Driving digital transformation in finance: Overcoming challenges & achieving measurable results

I’ve spent the last two decades working on organizational effectiveness and digital transformation across industries, from automotive and banking to big tech and, most recently, pharma.

Along the way, I’ve learned that while the technology evolves quickly, the real challenge in transformation is rarely the tools - it’s the people, the data, and the strategy behind it.

In this blog post, I want to share how I see digital transformation unfolding in finance today, why AI is changing the game, and the framework I use to make transformation real. 

What digital transformation really means

When people talk about digital transformation, it often sounds like something brand new. But in truth, it’s been happening for decades. The introduction of Excel was digital transformation. Even moving paper records to ERP systems was classed as digital transformation.

At its core, digital transformation is organizational change through digital tools and business models with the goal of improving performance. 

So, the key question for finance leaders isn’t “Should we do this?” It’s: How do we make sure our investments deliver real value instead of chasing shiny tools? 

Why now? The perfect storm for change

The reason transformation feels urgent today is because several forces have converged:

• Computing power has exploded - what once took mainframes now fits in our smartphones.
• Real-time connectivity is universal - 5G, cloud platforms, and collaboration tools make instantaneous information sharing possible.
• Data is everywhere - sensors, transactions, and digital footprints give us a flood of information.
• Costs are falling - storage and processing are cheaper than ever.

This convergence has also transformed consumer (and employee) expectations. People are used to seamless, personalized digital experiences in their daily lives, and they expect the same inside the enterprise. 

For finance teams, this creates both opportunity and pressure. AI models, cloud platforms, and analytics tools have become commodities. Any company can buy them.

The real competitive advantage lies in data quality, data accessibility, and data governance. 

Finance is uniquely positioned here. As the function that sees across operations, supply chain, and commercial activities, we’re often the “goalkeepers of enterprise data.” That gives us a responsibility (and an opportunity) to lead digital transformation. 

AI: From winter to breakthrough

AI may feel new, but it has a long history. After early promise in the 1960s, the field went through an “AI winter” in the 1970s when the technology wasn’t ready. Advances in machine learning and deep learning reignited momentum in the 2000s. 

A few milestones stand out: 

• 2016: Google’s AlphaGo defeated a human Go champion, making an unprecedented move that demonstrated machine creativity.
• 2017: The landmark paper Attention Is All You Need laid the foundation for today’s large language models (LLMs).
• 2023: Generative AI tools like ChatGPT hit the mainstream, shifting expectations almost overnight.

Today, machines already outperform humans in some areas and are catching up quickly in others. For finance, this means rethinking how we forecast, analyze, and advise the business. 

It’s also important to distinguish between:

Digitization: Converting paper processes to digital format.

Digitalization: Redesigning processes and business models to create new value. 

Finance must resist the trap of implementing tools for the sake of “being digital.” Instead, we should ask: Does this technology improve performance and decision-making? 

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A simple value-creation model 

I like to think about digital transformation through three layers: 

1. Data (Sensors & Inputs): The raw material (everything from IoT sensors to survey responses).
2. Models (Intelligence): The algorithms, prompts, and applications that turn data into insight.
3. Platforms (Execution): The systems that connect data and models to decision-making at scale. 

One of my favorite examples is a virtual grocery store in a Seoul subway station. Commuters scanned products on digital shelves, ordered via their phones, and had groceries delivered by the time they got home. It combined real-time data capture, intelligent processing, and fast execution into a seamless new business model.

Finance can take the same approach to budgeting, forecasting, compliance, and beyond.

Finance use cases in practice 

Across industries, I’ve seen several powerful applications of AI and digital tools in finance, such as:

• Duplicate payment prevention
• Automated reconciliation and close support
• Financial forecasting and scenario modeling
• Generative AI for reporting and chatbot support 

A practical example: with Microsoft Copilot, a finance professional can start the day by asking Excel for a data summary, have Teams auto-generate meeting notes, run scenario analyses in minutes, and instantly turn outputs into a presentation.

