Today’s CFOs are expected to be strategists, operators, risk stewards, and change drivers. Internal and external stakeholders demand agility, transparency, sharper insights and clear strategic goals. With AI, Finance functions are now more enabled than ever to address these ever-growing priorities, by leveraging opportunities provided by AI to speed up processes, improve accuracy and redeploy talent to where it matters most. But where we see most value being created is in the finance functions which do not look at AI only for efficiency gains, but look to transform finance into a predictive, strategic, and value-driven function.
AI value is delivered across three core dimensions
AI brings substantial value across every domain of the finance function, typically starting with use cases that address transactional pain points and then scaling to strategic impact. AI use cases in finance typically deliver value across three dimensions, with the best use cases touching upon two or more of these dimensions and addressing strategic transformation:
- Efficiency: Speeding up routine processes and reducing manual effort.
- Compliance: Minimising errors and strengthening regulatory adherence.
- Better insights: Enabling faster, smarter analysis for decision-making.
While there is no 'one size fits all’ or one single blueprint for AI adoption, the one constant is clear: a commitment to action anchored by a well-defined vision.
Choosing the right AI technology matters
Navigating the AI landscape can be complex, as not all technologies are created equal and deliver different value:
- Machine and deep learning: Strengthening data analysis and predictive capabilities.
- Generative AI: Enhancing narrative reporting, scenario planning, and stakeholder communication.
- Agentic AI: Orchestrating end-to-end processes with minimal human intervention.
The most successful organisations understand that the synergy between various AI and traditional solutions is the key to unlocking true value and that the transformative power of AI does not mean replacing all existing systems nor starting from scratch. In fact, AI delivers the greatest value when its capabilities are integrally layered on top of each other as well as on top of robust backbone (traditional) existing systems. By integrating AI with what already works well, organisations can unlock new efficiencies and insights without disrupting the foundations that underpin their finance function.
Consider standard AI solutions first
And companies are not alone in this journey. The software market is trying to keep up, with software providers increasingly embedding AI into their offerings (ERP, EPM, process dedicated solutions, cloud providers) and, with capabilities of building custom tools becoming more widely available in the market. To build a sustainable finance technology landscape, we recommend evaluating standard off‑the‑shelf capabilities before investing in custom solutions. Explore our technology alliances with leading software providers here for an overview of what these solutions can offer.
There are five lenses for a successful transformation
For a successful AI transformation, we see a holistic approach from five perspectives. In our experience, each perspective has unique challenges organisations face which have solutions both in the short term as well as long term, allowing organisations to flexibly define their AI journey. Some of these challenges we detailed below.
Challenges include fragmented and low-quality data, an abundance of AI solutions making decision-making difficult, and treating AI as a standalone initiative leading to isolated adoption. Organisations would do well to start with pilot projects aligned to business priorities, establish a clear AI strategy, and integrate AI within broader IT strategies for scalability and cohesion.
Resistance to change, insufficient skills, and a lack of effective change management can hinder AI adoption. Transparent communication about AI as a supportive tool, targeted upskilling, and embedding AI in company values are crucial. Change management should be implemented from the outset and continuously to build trust and align behaviour with AI integration.
Legacy processes are often not ready for AI, and fragmented AI governance increases the risk of duplication and compliance issues. Organisations are best off piloting AI in small-scale processes while redesigning workflows for collaboration between people and AI. Strong governance is essential for oversight and accountability.
Scaling up AI requires a revision of work structures, outsourcing strategies, and collaboration between people and AI. Organisations can accelerate progress through inspiration sessions and appointing AI champions, while developing a hybrid operating model that integrates AI-driven and human roles in line with strategic objectives.
As AI penetrates deeper into the financial world, so too does the responsibility. The need to act ethically and sustainably is increasing. This means attention to data security, transparency in processes, and an eye on the environmental impact of technological choices.
Start your AI journey today with these practical steps
Getting started with AI in finance is all about taking clear, purposeful steps. For financial leaders, this means mapping out current AI capabilities and creating an environment where change is encouraged. Some of the key steps we recommend include:
- Assess your current landscape to understand existing AI and IT capabilities.
- Inspire and lead change by engaging leadership and fostering a culture of innovation.
- Identify and launch impactful pilots to build confidence and demonstrate value.
- Align your strategy and roadmap with business objectives for sustainable progress.
- Support your teams with skills and new ways of working and establish strong governance.
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