In today’s fast-evolving business landscape, the role of the CFO has transformed beyond traditional finance responsibilities. According to a KPMG survey, 46% of companies now see artificial intelligence (AI) as a top growth enabler. Armed with cutting-edge technology, data-driven insights, and a keen sense of risk management, CFOs are uniquely positioned to lead their organisations through the next wave of digital transformation.
As industries embrace AI and automation, CFOs must play a pivotal role in integrating these technologies to stay competitive. This article delves into how CFOs can navigate the complexities of AI adoption and make smarter budgeting decisions by leveraging solutions that embrace the technology to accelerate digital transformation within the finance function.
The Expanding Role of AI in Finance
According to IBM’s latest study, CEOs are willing to take bold risks to stay ahead, with 62% increasing risk-taking to maintain their edge. CFOs need to use their influence and lean into risk, making strategic data-based decisions that align the organisation’s risk appetite with its financial well-being.
Despite its proven benefits, AI’s potential in finance remains largely untapped, with only 34% of finance operations operating or optimising traditional AI. For finance teams, AI can streamline tasks from invoice processing and payroll management to fraud detection and forecasting. Beyond efficiency gains, AI offers CFOs deeper insights into financial trends, helping identify areas for cost reduction, optimise cash flow, and improve strategic planning.
But what are the most valuable AI investments for the finance function? How should CFOs prioritise AI projects? And how can CFOs ensure that AI initiatives drive tangible results?
Key Challenges for CFOs Adopting AI
While the potential for AI to transform finance is vast, CFOs must carefully manage the complexities of AI adoption. In a 2024 Gartner survey, it was found that 58% of finance teams are already using AI, with 21% planning its implementation. However, implementing AI is not without its challenges. CFOs need to consider costs, integration, talent, and risks.
With Touchstone’s experience of working with finance teams across a multitude of sectors, here are some of the key challenges we’ve identified:
Talent Acquisition and Training: Implementing AI demands more than just bringing in technical experts; it also requires upskilling existing finance teams to effectively collaborate with AI tools. It is crucial to account for the “soft costs” of training, change management, and user adoption in your budget, as these factors can significantly impact the success or failure of an AI rollout.
Garbage in, Garbage out: Factoring data refinement and management into your AI budget is critical. Data quality is essential for AI efficacy, so CFOs will need to budget for cleaning, normalising, and cataloguing it ready for AI processing, and for continuous data maintenance and storage.
Additionally, data needs to be properly managed if it is to be integrated with other systems. Poor data quality or system integration can prevent AI from performing effectively. This is where tools like TimeXtender come in, helping business build a data infrastructure capable of consuming, transforming, modelling, and distributing clean, reliable data from a plethora of sources to ensure AI can operate and deliver to its full potential.
Managing Risks: Data security, compliance, and ethical concerns are major risks associated with AI. CFOs must account for these factors when planning their AI strategy, ensuring robust security measures and legal safeguards are in place.
How AI in Finance Can Support Businesses Financial Objectives
Software authors such as Hexagon, Sysynkt, systems@work, TimeXtender and Yooz are pivotal in driving this transformation by harnessing AI to automate routine financial processes. When integrated into financial management systems such as Infor SunSystems, these applications not only boost operational efficiency but also empower CFOs to make real-time, data-driven decisions with greater precision.
Here’s some examples of how these applications can help businesses achieve their financial goals:
- Using AI document recognition to remove the manual effort of keying in purchase invoices, matching against POs and routing for approval based on configurable workflow engines
- Using Machine Learning (ML) capabilities to auto-populate account codes and analysis dimensions based on previous documents, this reducing manual effort
- Assisting with Fraud detection by checking the supplier database against invoices for items such as company number, VAT registration number and bank details
- Matching bank statement lines to the ledger, reducing the need to perform manual reconciliation
- Reading information from expense receipts, classifying the type of expense and extracting amounts and descriptions, plus additional information such as the vendor, allowing additional analysis and insights to be made
- Inputting expenses conversationally, with automatic categorisation and structured reports
- Matching credit card statements against matching expense receipts
- Utilising AI-powered chat-bots to surface information from documentation, knowledge bases, training manuals etc. to allow users to retrieve data that relative to them in the context that they are working in as opposed to trawling through numerous data sources to find the answer
- Using AI-powered assistants to produce insights on the transactional data within the source system that would otherwise need to be generated manually.
In conclusion, the integration of AI and finance automation is not just a technological upgrade but a strategic imperative for modern CFOs. By implementing applications that are leveraging AI, CFOs can transform their finance departments into agile, data-driven powerhouses.
Want to find out more about how finance leaders can embrace AI adoption? Reach out to our team today at info@touchstonefms.co.uk.