That’s transformative efficiency. But it still requires human judgment. Even when using your own company’s data, you must double-check outputs before presenting or acting. 

Trends shaping finance transformation

In pharma (and broadly across industries), I see five trends dominating: 

1. Increased automation to improve efficiency.
2. Personalized experiences for both customers and employees.
3. Rapid AI investment, though ROI isn’t always clear.
4. Deeper integration of AI in core processes (e.g., diagnostics in healthcare, forecasting in finance).
5. Heightened focus on security, compliance, and sustainability.

For finance, these trends point to a future where we’re expected to deliver not just reports, but predictive and prescriptive insights that shape business strategy.

Challenges and risks

Transformation isn’t smooth sailing. The technology is often the easy part—the real hurdles are organizational and structural. From my experience, the biggest barriers are: 

1. Alignment with company strategy

Digital transformation can’t be something finance does in isolation. If your company’s broader strategy is focused on customer intimacy, but finance is investing in automation purely for cost-cutting, you’ll create friction instead of value. 

Transformation must be anchored to enterprise goals. That means finance leaders need a seat at the table early, shaping and aligning digital initiatives with the company’s north star. 

2. Data quality and variety

We often say “garbage in, garbage out,” but in digital transformation, the stakes are higher. With AI models and predictive analytics, poor-quality or inconsistent data doesn’t just limit accuracy, it can actively mislead decision-makers.

Finance sits at the intersection of so many data streams (operational, commercial, supply chain, HR) that we need strong governance to harmonize them. Without that, digital tools won’t deliver their promise. 

3. Skills gaps and workforce disruption 

Many finance teams are still built around traditional reporting and compliance tasks. But the future of finance requires fluency in analytics, data visualization, automation, and AI. Upskilling existing staff is crucial, but so is rethinking roles. 

Do we build capabilities internally? Do we bring in new talent? Or do we partner with external providers? Each path comes with trade-offs, and leaders need to manage the cultural impact as much as the technical one.

4. Regulatory complexity and compliance

Finance is already one of the most heavily regulated functions. Layer on top of that emerging AI regulations (like the EU AI Act or U.S. state-level privacy laws), and the landscape gets even more complex. 

For global companies, keeping pace with overlapping and sometimes conflicting rules is a real challenge. Non-compliance isn’t just a reputational risk, it can also mean legal exposure and financial penalties.

Because of these risks, many companies are moving toward private AI environments. Instead of relying on public large language models, they’re building controlled ecosystems: versions of AI tools trained only on company-approved data, with strict governance over inputs, outputs, and access. 

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A framework for digital transformation in finance

Here’s the framework I’ve applied in my work: 

1. Assess digital maturity: Where are we starting from? What’s our current capability in data, processes, and tools?
2. Align with company strategy: Finance’s digital strategy must connect directly to enterprise goals.
3. Identify focus areas: Use maturity assessments to decide where to invest first. 
4. Build digital culture: Data governance, predictive modeling, business process redesign, and upskilling.
5. Create a digital portfolio: Concrete initiatives like predictive forecasting, workflow automation, or smart dashboards. 
6. Establish a platform for collaboration: Share insights, invite innovation from across the company, and integrate with HR, IT, and business units. 

Throughout all of this, remember: Technology is 30% of the work. People are 70%. Change management, communication, and engagement are the true success factors.

The role of finance in leading transformation

So, who should lead digital transformation? In my view, no single department can do it alone. But finance is uniquely positioned to take the lead.

Why? Because finance has visibility across the entire organization. We see operational data, commercial data, supply chain data, and costs - all in an aggregated way. That vantage point makes us a natural driver of transformation, provided we step into the role.

This article is based on Juan Ignacio Pascual's brilliant talk at the Finance Transformation Summit. Finance Alliance members can enjoy the complete recording here.