While usage of generative artificial intelligence (GenAI), a branch of AI technology that can produce various types of content in response to a user’s prompt or request, is not as ubiquitous in the workplace as it is in headlines, 23 percent of organizations were already using GenAI or had active plans to do so, according to Thomson Reuters Institute’s 2024 Generative AI in Professional Services report.
As organizations evolve, and make the move to integrate AI or GenAI technology into workflows, chief financial officers, as the strategic partners that chief executive officers rely on, are expected to lead the charge in ensuring successful navigation through this technological revolution.
However, the rise of publicly available GenAI models, like ChatGPT, has placed finance leaders at a crossroads. While some perceive it as a threat that could automate away human workers, others recognize its potential to enhance the finance function.
Stephen Lo, CFO at Prenetics and a member of the Hong Kong Institute of CPAs, captures this sentiment succinctly: “This is the most empowering moment for me personally as a CFO. GenAI provides a framework that makes me a lot more effective as an executive.”
This perspective highlights the strategic opportunity for finance leaders to champion AI adoption across their organizations. The benefits of embracing GenAI are substantial, particularly in boosting productivity. Finance teams can generate business reports, emails, and complex Excel models far more efficiently. “We’re talking about 3x, 4x efficiency gains,” says Lo.
Jason Wong, Head of Finance at YUU Rewards, and an Institute member, emphasizes two key benefits: “The first one is predictive modelling using advanced scenario and impact analysis. And number two, automating mundane transactional processes to improve efficiency and effectiveness of financial reporting to enable more time on narrative and interpretation.”
Beyond individual productivity, GenAI can enhance collaboration and communication throughout the organization. Lo explains that GenAI enhances collaboration by creating a common language, improving efficiency in meetings, and facilitating better knowledge sharing. It allows employees to conduct research before discussions, leading to more informed conversations. Additionally, it helps break down organizational siloes, making collaboration easier for everyone, not just finance teams. “Collaboration will become much easier because it’s not just for the CFO, but for everyone I work with,” he says.
“Leaders in organizations need to embrace the technology and basically just admit that it needs to be in every single part of the operation, both in-house and in customer-facing operations,” says James Liu, Finance Director of IKEA at DFI Retail Group and an Institute member.
“This is the most empowering moment for me personally as a CFO. GenAI provides a framework that makes me a lot more effective as an executive.”
A recent benchmarking study published by PwC, Becoming the Catalyst: How Finance Functions are Driving Shareholder Value, reveals the vast and impactful applications of GenAI in finance. The study highlights key areas where AI can enhance data processing and generate actionable insights. For instance, GenAI allows finance functions to weave disparate data into cohesive narratives. “By integrating data management tools such as enterprise resource planning, robotic process automation, and extract, load, transform, AI can automatically transform and parse data, making it easier to extract insightful information in a timely manner and construct business insights more frequently,” says Wilson Chow, PwC Global TMT Leader and China AI Leader and an Institute member.
Moreover, the power of predictive analytics, driven by AI, enables finance teams to leverage complex datasets for more accurate forecasting. This capability not only bolsters revenue generation but also strengthens risk mitigation efforts. Chow notes that by reducing manual processing, professionals can focus on strategic initiatives, and that of the respondents in the PwC study, over 75 percent favoured these applications, showing a strong appetite for AI integration.
Lo emphasizes the significance of AI technology for CFOs: “This is a golden time for an effective CFO. If we can help the organization become more productive, then you will be hugely beneficial for everyone.”
This transformation means that professionals will have greater opportunities to focus on strategic activities that drive value for their organizations. Chow notes that as companies strive for efficiency and innovation, finance professionals will increasingly serve as advisors, interpreting data and making strategic recommendations. Human accountants will also play a critical role in ensuring the ethical use of AI, addressing biases, and maintaining governance over AI-driven processes.
“With AI handling routine tasks such as data entry, reconciliation, and basic report generation, accountants and finance professionals will have more time to focus on strategic activities like financial planning, analysis, and projections. This shift will enhance their role as strategic partners in the organization,” Chow says.
Finance leaders must also navigate the risks and challenges associated with AI integration. According to Lo, auditability is a key risk in adopting GenAI for finance and accounting functions. “GenAI is a black box. There’s no audit trail, and no one knows what’s happening within it. And that is completely contradictory to the fundamentals of a good process in the accounting sense,” he explains.
GenAI, being a “black box” without transparency into its inner workings, poses a challenge to the need for verifiable records. Because of this, System and Organization Controls (SOC) reporting can be crucial for AI applications to drive trust and transparency. A SOC 2 Type 2 report can reassure external auditors, as it evaluates the operational effectiveness of an AI service provider’s controls related to data security, privacy, and system processing over a specified period.
For Liu, the greatest risk lies in accuracy. “There still has to be a certain level of scepticism, which is what finance professionals are trained to do; we don’t take things at face value.” This scepticism can complicate efforts to gain employee buy-in. Chow agrees, noting that transitioning to AI-driven processes requires significant organizational change, and it is up to CFOs to lead these efforts by addressing resistance from staff and fostering a culture that embraces innovation.
To address this, ultimately there needs to be a mindset shift. According to Lo and Liu, it’s about showcasing the benefits of AI and getting the organization to buy into it.
Lo stresses the importance of change management, particularly focusing on the human aspect, while Liu suggests addressing employee concerns about job displacement.
