7 steps and key learnings to kick-start your AI journey in finance

Everybody’s talking about AI, of course. But, for the finance leaders I speak with, the conversation goes beyond idle chatter about which tool is which!

What CFOs are discussing is the profound transformation in finance enabled by AI. We see the latest technologies moving the function beyond simple automation to influence strategic organizational decision-making.

What I hear time and again is that technology alone is not enough. The success of intelligent automation in unlocking efficiencies depends on building a foundation of clean data, and having in place both mature processes and capable, confident teams.

It’s especially interesting, isn’t it, that last point? That AI success is all about people.

Because, while the chatter may be about sidelining the human element, finance leaders appreciate that true AI readiness requires a cultural shift. You need to foster a preparedness to act on the new world of insight that intelligent automation brings.

That makes it a strategic shift, too, with the CFO's role expanding to encompass change management, balanced against the traditional need for control.

What really matters to you: practical value

For CFOs like yourself, the pressure is on to not only adopt the technology but to demonstrate tangible returns, while remaining accountable for operational stability. That’s why you seek a practical, finance-specific roadmap that will bridge the gap between promise and reality.

Chatter aside, the focus of recent conversations about the ROI of implementation has moved away from direct, measurable cost savings. Perhaps you’ve experienced this complexity within your organization, justifying AI’s value in less obvious, even ‘hidden’ areas?

I am seeing that operational and strategic gains can be a major AI benefit. And the consensus is that landing this message is critical to building a compelling business case for investment.

Another consensus emerging among CFOs is that applying AI to flawed processes and bad data is a critical error. Inconsistency across global organizations blurs the detail at higher corporate levels.

That’s why process and data integrity must precede AI rollout—otherwise, far from fixing foundational issues, you risk making matters worse.

Balancing the revolutionary and evolutionary

The new capabilities AI brings are changing everything; truly, this is a technological revolution.

For one thing, there’s the core technical shift, with modern AI able to mimic the way a human brain thinks. That is to say, it’s no longer based on the old, fixed "if A, then B" rules, which previously governed automation.

Another way in which AI is revolutionary is the agentic model we hear so much about: autonomous digital agents, AKA "personas." This is AI capable of handling complex, high-value work previously done—painstakingly—by human experts, such as FP&A, and business partners.

However, my point about balance is that your focus must remain on human challenges. AI is a revolution, but its implementation is an evolution—for every organization.

Success hinges not on AI’s competencies, but on our own, and on our confidence regarding it. That’s why your role is evolving to encompass change management; it falls to you to lead the necessary cultural transformation.

So, to ensure both business stability and successful user outcomes, implementation should proceed in carefully managed steps:

1. Focus on people-centric challenges. Start by honestly assessing and acknowledging your team’s readiness and skills gaps.
2. Bridge the "digitalization literacy" divide. Be aware that for veterans, despite their deep business acumen, AI is an unknown.
3. Manage the "overwhelm" because the speed of change and the technology itself are daunting for many, as is the label ‘transition’.
4. Optimize your human resources. Create "mixed squads" pairing experienced staff with tech-savvy colleagues, for shared learning.
5. Take a broader view of value. Secure buy-in across leadership and the wider business by looking beyond your own savings.
6. Share every learning including that the most significant ROI of AI  may well be in operational areas, potentially saving £millions.
7. Put new insights to work. Aware that intelligent automation of routine compliance tasks is a strategic game-changer. Be ready!

So, what has got us to this point?

Coming so soon after the global pandemic—with the subsequent shift to remote working—the advent of AI has only added to a key trend exposing operational weaknesses. Better visibility into workflows is essential to facilitate the removal of redundant processes, unnecessary reports, and wasted time.

The next frontier for AI is GRC (Governance, Risk, and Compliance), particularly in interpreting unstructured regulatory documents, as changes such as the upcoming IFRS rules present new challenges. Similarly, the complexity of operating across multiple geographies and currencies fragments financial information.

The next frontier for AI is GRC (Governance, Risk, and Compliance), particularly in interpreting unstructured regulatory documents, as changes such as the upcoming IFRS rules present new challenges. Similarly, the complexity of operating across multiple geographies and currencies fragments financial information.

Now, by automating even high-level, yet routine tasks, teams are freed from their spreadsheets to focus on new challenges. Their time can be better spent on high-value analysis and decision-making, moving beyond routine reporting to turn compliance tasks into revenue-driving insights.

Only at the beginning of a new, multi-generational technology lifecycle, your key opportunity is in elevating finance to a full strategic partner. One CFO told me they’re using data to "act as the GPS for the organization."

How about some actual success stories?

From the recent experiences of CFOs I know, here are three significant case studies:

Implementing process modernization

The home-working revolution during the pandemic exposed a critical lack of visibility into team workflows for any number of businesses. For one I know of, the crisis instigated a mapping-out of its entire order-to-cash process, data, and systems, exposing legacy steps and needless reporting.

As a result, inefficiencies were eliminated and team members feel more connected, with a better understanding of individual roles’ impact on the whole value chain.

Dialing up operational efficiency

A global consumer goods company was examining an overlooked operational area: plastic film for product packaging. Data analysis revealed a significant hidden inefficiency the company had built up over the years: using 80 different SKUs for this simple, non-consumer-facing item!

The solution was to consolidate to just 20 SKUs, which has seen the business save some $17 million annually, showing how major ROI can be found in unexpected areas.

Making strategic insight work harder

I was interested when a financial services company I know transformed a routine compliance task, analyzing transactional losses, by applying machine learning to its data. The result? The finance team was able to identify specific patterns related to different countries, products, and seasonality. As a result, a standard finance task has become a valuable strategic insight for both product and revenue teams, directly informing business strategy on a global scale.

In summary, key learnings

1. A successful AI  strategy requires a dual approach: Embrace AI as revolutionary, but implement it step-by-step, as an evolutionary rollout to ensure stability.
2. Foundational readiness is non-negotiable. Apply AI to broken processes or flawed data, and far from solving your organization’s existing problems, you’ll amplify them!
3. The human element is critical to success. So, there’s a shift in CFOs’ focus. You need to build team confidence, address skills gaps, and manage change effectively.
4. Significant ROI from AI can be found in less obvious areas, from operational improvements to new strategic insights that sit outside the core finance function.
5. Considering next steps, start by assessing foundational readiness. Identify key areas where you need to see data integrity and process maturity improvements.
6. Next, invest in people; build your team's skills and confidence alike. This is a vital prerequisite for investment, before you start scaling your AI initiatives.
7. Finally, broaden the search for hidden AI value. Look for pilot projects where data and AI capabilities can solve problems within the finance function, with a tangible impact across the wider enterprise.

These practices can help CFOs transform their teams for a brighter future with greater automation and proactive, AI-powered support for decision-making.