Stopgap to Scalable: Transform Finance with AI and CTO Leadership

As the VP of Finance at Rossum, a company devoted to automating the finance function, I find myself grappling with an irony I can no longer ignore. 

At Rossum, we provide a state-of-the-art platform to help enterprises modernize and automate their finance operations. Yet, I still rely on Excel for reporting, ad hoc analyses, budgeting, and forecasting.

It’s not that I resist change. Quite the opposite. I understand better than most the transformative potential of automation and AI. But I also see the realities faced by finance teams. Tools like Excel persist not because we lack ambition but because they address needs that automation solutions - ours included - don’t fully cover. They offer flexibility, familiarity, and a universal language for collaboration across industries. They work. And, crucially, they are trusted by everyone.

But let’s be clear: Excel is not the future. It’s a stopgap. Organizations that cling to it without preparing for what’s next risk falling behind. This is where your leadership is critical: CTOs are uniquely positioned to bridge the gap between the tools we use today and the innovations we aspire to adopt tomorrow.

The Promise of AI: Delivering Real Value

AI offers unprecedented potential for Finance, with the ability to process vast volumes of data, streamline workflows, save time and reduce manual work (and its inherent risk of errors), identify anomalies, and even detect and prevent fraud.

A recent Gartner survey shows that CFO sentiment towards AI in general is largely positive, with 85% of respondents expressing optimism about using it within the finance function. McKinsey predicts that “most, if not all, finance functions in large enterprises will likely be using GenAI in significant ways within the next three to five years."

For finance professionals, AI’s promise of efficiency and accuracy resonates deeply. Many are still bogged down by repetitive tasks, grappling with talent shortages, and struggling to maintain consistent data quality. More than ever, in today’s fast-paced environment, delivering accurate, actionable insights quickly is a necessity for enabling leadership to make informed, strategic decisions.

In that context, AI’s ability to scale operations and manage large and complex volumes of transactions and data makes it a game-changer. 

However, for AI to deliver on this promise, it must meet finance teams where they are - offering tools that are efficient, understandable, auditable, and trustworthy.

10 Top AI Platforms!

Here's my pick of the 10 best software from the 10 tools reviewed.

1. 1. iguazio — Best for MLOps acceleration
2. 2. Alteryx Intelligence Suite — Best for data blending and advanced analytics
3. 3. IBM Watson Assistant — Best for conversational AI applications
4. 4. PyTorch — Best for dynamic neural network graphs
5. 5. Vertex AI — Best for end-to-end AI deployment
6. 6. Google Cloud AI Platform — Best for integrating AI into cloud services
7. 7. Databricks Lakehouse Platform — Best for combining data lakes and data warehouses
8. 8. Keras — Best for deep learning model flexibility
9. 9. DataRobot — Best for automated machine learning
10. 10. Microsoft Azure AI — Best for scalable AI solutions in the cloud

Show More (5)

The Challenges: Complexity, Trust and Risks

Despite its potential, AI adoption in finance faces significant hurdles. Complexity is a major barrier, with 25% of finance leaders identifying it as a top concern in Rossum's latest Document Automation Trends 2025 report.

Many finance professionals view AI as a "black box" - opaque, intimidating, and challenging to scrutinize. Unlike Excel, where mastery comes from years of practice, AI tools require an entirely new skill set that most Finance teams typically lack. 

And then there’s the matter of trust. How can finance leaders confidently explain AI-driven decisions to auditors if they don’t fully understand the underlying logic? What happens when AI produces errors - like misreading a $1,000 invoice as $100,000, leading to a 100x overpayment? This risk, compounded by the fear of AI "hallucinations," makes many finance leaders hesitant to embrace it. And it is not only a financial risk but also a reputational one.

This is especially true given that finance teams operate under strict compliance and regulatory requirements, such as maintaining confidentiality, anonymizing sensitive data, adhering to rigorous reporting standards, and maintaining an effective internal control environment.

