Artificial intelligence (AI) has been in use for several years in chatbots, virtual assistants, predictive analytics, and many other applications. But it’s generative AI specifically that has seen exponential growth within the past year or so. Generative AI refers to systems or models that can generate new content, such as images, text, or other types of data, based on the patterns and information they have learned from a training data set.
While the impact and implications of generative AI are still being considered, the technology is emerging and evolving. Finance professionals need to understand both the business potential and the finance and accounting applications of generative AI. The technology will cause significant changes to how things are done in an enterprise, which has implications for a business’s organisational design. Finance must be able to see beyond its own functional horizons and champion the adoption of generative AI.
There are four areas of potential for finance leaders and teams to actively consider and understand.
Consider gen AI’s business potential
Generative AI deployment is not about automating for efficiency but rather about new possibilities for how customers are served and the products they are offered. Generative AI algorithms enable new service offerings for existing and new customers.
Generative AI has potential use cases in new product development and product design and prototyping. Generative AI can speed up the research and development process in key fields, such as drug discovery. It can also simulate product characteristics, flex the design elements (such as colour, shape, and finish) of a product to improve it, and even generate 3D models of new products. Finance needs to recognise these areas of business potential and consider use cases that generate value.
Generative AI also has potential use cases that create process and cost efficiencies, with areas of deployment across all functions. Large language models (LLMs), for example, can assist in driving a better customer experience, building a robust supply chain, improving operations, and supporting the human capital of the enterprise.
Understand gen AI’s finance and accounting uses
Generative AI has the potential to transform core accounting and finance operations. Applying generative AI to processes such as the reviews of the general ledger and outstanding reconciliation items means reviews become more rigorous and effective.
Leveraging generative AI’s capabilities in the data-to-decision process will enable an enterprise to see sooner and act faster. Because it can write commentaries and identify trends so quickly, generative AI can enable finance to spend less time on the analytics, preparation, and consolidation of reports, budgets, and forecasts. This will allow finance to significantly improve business partnering by spending more time on value-adding activity, decision-making, and execution.
Using trained models on customer trends and data will enable identification of receivables that need prioritisation and improve debt and days outstanding. Generative AI will significantly improve risk identification and enhance enterprise risk management through identification of anomalies and improved risk assessment timelines.
Be aware of organisational design and training implications
The finance function needs to enable the organisation to become more agile and improve its ability to adapt and transform as generative AI is deployed.
New ways of working and processes that are affected will require organisational and structural changes. The deployment of generative AI across business functions will increase the rate of change, impact culture, and require strong change management capability.
There will be an increased need for training and development plans within the new structures and for the new processes. The finance team should prepare its own training and development plans and support the investment required in talent and training across the business to fully realise generative AI’s potential.
Specific training around data literacy, AI ethics, and human collaboration with AI systems is essential. The ability to adapt to evolving roles and responsibilities becomes a valuable skill in the context of generative AI deployment. Finance should partner with HR to create capabilities to cope with the level and pace of change.
In addition, the enterprise should emphasise the development of soft skills such as critical thinking, creativity, and emotional intelligence. These skills become more crucial as routine tasks are automated.
Champion the technology and overcome adoption obstacles
Finance needs to be closely involved in developing the business case for generative AI, as well as supporting business functions in modelling the financial benefits and costs of deploying it.
Also, finance should actively support the change management required to enable the investment and the implementation plans, including stakeholder management.
The finance team should be aware of adoption challenges and obstacles and help enable the deployment of generative AI. Finance must also address data governance and be involved in ensuring data accuracy, which is crucial to training the LLMs correctly and ensuring accurate outputs. It’s also important that finance understands generative AI’s ethical implications and data privacy compliance requirements.
— Ash Noah, CPA, FCMA, CGMA, is vice-president and managing director–Learning, Education & Development at AICPA & CIMA, together as 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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AICPA & CIMA resources
Articles
“Generative AI — What’s the Potential?“, FM magazine, 12 February 2024
“Employees ‘Unsure’ of Company Ethics Guidelines for AI“, FM magazine, 2 January 2024
“6 Ways to Future-proof Your Career in an AI World“, FM magazine, 15 December 2023
