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Getting started with AI (and generative AI) in finance

Keith Causey Profile picture for user Keith Causey July 9, 2024
Before implementing AI in finance, there are some essential preparations to make that go beyond technology. Keith Causey of Oracle talks tactics.


The latest evolution of AI in finance is more than just an upgrade. It's a massive operational shift that enables CFOs to accelerate key financial processes and enhance decision-making.

While all the hype is around generative AI, it’s certainly not a starting point for reimagining financial processes. There are numerous preliminary steps that companies need to take to drive the next quantum leap in productivity.

Adopt a data-first mindset 

Achieving a return on AI investments hinges heavily on its data underpinnings. Any finance team looking to adopt AI must first ensure the quality of their data, which is critical for automation, pattern recognition, and training AI for advanced capabilities.

A focused data cleansing may be necessary to power AI. Data will be your backbone and your finance platform will be your foundation for transformation and innovation, so organizations should also look to consolidate as many systems as possible and manage data on a common platform. With an integrated suite of applications that uses a shared data model, finance teams can apply more automation to foundational processes and enable more complex AI use cases.

Think strategically versus tactically

While using these technologies to increase productivity is a good first step, finance leaders should also look past the initial tactical gains and strive to enhance value derived from the team’s higher skill sets.

Finance leaders should define outcomes strategically for higher value and employee satisfaction and focus on data-driven outcomes that provide meaningful insights for real-time decision-making.

They also need to think about assigning AI resources for proactive vs. reactive strategic actions. For example, rather than implementing AI to cut down on repetitive processes and simply make inefficient tasks more efficient, finance teams should also look to AI to help anticipate future events and take measures to shape outcomes positively. 

This entails future scenarios where AI systems can take care of tasks behind the scenes and flag items for human review, such as forecasting cash flows by using historical data and flagging expected deviations for review. This type of improved user experience enhances collaboration and allows employees to fundamentally change their relationship with business applications. Ultimately, AI should help to reduce organizational friction and increase business opportunities.

Thoughtfully consider workforce implications

There are genuine concerns from employees about how AI and generative AI can potentially impact their roles, which finance leaders must acknowledge. It’s crucial to reinforce that technologies are here to augment and support roles, by automating repetitive tasks to allow for increased productivity and innovation.

CFOs should tackle the fear by empowering employees through investments in training to build their capabilities to take advantage of AI and generative AI. Examples of these include education programs for employees to learn more about data and peer groups where they can also learn from one another. These investments help ensure a data-driven business focus, prepare people for continuous change, and ease fears through open and honest discussion. Without these types of programs in place, employee resistance could slow down widespread adoption.

Treat finance transformation as a journey

An ERP platform strategy is the starting point for the journey, focusing on people, process, data, data-driven outcomes, and cloud-based technology. This platform should allow flexibility regarding when and what you implement and provide agility for growth, acquisitions, and changes in business model.

Don’t expect 'moonshot' AI capabilities on day one. AI is complex and needs to be implemented in careful increments. Starting with solid data and small experiences, companies can begin building wins and proving value, which will help them achieve greater capabilities and strategic gains further down the road.

Unleashing new capabilities

New use cases are emerging at a staggering pace, powered by the synergy of AI, generative AI, and expansive data models, all designed to take automation and insight to new levels. In a financial reporting scenario, generative AI will help prepare first drafts of 10Qs and 10Ks, including footnotes and MD&As (Management Discussion & Analysis).The close process will undergo meticulous scrutiny with every entry analyzed, with anomalies detected and corrective actions provided.

AI will simplify end-to-end business flows and processes between transacting organizations, from receivables risk and collection management to fraud detection. Other areas ripe for automation and simplification include contract preparation and project proposals, treasury activities such as loan applications and opening bank accounts, purchases, settlements, and more.

The finance leaders who embrace AI and generative AI now can align their initiatives to broader business operations and strategies and will reap greater benefits than those who sit on the sidelines. With a clear vision, adept change management and a solid handle on workforce implications, finance leaders can steer their organizations toward a future where finance teams pave the way for productivity and innovation.

Bottom line: AI is not a new technology to adopt – it’s an opportunity for finance leaders to reshape the way finance functions.

Disclosure: to learn more about Oracle’s approach to AI in Finance, visit

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