Data Quality – If there is one overarching lesson we have learned in finance over the last decade, it is that the basics need to be right first. Brilliant forecasting models, scenario analysis and – now – (Generative) Artificial Intelligence capabilities are futile if the data quality is lacking. In the new world, ‘garbage in’ becomes ‘garbage multiplied’. Moreover, finance functions have invested millions in establishing transparent data lineage and meeting the control and reconciliation requirements imposed by themselves, the accountant, and regulators. To improve data quality within the large datasets from multiple sources across the organization, AI is increasingly used to enhance data classification and quality. Effective data management is essential to enable GenAI; and leveraging GenAI to improve data management can enhance accuracy and speed within the finance function. Part of this data management challenge is also the accessibility of data that GenAI brings to the surface. Enabling Gen AI tooling like Microsoft CoPilot, might confront companies how in-compliant they are in terms of access to data that they should not have. So, having the right data quality also involves cleaning up databases and being compliant in terms of GDPR and many of the other regulations, like the Data Act and recently approved EU AI Act.
AI Models – It is understandable that a technology known to be less transparent may need time to be adopted within the finance function. Because besides data quality, the AI models' quality is still a discussion too. How can we be sure that the model is giving the right information? And that the model is not hallucinating? While the most often applied LLMs (e.g., GPT-4, Gemini and Llama 2) are generally the best models, they might lack specific specialized functions. We therefore see an increase in the application of more dedicated open-source models that are pre-trained (e.g., BloombergGPT) for specific niche functions, like the finance function. And of course, the obligatory (according to the to-be-applied EU AI Act) Human-in-the-loop is something that should be considered in evaluating the enablement of AI solutions within business processes.
People – Most individuals experience both natural concern and curiosity regarding GenAI. The primary concern is that many roles and activities will be taken over by GenAI. Satya Nadella, the CEO of Microsoft, has stated: “AI will make us more human, not less. AI cannot replace human qualities like creativity, empathy, and judgement. Instead, AI will amplify our human capabilities and help cultivate our creative spirit.” Finance function leaders must reiterate these messages. GenAI can be a key driver in freeing up more capacity for business control and business partnering activities. Meanwhile, essential accounting and reporting processes will still require human interaction for judgement and oversight.
Change management – Related to the challenge and role of people, finance functions often struggle with adopting new technology. The finance function is known for its strong adherence to principles like ‘Straight-Through Processing’ and ‘First Time Right’, which can make it difficult to embrace innovations such as Robotic Process Automation (RPA), Business Process Management (BPM) and Process Mining, especially given their complex IT architectures and processes. Some experienced finance professionals may require additional convincing that GenAI will bring real benefits and will not merely distract from ‘simply getting the job done’.