Finance leaders demand a level of transparency and accountability that many AI solutions struggle to provide. Without clear audit trails and robust governance, adopting AI can feel like trading control for uncertainty. According to our recent survey, 38% of finance leaders don’t prioritize automation, not because they don’t see its potential but because the compliance risks feel too daunting to tackle without the right governance in place​. 

Cybersecurity adds another layer of risk. Generative AI has emboldened cybercriminals, exemplified by a recent deepfake fraud scheme causing losses of up to $25 million. These highly sophisticated schemes pose a significant threat even to the most vigilant organizations. Their ability to continuously adapt, circumvent detection measures, and execute fraudulent activities at scale makes them particularly insidious. 

Yet, despite all these risks, the alternative - sticking with old tools, including Excel - is equally unsustainable. Our report reveals that 58% of Finance leaders still rely on Excel as their primary automation tool. While it’s effective for non-recurring analyses, it’s not scalable for the demands of a modern finance function. 

I’m here to help align our teams on what’s needed to safeguard our operations while you focus on delivering innovative technology. Let’s work together to bridge this gap, ensure compliance, and build trust - not just with AI but with the future of our business.

A Path Forward: CTOs and CFOs as Strategic Partners

To unlock the full potential of AI in finance, we need partnerships with CTOs. Here’s how that can happen:

1. Adopt a strategic approach

Start small and build from there. Help us identify where automation can make the biggest impact - the areas where the processes are the most manual, time-consuming, and prone to errors. Provide frameworks and clear steps to guide decision-making, smart implementation, and seamless integration into legacy systems.

2. Demystify AI

Change the narrative around AI-powered automation: Less hype, more real grounded benefits, and less technical jargon. Highlight the ROI and measurable outcomes, such as accuracy rates, automation rates, time saved… Be transparent regarding the time needed for customization or integration.

3. Lead with transparent AI

Address the “black box” stigma. Ensure that AI systems provide clear decision-making logic and audit trails. Build trust by keeping a human in the loop for decisions with low AI confidence, requiring judgment. We need to understand the mechanics of how the AI learns and make sure it doesn’t repeat the same mistakes.

4. Embed governance to ensure compliance

Strong governance is non-negotiable. Implement data governance protocols to keep information clean, secure, and centralized. Collaborate with finance and legal teams to manage and mitigate GenAI’s risk. Following an AI governance framework may help.

5. Strengthen cybersecurity

AI is both a tool and a target. Regular audits, secure data practices, and proactive risk management are essential to prevent fraud and mitigate evolving sophisticated threats.

6. Empower self-sufficiency

Finance teams have to be flexible and adapt quickly to the changing needs of the business or the regulatory environment. Equip them with user-friendly tools that don’t require constant IT intervention. Low-code or no-code platforms, coupled with training, can accelerate adoption and adaptability.

7. Foster continuous collaboration

Keep the dialogue open. Combining your technical expertise with finance’s regulatory insights will enable proactive risk management and innovation.

I know the temptation is to chase big wins. But I urge you to take a balanced approach. Tools like Excel or legacy software remain reliable and trusted by finance teams. Introducing AI alongside legacy systems in a transitional phase can help build confidence. Testing both systems side by side and ensuring consistent results will foster trust while easing the shift toward innovation.

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Looking Ahead

While 42% of finance functions are not currently using AI, half of these plan to implement it soon, according to a recent Gartner survey. The tools we rely on today, from Excel to early AI adopters, will give way to self-learning systems and predictive analytics. But getting there requires preparation.

CTOs, you hold the key to this transformation. By fostering collaboration, prioritizing transparency, and taking a measured approach, you can guide Finance teams like mine through this shift.

As I sit here, toggling between Rossum’s automation platform and yet another Excel sheet, I’m reminded that progress isn’t linear. It’s messy, full of false starts and hard decisions. But with the right leadership, we can bridge the gap between the tools we need today and the innovations we’ll rely on tomorrow.

Let’s make it happen - together.

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