeat the same process.”
• “We still keep the responsibility… for the decisions that come out of it.”

Looking Ahead

The Future of Finance Lab is more than a webinar—it’s an interactive community dedicated to learning, sharing, and growing together. We invite you to join our next session, connect with peers, and continue building the skills that will define the future of finance.

For resources, recordings, and more, visit our Knowledge Hub and stay tuned for further updates!

You can find more content on our Eloquens channel, including the FREE slides.

Here is a link to the specific content on Eloquens.

The session was also recorded and is available to be viewed on YouTube.

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Want to discuss how AI can transform your finance function? Get in touch with Model Citizn to learn more about our approach to AI-powered financial modelling and strategic implementation.

Conclusion: The Evolution of the Finance Professional

The Future of Finance Lab series consistently emphasised that the role of finance is evolving—from number crunchers to strategic advisors.

This final session brought that vision into focus.

The modern finance professional must now:

• Understand data deeply
• Frame problems clearly
• Leverage AI effectively
• Apply judgment rigorously

AI is not replacing finance—it is raising the bar.

Those who succeed will not be the ones who use the most tools, but those who:

• Build structured workflows
• Think critically about outputs
• Continuously adapt their skillset

Want to take action?

You have two options available if you want to continue to grow and learn.

1. Formal accreditation program

We launched the AI-Powered 8-week accreditation program, which covers 9 modules and 6 core pillars as foundations. If you want to explore more and get yourself prepared for the future, then perhaps this is just the course for you.

The program covers both technical and soft skills, including a growth mindset and human skills needed for the future.

For more information, click this link

2. Future of Finance Lab

We continue to offer our Finance Lab for free sessions to unpack the hype and provide solid foundations for finance professionals.

Join our Friday Future of Finance Lab in the format of Lightning sessions on Maven.

Join our fortnightly community where finance professionals share insights, solve real challenges, and stay ahead of the AI revolution (max 50 attendees).

Key Benefits:

✅ Live Problem Solving – Bring work challenges

✅ Expert Insights – Practitioners not trainers

✅ AI Integration – Leverage AI in finance

✅ Networking – like-minded professionals

✅ Zero Cost – Completely free, always

What You’ll Get:

✅ – Fortnightly Focus Areas in the following areas

✅ – Financial Modeling Deep Dives

✅ – Data Analysis & Power BI Techniques

✅ – AI Applications in Finance

✅ – Open Forum & Case Studies Exclusive Resources

✅ – Meeting recordings and AI-generated summaries

✅ – Templates and tools shared during sessions

✅ – Resource library access

Who Should Attend:

✅ – Finance professionals wanting to upskill

✅ – Data analysts working with financial data

✅ – Anyone curious about AI in finance

✅ – Business leaders seeking data-driven insights

Buckle up and look forward to seeing you there!

AI Accounting Finance #CFO #FinancialModeling #AIAdoption #FutureOfWork

Call to Action

If you’ve followed this session, it’s only a glimpse of what’s possible.

Explore the full Future of Finance Lab series to:

• Learn practical AI applications in finance
• Access real-world case studies and tools
• Build your own AI-powered workflows

The future of finance isn’t coming—it’s already here. The question is whether you’re building the skills to lead it.