AI is transforming the FP&A function by providing better data, faster insights and smart analysis at scale. But to reap the benefits, organisations must understand the current state of AI in finance.
Bruno J. Navarro
Senior Editorial Strategist, Finance
Workday
Financial planning and analysis (FP&A) teams no longer budget and forecast the way they once did – and that’s a good thing. Artificial intelligence (AI) has transformed financial planning from a backward-looking, static process into a dynamic, data-driven strategy powered by cutting-edge technology.
In 2025, AI is empowering FP&A teams to make real-time, informed decisions so their organisations stay agile and ready to adapt to shifting market conditions.
By integrating AI into enterprise FP&A, CFOs and finance leaders no longer have to rely on gut instinct. Instead, they gain access to up-to-date insights that enable smarter, data-backed decision-making at every level. As AI in FP&A continues to evolve, staying ahead of emerging trends and innovations is crucial for finance teams to maintain a competitive edge.
AI is no longer a future concept in FP&A – it’s actively reshaping how finance teams operate today. According to the recent Workday Global CFO AI Indicator Report, financial planning is among the top transformational areas for AI across the board.
Finance operations have almost universally benefited from the adoption of AI. Finance teams particularly highlighted forecasts and budget decisions (34%), strategic planning support (32%) and scenario planning tools (32%) as areas where they were seeing immediate value.
As AI becomes a cornerstone of modern FP&A, key benefits and challenges are emerging that are important to know and navigate intentionally. While AI-powered tools enhance areas such as forecasting and automation, others, such as security and business partnering, require a careful approach.
For business leaders, understanding how the implementation of AI will impact FP&A teams is critical. Here are the key benefits and challenges of adopting AI into finance workflows.
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Finance teams that embrace AI undoubtedly gain a competitive edge by gaining access to better data, higher efficiency and stronger collaboration. Key advantages of AI in FP&A include:
Despite its clear advantages, AI adoption in FP&A comes with hurdles that organisations must proactively handle to fully leverage AI’s potential. These include:
AI is reshaping FP&A, but its impact depends on how well organisations adapt. The right approach doesn’t just embrace innovation – it ensures organisations implement AI responsibly, securely and in a way that truly enhances financial decision-making.
According to the Workday Global CFO AI Indicator Report, financial planning is among the top transformational business areas for AI and ML.
The next evolution of AI in FP&A isn’t just about efficiency – it’s about smarter, more autonomous systems that enhance forecasting, decision-making and financial strategy. As AI capabilities grow, finance teams will need to navigate new challenges, from ensuring AI insights are transparent to integrating real-time, personalised insights. The following financial planning trends will define what’s next.
AI forecasting is becoming increasingly autonomous. AI forecasting systems no longer require regular manual recalibration because they learn from new data, adjust assumptions dynamically and integrate external economic indicators to produce near-instantaneous financial forecasts.
This is creating profound change in the way finance teams forecast. Rather than doing frequent (and time-consuming) analyses, teams can simply oversee self-adjusting models that detect important changes automatically – often before they even fully materialise in the market. In turn, organisations can proactively adjust strategies to capitalise on opportunities, improve risk management and reduce human errors.
As autonomous models continue to rise in popularity, FP&A teams must be aware of the potential “black box” nature of AI models that don’t have clear, explainable decision-making processes and thus pose risks around compliance and security.
“Traditional legal and regulatory frameworks, built around human decision-making, face challenges when confronted with AI's Black Box nature,” wrote Joshua Dupuy, international law expert, in a recent analysis for Reuters.
“This dilemma, where the rationale behind AI decisions is not transparent or easily understandable, complicates the assignment of liability and responsibility, especially when such decisions lead to adverse outcomes or systemic risks.”
Explainable AI (XAI) frameworks combat this challenge, ensuring AI-generated predictions are transparent and auditable. XAI provides visibility into how AI models arrive at their conclusions so teams can understand, validate and trust the reasoning behind AI insights.
Organisations that fail to demonstrate how their AI models generate insights risk regulatory fines, legal liability or reputational damage. If AI models develop unintended biases – such as favouring short-term profitability over long-term sustainability – companies could also make financial decisions that undermine shareholder trust.
To mitigate these risks, finance leaders must prioritise XAI to build AI models that provide clear, auditable justifications for their recommendations. As they adopt AI in FP&A, organisations should also be investing in AI ethics frameworks to keep their FP&A AI systems aligned with their corporate values, legal requirements and stakeholder expectations.
With a holistic view of the enterprise, CFOs will be better-informed not only about what makes sense from a financial perspective, but about what aligns most with what other top company leaders prioritise in their own strategic visions.