“One of the main concerns employees have is that AI will replace their jobs. To address this, we provide that safety cover to our workforce by saying: Look, we are not looking to reduce headcount,” Liu says.
Finance leaders must therefore communicate authentically. “You have to make them see what’s in it for them, why they need to do it. Is it going to take away my job, or is it actually helping me to become more effective?” Lo says. “If they don’t have the buy-in, it will be difficult for any digital transformation project.”
As such, a clear vision of how AI can empower employees, rather than replace them is also important. Reassuring employees that the goal is to enhance their capabilities and allow them to work more efficiently will be key.
Liu shared how IKEA incentivizes innovation by setting annual objectives for team members to automate at least one process using AI. This not only drives adoption but fosters a culture of continuous improvement. “The natural competitiveness of our accountants means that they will come up with something great,” he says.
Encouraging openness is equally important, according to Wong. Involving the finance workforce in AI discussions and decisions can help employees view the technology as an asset or partner rather than a threat. “Emphasize AI’s role in enhancing productivity. Showcase how AI frees up time for more meaningful work and promote a continuous improvement and learning environment,” Wong advises.
Leaders should also celebrate success stories and empower employees to take ownership of the AI transformation. “Lead by example,” Liu argues. “In terms of training or awareness of what is available, given the speed of AI development, we need to spend time to properly expand it and use it, and think about how to leverage it ourselves.”
When leadership demonstrates a commitment to learning and using AI, it sends a powerful message throughout the organization.
Wong adds that finance leaders should be proactive in identifying specific use cases that align with their organization’s goals and deliver quick wins. “Focus on selective, discrete use cases that have the potential to deliver the most meaningful impact,” he says. “Look for tangible, quick wins that will help build momentum and confidence for the finance function and the rest of the organization to further invest in GenAI.”
As an example, Liu shares how IKEA has harnessed AI in its finance function. “We now produce safety videos using AI, with an avatar of our own face as the instructor delivering the training material; this is a huge cost saving.” The company has also automated the scanning and data entry process for vendor invoices. Liu notes, “Now it’s just a matter of putting that chunk of invoices into a scanner and performing checks at an exception basis.”
Managing the risks associated with this technology is equally important. “AI systems thrive on high-quality data, so CFOs must implement robust data management practices to ensure accuracy and security,” says Jacqueline Chan, Managing Director and CFO of DBS Bank Hong Kong, and an Institute member.
Wong highlights the necessity of collaboration among teams to ensure ethical and secure implementation. “It requires a collaborative approach between finance, IT, legal, and compliance, depending on the industry,” he says. Chan agrees, stating that leaders must recognize the inherent risks associated with AI and establish clear governance frameworks to guide the deployment of these transformative technologies.
At DBS, Chan explains the bank’s approach on data management: “We adopt a ‘PURE’ framework to ensure responsible data use. P stands for purposeful, U for unsurprising, meaning that data use should be expected by stakeholders. R is for respectful, meaning data use should consider social norms and ethics, and E is for explainable.” This comprehensive approach to data management lays the foundation for the responsible use of AI, ensuring that the bank’s principles are upheld.
For the governance framework, Chan highlights that a cross-functional task force is formed to identify risks and build guardrails associated with AI adoption. This multi-layered assessment process includes self-assessments by the initiating units and oversight from independent committees to ensure that AI applications meet high standards of safety and reliability. For now, the bank’s GenAI applications are restricted to internal use, prioritizing caution over customer-facing deployment.
By systematically assessing risks, Chan explains, “Each AI and GenAI use case can be evaluated to determine if inherent risks can be adequately mitigated and whether any residual risks are at an acceptable level prior to deployment.”
As finance functions evolve to meet digital age demands, measuring the success of AI initiatives becomes critical. Chan emphasizes the importance of establishing control groups for this purpose. “We establish a control group, and then the outcomes are compared with the experimental group that uses AI. The differences captured can be attributed to AI.”
“Look for tangible, quick wins that will help build momentum and confidence for the finance function and the rest of the organization to further invest in GenAI.”
This scientific approach allows organizations to quantify the tangible benefits of AI, such as time savings, improved accuracy, and enhanced decision-making. Chan cautions against focusing solely on cost savings. “Revenue improvement is also a very important incentive for us, because enhanced decision-making will help generate more revenue, improve client engagement, and provide deeper insights into market trends.” Only by taking a holistic view can finance leaders build a compelling case for continued investment in AI technologies.
As the role of the CFO evolves, experts agree that finance leaders must become forward-thinking, data-driven strategic partners who can manage the risks and opportunities presented by AI and GenAI. The path to successful AI adoption requires a delicate balance of vision, governance, and employee engagement.
By leveraging these cutting-edge technologies to drive sustainable growth and enhance risk management, Chan believes that the finance function will be “empowered to scale new heights, drive greater value, and foster efficient data intelligence.”
PwC’s Chow concurs, adding that “many people express worries that the adoption of AI might eliminate human jobs. In reality, it will lead to an evolving role for accountants and finance experts, allowing them to step up and drive more value within their organizations.”
According to a 2024 survey of CFOs by McKinsey & Company, one in five CFOs report the use of GenAI tools, and of them, nearly half are still in the pilot and experimentation phase. In areas where finance functions have already adopted GenAI, CFOs most often cite improved employee productivity as a benefit (71 percent). 54 percent cite better use of data in business decisions and 48 percent cite insight generation that allows employees to focus on higher-order tasks.
