Finance advisory expert says AI is taking finance teams more time, not less

Christian Franz Hansen is a partner on the Finance Advisory team of Implement Consulting Group, one of the leading management consultancies in the Scandinavian region. He's also the author of Communicating Financials to Executives.

We sat down with Christian to discuss the "trap" of AI — and where AI's real potential lies for finance teams. Here's what he told us.

AI makes some facets of the finance discipline even more critical

My name is Christian Franz Hansen, and I'm a partner at Implement Consulting Group — the largest consulting firm in Denmark and one of the leading management consultancies in the Scandinavian region, with offices across Scandinavia, Germany, Switzerland, and the US.

I'm a partner on our Finance Advisory team at Implement, a team of about 120 professionals working across the entire finance value chain. Our large team specializes in CFO advisory, master data management, risk, and other areas typically falling under CFO responsibilities. I work primarily with CFO teams and senior finance management across a wide range of organizations (pharmaceuticals, banking, logistics, and many others). I'm fairly industry-agnostic, but I focus on senior finance executives in large organizations, covering everything within the CFO's remit.

I've spent the last fifteen years in finance and CFO advisory. I started at the National Bank of Denmark, doing what I like to call Excel gymnastics: financial modeling, the real fundamentals. Then, I moved into management consulting, first with EY across Scandinavia and then in South Africa, in Cape Town and Johannesburg, before coming back to the Nordics to focus on finance transformation, business partner development, and performance management for large organizations globally.

For a long time, I lived deep in the data — building ever more elaborate models, convinced that if my analysis was sophisticated enough, I could change how a company made decisions. I learned, sometimes the hard way, that the model is only half the job. Uncommunicated insight never becomes impact. That realization led me to write a book last year on communicating financials to executives — bridging data to insight, and then insight to impact.

AI has now entered the scene and will likely dramatically change how we move from data to insights. But here's what I think many miss in AI conversations: people don't change behavior because an algorithm tells them to. Insights from an AI system are useful only if someone acts on them. That's where we come in as finance business partners. We translate insights into action. We drive change. We're communicators, change agents, and we make things happen. In fact, the behavioral and change management side of finance becomes even more critical with AI.

How AI can unlock finance's potential

Here's what genuinely excites me about this moment: AI has the potential to free our time from the mechanics of data extraction and analysis — something we've discussed for years. We finally have a tool that can deliver on that promise.

If used right, it gives us back the hours we've lost to Excel and modeling, allowing us to focus on the real value-add: strategy, relationships, influence, and driving business outcomes. We can finally unlock finance's potential as true business partners.

We're currently working with a client to scope a transformation of their accounts receivable and accounts payable processes at their global shared service center, where thirty-five FTEs handle invoices largely manually.

We are exploring the deployment of advanced AI-enabled technology — using Celonis — to handle invoice processing more effectively than their current manual setup. Preliminary analysis suggests we could reduce headcount from 35 in the SSC to much fewer at HQ. The remaining experts would focus on exception handling and process oversight instead of repetitive transactional work.

We're still in the scoping phase, but this engagement excites me because it signals the future. AI will handle the volume and the mechanics. Humans will handle judgment, relationships, and anything outside the normal pattern. If we execute this well, three highly skilled people will manage the work that previously required thirty-five.

Why finance leaders must still own judgment and storytelling

That split is important. Today, I use AI for data manipulation and preparation. This includes organizing datasets, running calculations, and preparing management reports. AI excels here, saving us significant time.

The real value, however, remains fundamentally human: taking those insights, crafting a narrative, and communicating them to influence business stakeholders to act. This involves storytelling, judging what matters, and connecting with people to drive change.

We haven't automated this, and we shouldn't.

How to avoid the "trap" of AI in finance

I've seen sobering results from AI's use, and it worries me.

AI can make us dangerously comfortable not using our own cognitive power. We risk going on autopilot: we ask the system for an answer, take it at face value, and move on without reflecting on the information. I've seen financial analysts become prone to error because they rely too heavily on AI output without their own critical thought. They've outsourced the reflection necessary for analysis. That's the trap.

