Four scenarios for the future of AI and finance
Artificial intelligence will affect finance in four very different ways. Here’s what leaders should prepare for.
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One rule holds true for CFOs trying to navigate the changes that artificial intelligence will bring to their departments and the rest of their businesses: Preparedness is key.
Research from SAP Insights has examined several possible outcomes for the future of AI in finance, each with distinct implications for companies, employees, and the broader economic landscape. Businesses may experience more than one of these scenarios as they create new processes and workflows that take advantage of AI’s capabilities. How they play out depends on a combination of company culture and comfort with quickly changing technologies.
The transformation matrix shown below evaluates two key dimensions: the social and cultural readiness of business functions to implement AI, and the technological readiness required of them to do so effectively. These factors determine whether businesses will thrive, transform, or struggle in an AI-driven future.
The following are four scenarios that emerge from this framework.
1. Transformation of work
This scenario assumes that organizations’ social and technological readiness are high. Companies would have invested heavily in retraining employees, ensuring that AI is used to augment rather than replace human capabilities. Flexible, empowered workforces and strong infrastructures would allow a transformative shift in work processes. In some cases, finance departments would expand beyond their traditional roles, becoming strategic hubs where data and AI-driven insights drive innovation. AI would be an indispensable collaborator here, enabling employees to take on more value-added tasks while reducing the burden of routine processes. In this scenario, companies would have clear, ethical guidelines for AI usage that apply across the entire business
Implication: Businesses that prioritize retraining and ethical AI usage—for example, screening for hiring biases—would be able to create a work environment where AI and humans collaborate seamlessly. The result would be a more engaged workforce, more strategic finance departments, and a stronger competitive edge. and a stronger competitive edge.
2. Strategic work with AI support
Where social readiness is high but technological infrastructure is lacking, businesses could still implement AI. But weak infrastructure would pose hurdles, making AI implementation complex and costly. In this scenario, AI would support human workers by automating low-value tasks; however, teams implementing independent solutions might hinder seamless navigation and inadvertently creating fragmented implementations. Employees would remain the ultimate decision-makers and would be free to expand their operational horizons into higher-value work.
Implication: In addition, there could be some risk here if employees use AI before appropriate safeguarding policies such as data protection are in place. Companies in this scenario must navigate the complexity of implementing AI in an evolving regulatory environment, and so overall it would be less efficient than it could be. By coming together with other departments to focus on building internal AI literacy and ethical governance and by implementing specific guidelines over misuse and compliance, they could still thrive by combining AI’s efficiency with human decision-making skills.
3. Replacement
In cases where technological readiness is high but social and cultural acceptance lags, we see a very different picture. Companies could forge ahead with AI adoption for efficiency and cost reduction but without retraining or reorganizing the workforce, they would risk creating a disengaged, anxious employee base. High-tech adoption in this scenario may lead to significant turnover, and employees might resist AI-driven changes due to their fears over job security.
Implication: While companies might see short-term gains in efficiency, the long-term risks of neglecting the human element could lead to financial and operational instability. Without employee support and proper planning, these businesses could undermine their own success.
4. Risk of irrelevance
In the most severe case, both social and technological readiness are low. A lack of trust in AI, compounded by weak infrastructure and governance, could lead to stagnation. Companies in this scenario would be unable or unwilling to invest in AI, fearing its effect on jobs, unclear on its benefits, or struggling with its complexity. The result? Such companies would risk becoming obsolete as competitors who implement AI pull ahead. This scenario illustrates the dangers of not preparing for the inevitable AI-driven future.
Implication: Companies that fail to invest in both AI and their workforce might find themselves slipping out of the picture—and out of business. To avoid this fate, businesses would need to be proactive about AI adoption, workforce retraining, and building infrastructure that supports long-term success.
Mapping process transformations to business benefits
Across all scenarios, one thing is clear: AI will drive profound process transformations in finance. Businesses that implement AI strategically could expect enhanced decision-making, improved operational efficiency, and stronger user engagement, among other benefits. But those that fail to prepare, either by ignoring the need for technological infrastructure or neglecting their workforce, would risk falling behind.
In the Transformation of Work scenario, AI could free up employees from routine tasks and enable them to focus on more strategic, creative work. Finance departments in this future would no longer be just number-crunchers—they would become innovation hubs, leveraging data and AI insights to make better, faster decisions.
By contrast, the Replacement scenario highlights a cautionary tale: If businesses focus solely on AI for efficiency without considering the effect on their workforce, they might experience resistance from employees, leading to disengagement and long-term instability. The future of finance at work isn’t about machines taking over—it’s about humans and AI working in tandem.
Next steps for finance leaders
As AI continues to evolve, the decisions businesses make today will determine their success in an AI-driven future. Companies that align AI usage with business strategy, invest in both AI technologies and their people, and build strong AI governance will be best positioned to reap the benefits of these process transformations.
Our transformation matrix offers a strategy for navigating the complex landscape of AI adoption. Developing strong, ethical AI frameworks; retraining employees for more strategic roles, and building the technological infrastructure needed to support AI will help businesses be proactive in shaping their AI strategies.
What does your business look like through this lens?
More on AI’s effects on finance
Learn more about how AI is transforming financial work and where human skills remain essential; five major trends for finance, influenced by AI adoption; and dive into our thorough FAQ for leaders on how AI affects finance jobs, how to get started, and how to advise the rest of the business on AI investments, strategy, and governance.
About this research:
SAP Insights commissioned TRENDONE GmbH to study trends in the field of artificial intelligence that would be expected to affect the business processes within the finance department of a typical midsized-to-large company, and was summarized in this article. This article offers an additional analysis of the technological adoption curve and its effect on people and business, which was developed in the form of a future business transformation scenario model.