Beyond automation: The CFO’s role in AI adoption

As AI capabilities continue to advance and become increasingly common in everyday use, finance and accounting leaders have an opportunity to rethink how work is performed and where value is created. 

For Chief Financial Officers (CFOs), this means looking beyond efficiency gains and exploring how AI can contribute to decision making and the organisation's broader strategic direction.

In a recent RSM Middle Market AI Survey in the United States, 91% of respondents confirmed they are using generative AI in their organisation, however only 25% said it is fully integrated across core operations or workflows. Importantly, 92% said they experienced challenges with implementation, and 70% needed external support to get the most out of their AI solutions.

Leading AI adoption from the finance function

CFOs today often sit across technology, governance and business operations and it is unsurprising many are taking a leading role in AI adoption across finance and the wider business. To succeed, this requires a clear understanding of where AI can advance organisational goals, the risks it introduces, and how to balance innovation, governance and long-term benefits.

CFOs have four key areas of responsibility for guiding comprehensive and successful AI adoption and integration:

1. Vision and strategy: Establish a clear vision for AI integration in finance teams – outlining its strategic purpose and potential to enhance efficiency, innovation, and business growth.
2. Build confidence through leadership: Actively use AI tools for key processes and decision making, combined with continuous learning about new developments and emerging capabilities to champion adoption and demonstrate value to the wider organisation.
3. Promote responsible and effective use: Prioritise responsible AI use by establishing ethical guidelines, promoting sound data practices, ensuring data integrity for dependable results, and monitoring AI systems to support reliable outcomes while managing risk.
4. Implementation and change management: Focus on targeted AI pilot projects to showcase value, carefully select AI use cases that deliver ongoing efficiencies, and proactively manage change through clear communication and ongoing stakeholder engagement.

Opportunities for finance leaders

Finance functions have significant potential for AI optimisation and improvement. By effectively leveraging all forms of automation, finance teams can drive efficiencies and performance through:

• Analysing: AI can help improve the accuracy of reporting by identifying errors, anomalies and inconsistencies within large datasets. Analysis through automation can also provide deeper understanding of revenue streams, customer channels, and performance.
• Predicting: By analysing historical data and identifying patterns, AI can improve forecasting activities, detect unusual activity, and assess risks such as fraud or delayed customer payments. 
• Generating: AI can assist with preparing reports, summarising information, answering questions, and drafting documents such as accounting policies to reduce administrative burden on finance teams.

Identifying an opportunity to automate does not automatically mean AI is the right solution though. Finance leaders must assess each on its merits and determine which technology is best suited to the task. Other advanced technology tools, such as robotic process automation (RPA), may provide a simpler and more cost-effective solution than AI.

The goal should be to solve business problems rather than adopt technology for its own sake. The question “do you need AI or would RPA suffice?” should always be considered. In addition, consider the benefits of leveraging AI built into existing toolsets compared to a custom model factoring in costs, security and data quality.

AI applications in finance

There are a multitude of RPA and AI-powered tools to increase efficiency, boost productivity and strengthen business insights. Some businesses will benefit from a single application, but multiple AI tools can work together to address complex challenges. Developing a strong AI use case requires a strategic approach that starts with identifying key areas and establishing a framework. Clearly defined business goals and aligned AI are key to achieving the designed objectives.

Finance teams can accomplish several key objectives from leveraging AI functionality pre-built into everyday business tools, such as these Microsoft applications:

Microsoft Copilot Studio: Builds and deploys AI agents. 
Power Automate: Utilises RPA to automate workflows and repetitive tasks. 
Azure OpenAI Service: Delivers advanced AI language models for integration with applications. 
Azure Storage: Provides secure, scalable cloud storage. 
Azure Functions: Executes event-driven processes. 
Azure AI Search: Enables AI-powered search and data retrieval.

CFOs can also leverage AI features and functionality in digital tools and applications such as:

• NetSuite: Provides AI-powered forecasting, anomaly detection and business insights. 
• Workday Adaptive Planning: Used for predictive forecasting and scenario planning, incorporating enterprise AI to eliminate manual number crunching, automate variance analysis, and support ‘what if’ scenarios. 
• Optical character recognition (OCR) tools: Increasingly use AI language models to understand document structure and context to improve accuracy.

The importance of AI governance

As CFOs embark on deploying AI tools and applications into the finance environment, the importance of AI governance cannot be understated.

In a recent podcast, Unpacking AI and data governance, RSM’s Andrew Sykes, Srdjan Dragutinovic and Gerard Sayers took a deep dive into this very subject.

For CFOs, key takeaways include the growing need to consider:

Governance and accountability: Clear ownership, approval structures, and governance frameworks are required so responsibility and accountability for AI does not become fragmented across the organisation.

Data security: Implement strong controls around the quality, access, lineage and usage of data – particularly as AI expands reliance on unstructured and shadow data.

Risk, ethics and oversight: AI outputs should be subject to appropriate review and challenge, particularly in relation to financial reporting or forecasting decisions. There must be clear guardrails around accuracy, bias, traceability and the circumstances where human judgement is essential.  

Ongoing change management: Governance must move beyond static policy documents and become embedded in day-to-day operations and behaviours. 

As CFOs find practical ways to balance AI innovation and risk, proper governance will give them greater confidence in taking action to unlock its full potential.

The path forward for CFOs

As more AI solutions rapidly become available for finance leaders, there is increased need for the right AI strategy across the organisation. CFOs must continue assessing where to focus their time, resources and investment, evaluating:

• opportunities that justify investment
• processes that are suitable for automation
• capabilities that need to be developed internally.

It also means recognising AI is only one tool in the technology landscape, and not every challenge requires an AI solution.

If your organisation is ready to move from experimentation to thoughtfully executed implementation, our CFO Advisory Services team can help. We work closely with clients in conjunction with our AI and data analytics teams to:

• Assess AI readiness
• Develop an AI strategy
• Consider potential solutions
• Support procurement and implementation
• Assist with change management and ongoing operations

When the roadmap is built on well-considered decisions and underpinned by strong governance, organisations are better positioned to realise the full value of their AI investments.

For more information on how we can assist CFOs with AI adoption, please contact our CFO Advisory Services team.