And it's made worse by short-form video and social media bombardment. We're outsourcing our thinking while simultaneously losing the mental discipline to think deeply. That's a real threat to how finance — and society — solves problems.

AI is a tool to amplify our thinking, not replace it. If we're not careful, teams move faster but think less, which leads to mistakes. Humans must maintain the discipline of questioning the output, stress-testing assumptions, and understanding the logic; otherwise, we automate our way toward unforeseen problems.

But on the flip side, much genuine good also occurs. I've seen people become 20%, 30%, even 40-50% faster at basic tasks. This work used to consume huge amounts of time. Data consolidation, data cleanup, and preparation work that once took a week now happens in hours. That's genuinely transformative.

The key is to maintain critical thinking and reflection while capturing speed and efficiency gains. That balance is everything.

Why AI requires new frameworks for validating output

With AI, it becomes genuinely more difficult in finance to trace back where decisions came from and where insights originated when AI generates output without human intervention.

We will need to get comfortable relying on insights AI pulls and delivers without us creating them. That's tricky because it has a black box element. If I haven't built the model, haven't walked through the logic step by step, haven't verified every assumption, how much do I trust it?

That's a real concern for me. It becomes harder to have confidence in numbers when you haven't created them yourself, when you can't fully trace the path from raw data to final insight. In finance, we've always valued that auditability, that ability to explain exactly how we got to a conclusion.

With AI, we will need to shift that mindset. We'll need new frameworks to validate output, spot-check results, and build confidence in insights we didn't personally construct. It's a different kind of trust, and getting there won't be easy. But it's essential to unlock what AI can do for us.

Why AI can be a time sink for finance teams

We talk endlessly about AI's transformative potential, and expectations have been massive. But here's what I'm seeing on the ground: for most people in finance, it's taking more time right now, not less.

Why? Because they're learning a new technology, new vocabulary, new ways of working — while still running their old processes. The transition cost is real.

Most finance teams have access to tools like Copilot or Claude, and they can use those day to day for little tasks. But that's different from a fundamental process change. In other parts of the business, the impact is undeniable — coders are working completely differently; AI-enabled chatbots and client interactions are reshaping customer service. But in finance, we're still waiting for significant process transformations where the work itself changes dramatically, instead of just getting a bit faster around the edges.

I'm optimistic about where we're heading, but I'm realistic about where we are. The big value creation in finance is still ahead of us.

Christian Shares

For most people in finance, it’s taking more time right now, not less. Why? Because they’re learning a new technology, new vocabulary, new ways of working — while still running their old processes. The transition cost is real.

Why AI's real potential lies in FP&A

The low-hanging fruit is manual, transactional processes like expense handling, accounts payable, accounts receivable, and basic bookkeeping and accounting. AI excels at handling the volume and mechanics in these areas, offering the quickest payback.

But the real potential lies elsewhere: in financial planning and analysis (FP&A). AI could be truly transformative here. Today, we still view planning as a periodic exercise: building a budget, creating a forecast, and living with it for a quarter or a year. But imagine AI helping us move to continuous close and continuous updating of goals and forecasts. This would radically change how businesses manage daily performance.

Beyond planning mechanics, AI can help us with analytical work that currently takes weeks. This includes variance analysis, identifying root causes of deviations, and writing management reports that tell a story about business events. All of this becomes faster and richer.

So, start with transactional processes. But the bigger opportunity, the one that truly changes how finance runs, involves reimagining financial planning and analysis from the ground up.

Why finance leaders must shift from bottom-up to system-generated forecasts

For years, we believed that if every department and cost center carefully built its piece of the puzzle, we would create a useful long-term plan. But that approach is labor-intensive, slow, and it often disconnects from reality the moment the business starts moving.

Working with AI taught me that we can flip this entirely. Instead of building bottom-up and feeding it into a system, the system can generate driver-based forecasts, and then we verify and refine its outputs. It's faster, more grounded in business dynamics, and requires far less manual intervention.

That shift in thinking — from bottom-up construction to system-generated output with human verification — is changing my approach to financial planning. It means we're spending less time on the mechanics of aggregation and more time on the judgment calls that matter.

