As the use of generative AI continues to expand, and the possible applications for it continue to grow, it is gaining significant attention from finance leaders.
It is still too early to tell how AI will change our world, but already organisations are seeing immense potential for AI tools across many industries and all business functions.
How successfully a business integrates AI into all its workflows and operations will determine its future, and so finance leaders should support and champion the transformation — for five reasons:
Business model transformation
AI helps businesses develop new products and services or enhance existing offerings to benefit customers by incorporating advanced features such as predictive user consumption, enhanced usability, and smarter functionalities that adjust to user preferences over time.
As an example, Spotify, the music streaming service, has continually leveraged AI to expand its platform and evolve its business model whilst improving user experience and engagement.
Spotify invested in AI tools more than a decade ago. In November 2024 its monthly active users had increased year on year by 11% to 640 million, with 252 million subscribers. Its total revenues were up — by 19% — to $4 billion, and the company said it was on track to a full year of profitability. Its growth from 36 million users in 2013 has been powered by an ongoing business transformation focusing on investment in AI to personalise music experiences and expand its podcast and its targeted advertising businesses.
Competitive advantage
Despite concerns about AI-generated music on the platform and the company’s royalty payment model, Spotify has used AI to enhance its competitive advantage in the music streaming industry. The company has used AI algorithms to analyse user data to provide tailored music recommendations. Features such as Discover Weekly, Daily Mix, and Release Radar are innovations that have given Spotify a competitive advantage by giving each user a personalised experience, driving engagement and user satisfaction.
This dynamic approach keeps content fresh and makes users more likely to stay engaged with the platform longer, increasing the likelihood of subscription renewals and reducing churn rates.
These features have helped Spotify grow its market share to more than 30%, far ahead of competitors such as Tencent Music, Apple Music, YouTube, and Amazon Music.
Efficiencies and productivity gains
For many businesses, AI is used to automate various backend operations, which reduces operational costs and repetitive tasks — not only improving accuracy in business-critical processes, but also job satisfaction and user experience.
AI can automate routine tasks such as data entry, processing transactions, and handling routine customer inquiries. This speeds up processes and reduces error rates — allowing employees to focus on higher-value and more complex tasks.
AI has use cases across all industry segments. In healthcare, it is delivering faster diagnosis and better care, detecting disease patterns for diagnosis and treatment. In agriculture, it is predicting ideal planting and harvest windows and powering smart harvesting systems. In industries that use heavy machinery and equipment in manufacturing, it helps manage thousands of variables to deliver efficiency and productivity gains. In financial services, it can provide smarter and safer banking, improving fraud prevention. In education, it can help adapt learning to suit individuals’ specific preferences, adjusting content difficulty and teaching methods in real time.
AI algorithms can also analyse vast amounts of data and detect patterns to optimise logistics and supply chain operations. This includes routing thousands of origin-destination combinations to select the fastest transit time and lowest cost, managing inventory, and forecasting demand.
AI is driving a revolution in marketing, enabling true personalisation at scale by analysing a vast amount of data related to customer preferences to serve customers according to their needs.
Decision-making
Business partners identify a significant gap between what they need and what finance delivers in its business guidance role. AI should be leveraged to close this gap.
AI supports decision-making by providing actionable insights derived from data analysis. It can process and analyse large datasets faster than human analysts. AI is also able to identify trends, flag anomalies, and highlight opportunities that might not be immediately apparent.
The technology uses historical data and machine learning models to make predictions about future trends. This is particularly useful in areas like demand forecasting and resource allocation. Predictive analytics can anticipate problems, suggest preventive measures, and recommend resource adjustments, thereby improving the decisions of short-, medium-, and long-term capital and working capital allocations.
AI improves both the quality and speed of decision-making. This capability enables more informed, data-driven decisions and enables an improved business guidance function.