The continuous alignment between fiscal responsibility and strategic goal-planning offered by integrated FP&A has potential to unlock new levels of leadership engagement and stakeholder approval.
“This dilemma, where the rationale behind AI decisions is not transparent or easily understandable, complicates the assignment of liability and responsibility.”
FP&A has historically been quantitative, relying on metrics such as revenue, cost and economic indicators to guide financial planning. But today, AI-powered natural language processing (NLP) adds a new dimension – the ability to analyse market sentiment, consumer psychology and investor reactions to anticipate financial trends before they materialise.
By integrating alternative data sources such as social media, investor call transcripts and real-time consumer purchasing behaviour, NLP helps FP&A teams detect early signals of demand shifts, stock market trends and changing investor sentiment.
By incorporating non-quantitative insights, FP&A teams can better anticipate market shifts that traditional metrics might miss. Sentiment and behavioural patterns add depth to financial analysis, providing a more complete view of risks and opportunities. As AI improves, these deeper insights will be a critical tool for more adaptive, comprehensive financial planning.
AI is enabling increasingly more personalised, data-driven decision-making. Instead of relying on broad, top-down financial strategies, FP&A teams can now tailor insights, forecasts and recommendations to specific business units, functions and even individual decision-makers.
For example, FP&A AI tools can assess regional demand trends for sales teams, adjust cash flow projections based on supply chain volatility for operations or generate hyper-specific budget recommendations for project managers.
This level of context-aware financial planning gives every decision-maker access to insights that are relevant to their specific challenges and goals, enhancing financial transparency and trust. When stakeholders receive financial guidance that directly aligns with their work, they are more likely to engage with FP&A teams and act on their recommendations.
Partnering with an AI expert is the surest way for FP&A teams to avoid risk and move through the AI adoption process with confidence.
As AI technology continues to reshape FP&A, finance leaders must move beyond simply understanding these trends and focus on strategic implementation. Successfully integrating AI in FP&A means more than adopting new solutions – it requires a shift in mindset, processes and culture.
To get started, finance leaders and FP&A teams should take the following steps:
Driving FP&A transformation isn’t without challenges. Success for CFOs will depend on addressing skill gaps, improving data governance and fostering alignment across the organisation. Here are three challenges that CFOs must be mindful of in 2025 – and beyond.
Recognising that FP&A employees need upskilling is only the first step – training them effectively is where things can be challenging for CFOs without the right execution. Change management plays a key role in making it happen.
Workday research has identified a critical trust gap that exists right now between AI and the people expected to use it – only 52% of employees believe their companies will adopt AI in a responsible and trustworthy way.
The takeaway for CFOs: employees must be empowered and educated about new AI tools, rather than forced into adopting them. This means communicating the why behind new FP&A approaches, highlighting real value-adds and emphasising how AI will enhance rather than replace roles in the finance department.
Only 52% of employees currently believe their organisations will adopt AI in a trustworthy and responsible way.
The use of AI in FP&A hinges on accurate, structured and accessible data – but fragmented systems and internal inconsistencies remain common obstacles. CFOs must work to establish robust data governance frameworks to organise, clean and harmonise data.
Cooperation between CFOs and CIOs is crucial to maintaining reliable data, along with sound software tools that can manage data and automate FP&A processes at scale. Companies that fail on these counts risk creating a cycle of poor-quality data creating inaccurate results, which in turn leads to further poor-quality data.
Integrated FP&A requires cross-departmental collaboration, but as any leader knows, buy-in doesn’t happen overnight. Just as focused change management is needed to drive successful upskilling, CFOs must be ready to demonstrate the value-add for other members of the C-suite and their teams to take a more active, engaged role in financial planning.
Software tools help here again by making data accessible and collaboration easy, as well as delivering quick time-to-value – when leaders in other parts of the organisation can see data translated into results for their part of the business, they’ll be quicker to get on board.
Putting members of the FP&A team on project teams across the organisation can also be effective. By injecting an FP&A perspective into non-finance initiatives, stakeholders in other parts of the company can see its value first-hand.
In 2025, finance leaders have an exciting opportunity to lead the transformation of FP&A into a central part of enterprise strategy. CFOs will continue to deliver on financial goals, but the future of FP&A will see greater integration, cross-organisation accountability and strategic impact.
Meeting this potential comes down to a few key steps:
With these strategies top-of-mind, CFOs can position their teams to deliver sustained value, evolve with emerging FP&A trends and shape the strategic direction of their organisations.
Pressure for finance leaders to invest in AI is at an all-time high – but what are the benefits? Learn more about the potential impact of AI implementation in this Workday report, from 2,355 senior business executives.
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