The most underestimated risk with AI implementation in finance

Leaders often underestimate a significant financial risk with AI: they fail to properly estimate implementation costs or build in the governance required for safe deployment.

The technology is powerful but expensive. Token costs add up quickly, proper implementation requires significant investment, and governance isn't cheap. Yet organizations rush in without adequately budgeting for either.

The consequences can be severe. Companies have already implemented AI with minimal governance controls, leading to systems deleting critical client data without permission. A recent example was a firm — PocketOS — who was left scrambling after a rogue AI agent deleted swaths of code underpinning its business. That kind of thing causes a catastrophic financial and reputational hit.

Beyond that, ROI presents another risk. Organizations can spend substantial money deploying AI solutions without seeing a return on investment, at least in the short term. Payback might eventually materialize, but if organizations haven't thought through the full cost picture — implementation, governance, training, and transitional inefficiencies — they can end up significantly over budget with disappointing results.

Don't underestimate the actual cost of proper AI implementation. Build robust governance from day one, even if it initially slows things down. And be realistic about ROI timelines. The financial risk of cutting corners is far greater than the cost of doing it right.

Why AI is a people problem, not a technology problem

I underestimated early on how resistant people are to radical change.

Getting people to adopt AI and fundamentally shift how they work is difficult. It's not a technology problem. It's a people problem. We're asking people to change their mindset entirely, to look at their jobs in a completely different way. That's massive. And it won't happen simply by deploying a clever system.

The human element here is critical. Technologically, AI is fantastic. But successfully rolling it out requires a massive change management initiative on the people side. You need to help people understand why they should care, how their role is evolving, and what success looks like for them personally. You need to address the fear, the uncertainty, and the loss of control people feel when their day-to-day work gets reimagined.

I wish I'd prioritized that earlier because it's where the real work is. It's where transformations succeed or fail.

Why finance leaders must become change agents

Soft skills are becoming far more critical, not less.

AI can handle the technical aspects of finance — the data crunching, the modelling, the analysis. So what becomes genuinely scarce and valuable? People skills: the ability to influence others, build trusted relationships, and communicate in a way that resonates and sticks. Because trust isn't just about delivering an insight — it's about a relationship built over time.

We need finance professionals who can tell stories, paint pictures in people's minds, and understand how to connect emotionally with their audience. These are people who can drive decisions and move organizations through change. That's where the premium goes as AI becomes more prominent.

So, the profile shifts dramatically. You're no longer just a data person who crunches numbers. You become a change agent. You become someone who can partner effectively across the business, who builds credibility, and who knows how to navigate organizations and influence outcomes. Those are the capabilities that will separate exceptional finance professionals from the rest.

That marks a significant shift in how we think about finance talent and development.

Why finance teams must become agile now

My best advice right now, though it sounds a bit cliché, is for finance teams to stay agile. Frankly, finance isn't naturally good at this.

I recently spoke to fifteen hundred finance and IT people at a global tech company. I designed an agile finance framework for them, adapting the classic Agile Manifesto into something that resonates with finance people. It landed really well when they saw it. But when I asked if they lived those agile values day to day, only about sixty percent said yes. Forty percent admitted no. So even when finance teams embrace agility, they struggle to execute it consistently.

That's the mindset we must crack with AI. We don't know where this is going. We need to be experimental at both the personal level, by improving our own efficiency, and the process level, by rethinking systems of work. We must be comfortable running pilots, knowing some will fail and others won't deliver positive returns initially. That's how you build an AI muscle. That's how you develop real capability.

Christian's Advice

My best advice right now, though it sounds a bit cliché, is for finance teams to stay agile. Frankly, finance isn’t naturally good at this. That’s the mindset we must crack with AI. We don’t know where this is going. We must be comfortable running pilots, knowing some will fail and others won’t deliver positive returns initially. That’s how you build an AI muscle. That’s how you develop real capability.

Follow along

You can follow Christian Franz Hansen's work on LinkedIn. And check out his book, Communicating Financials to Executives.

More expert interviews to come on The CFO Club!