It also provides the capability of multiple scenario-planning iterations that set up a business for rapid response if a particular situation occurs. Businesses need to be prepared for multiple scenarios in a rapidly changing landscape. The geopolitical impact of the US elections, for example, required organisations to begin planning well before election day for which administration might take office and how taxes, tariffs, and fiscal policies could play out over the next four years.
Risk reduction
AI can detect patterns in large volumes of historical data to predict future risks. Credit card companies protect their customers from fraud by identifying anomalies in their purchasing behaviour, based on their history.
Using algorithms, AI detects outlier transactions and highlights anomalies for further investigation. All of this can be done in real time, enhancing detective controls in organisations and reducing incidences of fraud.
In addition, credit managers deploy AI models to predict credit risk by analysing transaction history and customer spending behaviour to identify signs of potential default.
For cybersecurity, AI algorithms can detect patterns indicative of potential security breaches. They can also be trained to create automated responses to mitigate an identified risk, to respond to counteract a cyber breach and isolate impacted systems, and to deploy security patches. This limits damage and improves response times.
AI is used across Spotify’s technical infrastructure to reduce risk. Spotify maintains a high level of platform performance and availability through AI-driven predictive maintenance. This enables high service levels without disruptions due to outages.
Banks are able to analyse multiple data points in transactions, with AI spotting fraudulent patterns to identify probable money laundering activities.
Ensuring the investment delivers
Now that they recognise AI’s potential, significant AI investments are being made by businesses, governments, and not-for-profits. Finance leaders need to guide their organisations through these investment decisions, cutting through the hype and ensuring that their AI implementation and integration is successful. Finance leaders must ensure that:
Staff are adequately trained
AI training is not just for the IT function, data scientists, and engineers. AI literacy is required across the entire organisation. The level of change introduced by the incorporation of AI into workflows is significant. To ensure that the full potential of the AI investment is realised, the finance leader must ensure that the business has adequate funding for staff training.
AI competencies should be articulated, and the workforce should be trained in the new AI tools and given basic training on how algorithms work. As the field of AI is continuously evolving, there is a need for ongoing training to ensure that there is an awareness of the latest developments and tools available.
Cross-functional collaboration occurs
AI deployment across the business changes handover points between functions as transactions become “touchless”, redrawing functional boundaries. New functions emerge in an AI-enabled organisation. Embedding AI in processes and workflows also changes controls, checks, sign-offs, and approvals. All of these should be addressed in any organisational design changes.
Ways of working will change as AI gets embedded across the organisational structure. Greater cross-functional collaboration is essential in the design and implementation phase of the AI roll-out to avoid confusion and ensure all constituents’ needs are identified and addressed.
Investment in innovation continues
The advent of generative AI has happened rapidly over just a couple of years. It is essential that the organisation invests in dedicated AI innovation labs that will monitor new technologies and leverage cutting-edge AI solutions aligned with their business needs.
Spotify, for example, has been consistently investing in AI capabilities. Finance leaders should ensure that capital is made available for continued investment in innovation to maximise the impact of AI investment.
Attention is given to data quality and governance
AI models rely upon the quality, integrity, and relevance of the data being used to train the model. The investment in AI will be rendered useless if the data used to train the models is inaccurate and not fit for purpose. Finance leaders should also ensure that proper governance and controls are put in place before data starts to drive decision-making. It is essential that the finance office establishes frameworks for data governance and ensures that data is accurate, consistent, and secure.
High-quality data governance will enhance the performance of AI systems and also build trust amongst users by safeguarding sensitive information and ensuring ethical AI practices.
Ash Noah, CPA, FCMA, CGMA, is vice-president and managing director–Learning, Education & Development at the Association of International Certified Professional Accountants. To comment on this article or to suggest an idea for another article, contact Oliver Rowe at Oliver.Rowe@aicpa-cima.com.
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Articles
“Approaches to Artificial Intelligence: Leaders Look for Answers”, FM magazine,
6 November 2024
“What Gen AI Means for Executive Decision-Making”, FM magazine, 9 October 2024
“What CFOs Need to Know About Gen AI Risk”, FM magazine, 19 August 2024