At a glance
- Finance is entering a defining window where technology will shape value creation and risk management.
- AI-driven trends are reshaping finance, from talent to decision-making, with five key themes coming into focus.
- The next 12 to 24 months will be critical for modernizing finance capabilities.
- The priority now is how to operationalize and scale this shift effectively.
The finance function is entering a defining window where technology decisions will directly influence how it creates value and manages risk. The question for finance leaders right now isn't whether technology will transform finance, but how effectively they can operationalize and scale that shift.
Finance trends in focus
Many finance functions are already embedding AI into core processes. Those that delay risk falling behind or losing market share, yet those that move without a clear strategy risk creating fragmentation and control gaps. With the current pace of change in AI and technology, the next 12 to 24 months will be decisive as organizations determine how—and how quickly—to modernize finance capabilities.
Against this backdrop, we see five technology trends defining how that shift unfolds.
From automation to orchestration—the real value of AI in finance
AI is becoming foundational within finance, but adoption alone isn’t the differentiator; execution is.
Organizations that use AI as a tool to replicate existing processes often fail to realize expected gains. The finance functions that pull ahead will be those that identify practical applications and redesign workflows around them, rather than layering AI onto legacy processes.
Where does your organization stand?
By 2030, 70% of finance functions will use AI for real-time decision making, and agentic AI will make at least 15% of daily decisions autonomously, according to Gartner’s Future of Finance report.
Agentic capabilities allow organizations to build on the automation and autonomous finance layers many functions have already adopted, connecting previously siloed processes through an orchestration layer—an underlying system that coordinates and manages how different processes and tools work together as one. This enables coordinated workflows that can sequence and manage actions, rather than simply executing discrete tasks. It represents the next stage of maturity within the finance function.
The top agentic AI use cases within the finance function include:
- Predictive forecasting across revenue, cash flow, and scenario modelling.
- End-to-end accounts payable orchestration.
- Knowledge management and intelligent information retrieval.
- Continuous monitoring of financial controls, including AI-assisted risk modelling and anomaly/error detection.
Here’s a practical example: Consider invoice processing, which is already largely automated in many finance environments. In an agentic model, invoices are not only processed automatically, but can also be generated intelligently by drawing on contract data, time records, and project progress. This then extends into cash flow forecasting, working capital optimization, and scenario modelling across capital allocation strategies, all as part of a connected, coordinated workflow rather than a series of discrete steps.
Technology is reshaping the finance workforce and talent strategies
The finance talent model is under pressure from several directions: a shrinking talent pipeline, expanding capability requirements, and existing education models that haven’t fully adapted to the demands of AI-enabled finance.
70% of CFOs said they plan to use AI to alleviate team workloads and upskill staff, and 79% believe generative AI will help them reduce burnout in their teams, according to Gartner’s report, Current State of AI in Finance, 2025.
The traditional strengths of finance professionals—accounting expertise, reporting, and financial analysis—remain foundational, but on their own are no longer sufficient. Finance teams increasingly require professionals with:
- Data literacy and systems thinking.
- Proficiency with AI-enabled tooling.
- The ability to interpret and evaluate AI outputs critically.
This shift is as much about mindset as it is about skillset. Finance professionals must move beyond manual, task-oriented execution to using judgment to interpret and stress-test machine-generated outputs.
Takeaway: The role of finance is changing, and the team has to evolve with it. CFOs and finance leaders will need to upskill teams in digital and analytical capabilities, redefine roles, and/or leverage external expertise to bridge capability gaps.
Real-time, predictive analytics in finance is replacing static reporting
For many organizations, the finance function has historically been anchored in a familiar model: extracting data from core systems, validating it, and reporting on where the business has been. That model is giving way to one where finance is expected to forecast, model scenarios, and inform decisions in real time. This shift toward predictive, forward-looking finance requires CFOs to change their own view of value creation, not just their team's.
How quickly can your finance function turn data into decisions?
69% of finance leaders say their executives demand real-time financial information, according to a Sage and Foundry MarketPulse report.
Fragmented systems, legacy architectures, and layered tools on top of legacy environments create silos, inconsistent reporting, and manual workarounds.
As CFOs and finance leaders modernize their functions, the priority is building connected data ecosystems that give finance a unified, real-time view of the business.
