Responsible use of AI technology is essential to its widespread adoption and integration into daily work where it can be best leveraged.
Trust is a cornerstone in all areas of finance, so implementing AI requires a sensitivity to how it may affect client trust and relationships. It is up to us as early adopters and finance professionals to set out the ways of working with AI and maintain integrity, honesty and transparency.
Boardroom leaders for AI
CEOs and CFOs need to take steps in their organisations to manage the associated risks of AI, while setting out a solid example for ways of working.
As AI plays a greater role in the accounting and financial reporting of businesses, CFOs and financial controllers will have to be confident about the adequacy of oversight and controls of AI systems.
Implementing AI is gathering pace, but it will still take time for the technology to be fully embedded into the working life of finance professionals. That means there will need to be a cost (both in terms of money and time) in getting people skilled and the right systems implemented.
CFOs in particular will need to take a long term perspective here, thinking about future advancements, challenges, and the needs of the organisation when it comes to AI technology. Considerations such as solution scalability, employee morale, performance management, and growth opportunities versus cost reduction should all be kept in mind when looking at AI technologies.
With the pace of development, it is important that any tools you implement can be adapted for future advancements and challenges.
AI trust with ACCA
In the first in a series of insights, AI monitor: Trust, we at ACCA (the Association of Chartered Certified Accountants) are urging finance professionals to ensure that AI governance and AI risk management is in place, beginning with:
Investing in AI literacy and skills development: finance professionals must invest in education and training to critically evaluate AI outputs, communicate clearly with key stakeholders, and make informed decisions.
Collaborating via cross-functional teams: finance professionals should actively engage with IT, data science, legal and risk management teams.
Developing an AI governance framework: beginning with critical uses, finance professionals should take steps within their organisation to establish clear policies, oversight and governance practices.
AI opportunities
AI presents many opportunities to businesses such as providing more insights from a wider array of information sources, driving greater efficiency and better customer experiences. But it also poses a challenge to trust in accounting and finance reporting with new dynamics being introduced to the traditional trust mechanisms that underpin corporate accounting.
Introducing AI is both about trust in the systems and trust in the people that we work with, and how we bring those two elements together.
CEOs and CFOs need to focus on making the changes needed to harness the many potential opportunities but also retain trust. This includes upskilling to deal with the technology and introducing new knowledge into their organisations. They also need to focus on the governance, the oversight and culture required to allow different teams to work together effectively. It’s about bringing change management and governance together.
AI risk considerations
There are risks associated with AI, but it’s important to recognise these not as negatives but as warnings to bear in mind when using the technology. This includes:
AI impacting decision making without clear explanation or understanding of the rationale behind the forecast or recommendation;
An over-dependence on AI procedures in for example, auditing and assurance, and a decline in use of human intervention and judgement;
Concern over AI bias or error in fraud detection, risk assessment and compliance monitoring;
Over-reliance on AI-powered virtual assistants which give inaccurate or inappropriate responses.
In the AI era, the role of finance professionals is to focus on the outcomes driven by technology. Value lies in understanding how these outputs inform decisions and actions that drive business outcomes.