Data governance is becoming a strategic prerequisite
Data quality remains a persistent challenge for the finance function. Many organizations are data rich, but insights poor—they sit on vast amounts of operational and financial data, yet fragmented systems limit advanced analytics and prevent teams from translating it into meaningful insight. Without integrated data, predictive modelling and real-time reporting will remain out of reach.
Data hygiene is a top priority for finance and accounting leaders, according to Sage and Foundry MarketPulse report.
- 60% say improving the accuracy of data for real-time decision making is a priority.
- 49% say improving data availability is a priority.
AI is also forcing a rethink of how finance approaches governance. Finance has traditionally operated with an expectation of precision: clean, structured, reconciled data. While audit-grade information remains critical for core financial records, AI introduces a more nuanced reality, where responses are probabilistic rather than deterministic.
This makes governance, not perfection, a critical prerequisite for scaling AI in finance. That means defining what ‘audit grade’ looks like in an AI-enabled environment and closing the gap between capability and control by:
- Establishing governance frameworks that ensure models are reliable.
- Defining acceptable thresholds of accuracy.
- Continuously reviewing and refining outputs.
Takeaway: Start with the fundamentals—address fragmented systems and data silos. If organizations don’t have a unified view of their information, it’s much harder to generate the insight they need, model effectively, or scale more advanced analytics and AI.
The transformation from legacy scorekeeper to strategic advisor
Taken together, these trends point to a broader transformation of the finance function and the value it delivers to the business. The function is moving beyond its traditional mandate of reporting historical performance toward becoming a strategic advisor. Technology is what makes that shift possible.
CFOs increasingly serve as orchestrators of insight, leveraging integrated data, predictive analytics, and AI-enabled tools to guide enterprise strategy and risk management.
This requires a fundamental mindset shift—from reporting what has happened to shaping what happens next. As organizations build more integrated, AI-enabled environments, finance will play a central role in translating data into strategic action.
Takeaway: Technology and tools should never drive strategy. Focus on business use cases and make practical technology decisions. What matters is whether it is being applied in ways that solve real business problems and support the role finance needs to play going forward.
Industry snapshot: The uneven road to finance modernization
The imperative to modernize is consistent across industries, but where organizations start and what stands in their way varies based on their existing systems, data foundations, and organizational priorities. The trends below reflect how this is playing out across select sectors.
Manufacturing
Manufacturing organizations often find themselves at one of two extremes: strong ERP systems paired with outdated legacy plant systems, or advanced plant systems constrained by legacy ERPs. Both scenarios make it difficult to integrate operational and finance data. This limits the finance function’s ability to access timely, granular operational inputs needed for accurate costing, forecasting, and performance analysis.
The priority for manufacturers is integrating operational technology with financial systems, enabling finance to move beyond retrospective reporting toward real-time margin visibility, scenario modelling, and more dynamic decision support.
Mining
Operational technologies used in extraction are often highly advanced, while finance systems remain fragmented across regions. This leaves finance teams with rich site-level data, but limited ability to assess performance across geographically dispersed operations or deliver consistent, enterprise-wide insights.
As a result, finance functions often spend disproportionate time reconciling and analyzing data locally rather than informing strategic decisions. The priority is to standardize and consolidate financial data across global operations, enabling finance to support enterprise-level planning and capital allocation.
Technology
The maturity landscape across tech companies is highly uneven, with a stronger orientation toward product innovation than operational discipline. This leaves finance teams managing rapid growth with fragmented data and inconsistent reporting structures.
The priority is embedding scalable finance processes and data governance early, positioning finance to deliver reliable forecasting, support unit economics analysis, and act as a strategic partner as the business scales.
Realize value from digital transformation in finance
Succeeding in the future of finance isn't about adopting the right tools, but about knowing exactly what you need them to do. At BDO, we focus on where transformation drives the greatest impact, combining leading technologies, proprietary solutions, and strategic partnerships to address core finance, audit, and assurance challenges. Our approach helps CFOs and finance leaders translate digital capabilities into stronger insights, more efficient processes, and trusted decision support.
Do more within your finance function
The future of finance
The finance function faces an opportunity and a clear imperative to transform and elevate its strategic impact. Our hub helps CFOs and finance leaders understand where opportunities lie and how we can empower transformation through technology-enabled change that supports long-term value creation.
CFO and finance function transformation
Our podcast explores how the transformation of the finance function enables leaders to influence business strategy and decision-making.