The Next Phase of AI in Finance Is Here. Is Your Team Prepared?

The Next Phase of AI in Finance

We are now entering a far more transformative stage of AI use in accounting and finance where we will see AI integrated much more deeply across finance functions. Teams will begin to use AI to generate the predictive insights that support scenario modelling, and power real-time business intelligence. This will turn finance teams from historical reporters into strategic advisors.

Perhaps one of the biggest indicators of the power of AI has in finance and accounting functions is its potential impact on financial reporting. Traditional month-end and year-end processes, which once defined the financial calendar, are gradually being replaced by dynamic, continuous accounting practices. In fact, 75% of SMBs are expected to adopt dynamic, continuous accounting practices by 2030. Instead of waiting to analyse and report financial data, organisations will increasingly rely on real-time data to enable agile decision-making and ensure more proactive strategy development.

The Strategic Impact of AI in Finance

While many organisations have started using AI for automation, the next phase will see AI more deeply integrated across key financial functions. The impact of AI on finance can be felt across three core pillars:

1. Accounting and Reporting
   AI improves the accuracy of financial reporting and enables organisations to move from traditional periodic reporting to continuous, real-time updates. AI reduces manual intervention, minimising human error, and creating a single, consistent source of truth across finance and operations.
2. Governance and Risk Management (Including Control)
   Effective risk management is critical in today’s uncertain environment, and AI plays a crucial role here. With AI’s ability to monitor financial data in real-time, businesses can identify anomalies and potential risks earlier, improving their ability to take corrective action. AI also ensures governance standards are consistently met, without overburdening teams.
3. Data-Driven Decision-Making and Planning
   AI moves finance from historical reporting to forward-looking, data-driven decision-making. By integrating financial data with external variables such as market trends, customer behaviour, and supply chain disruptions, AI equips finance teams to predict outcomes and perform scenario analyses that guide leadership with actionable insights.

Sector size and resources also change how organisations address AI. Larger enterprises often benefit from greater investment capacity and access to specialised talent, but they can be held back by complex legacy systems and multiple layers of governance. Integrating AI into such environments can be slow and challenging, even if the potential for transformation is substantial.

SMEs are able to leverage more agile, modern cloud platforms and adopt new technologies quickly. However, scaling AI can be difficult without disciplined data management practices and sufficient digital skills within the finance team.

Regardless of size or sector, successful AI integration comes from structural readiness. The biggest challenge for CFOs is the maturity of their data management and crucially, leadership’s willingness to drive change.

Why Some Finance Functions Are Moving Faster Than Others

The pace at which finance functions adopt AI has not been uniform. Instead it has largely been determined by foundational readiness. The factors that typically determine how quickly organisations move from basic automation to strategic AI use are:

• Data and Systems Foundations
  AI is only as powerful as the data behind it. Organisations that operate on integrated, cloud-based ERP systems with clean master data have a strong foundation to move rapidly from basic automation to more advanced use cases like predictive modelling and real-time reporting. These firms can easily harness AI’s capabilities to make smarter, faster decisions. On the other hand, businesses relying on fragmented legacy systems and spreadsheet-driven processes often find AI initiatives stalling at the pilot stage. Without trusted, structured data, AI-driven insights cannot be relied upon, making advanced applications ineffective or unattainable.
• Risk and Governance Requirements
  Finance functions face unique requirements when it comes to AI adoption. Outputs need to be traceable, auditable, and defensible, especially when regulatory scrutiny is high. In highly regulated sectors such as financial services, healthcare, and utilities, this can slow adoption. Not because leaders doubt its value, but because they must ensure appropriate controls and oversight are embedded into every process. The organisations that are moving the fastest are those that view AI governance as an integral part of their transformation strategy rather than a barrier to it. Embedding governance controls into their AI strategy early, they reap the rewards of AI without sacrificing regulatory compliance.
• Operating Model and Capability
  Technology alone cannot shift a finance function into a strategic, decision-driving role. Organisations must redesign processes, clarify ownership across core finance cycles such as Record-to-Report, Order-to-Cash, and Procure-to-Pay, and develop data literacy across the entire finance function. The most advanced finance functions are rethinking how they contribute to live decision-making and redesigning their operating model to ensure teams have the skills, structure, and processes in place to leverage AI for strategic value.

Those that have the right building blocks in place are able to move faster, while others must overcome legacy barriers before they can fully unlock the potential of AI in finance.

Assessing Your Current AI Preparedness

Many finance teams remain in the early stages of AI integration. That’s understandable. The technology is evolving rapidly, and the skills required aren’t always readily available internally. But with AI adoption accelerating, leaders must move on from cautious experimentation.

Before moving forward, finance leaders must understand their current level of AI preparedness, because without a clear baseline it’s impossible to prioritise investment, address capability gaps, or scale AI in a way that delivers measurable business value.

Consider these key areas:

• Technology Usage
  Are AI tools already embedded in reporting, forecasting, or decision support?
• Talent Capability
  Do you have professionals with data literacy, modelling expertise, or AI governance experience?
• Culture
  Is there openness to experimentation and continuous improvement?
• Leadership Mindset
  Are senior leaders aligned on the value of AI beyond automation?

Understanding where you sit across these dimensions provides a practical starting point for building an AI-ready finance function.

Want a clearer view of your current position? Take Cedar’s finance AI benchmarking tool to assess your team’s digital readiness and identify priority actions for the next phase of transformation.

Building an AI-Ready Finance Team

Once you’ve assessed your AI readiness, you can take steps to build teams that are truly prepared for the impact of AI in finance and accounting.

Creating a Culture of Innovation in Finance Teams

No AI transformation succeeds without the right culture. Finance teams have traditionally been risk-averse, but the next phase of AI in accounting and finance requires curiosity, experimentation, and adaptability. Leaders can foster this by:

• Encouraging cross-functional collaboration with data, tech, and ops teams.
• Recognising and rewarding innovation and learning.
• Providing safe spaces to test and learn from AI tools.
• Regularly communicating how AI supports business goals.

Change won’t happen overnight, but small cultural shifts build momentum over time.

Skills for the Future of Finance

The future of finance and the use of AI in the finance function is not about replacing people with machines. Instead, we must provide professionals with tools that will enhance their judgement and capabilities and the skills and expertise to use them properly.

To successfully implement AI, finance teams must be able to combine traditional accounting expertise with new digital and analytical competencies that include:

• Data Storytelling and Visualisation
• AI Model Interpretation and Oversight
• Scenario Analysis and Predictive Forecasting
• Transformation and Change Management
• Cybersecurity and Data Governance

Upskilling existing staff is essential, but it won’t be enough on its own. Leaders must also consider where to bring in new talent with specialised skills in AI, data science, and digital finance transformation.

Hiring for the AI-Ready Finance Function

The shift toward AI in finance has major implications for hiring. Finance leaders should review their talent strategy through the lens of digital readiness. This means:

• Prioritising candidates with experience in finance transformation.
• Looking for analytical aptitude and adaptability over traditional credentials.
• Using behavioural interviews to assess openness to change.
• Creating blended teams that combine finance, tech, and data expertise.

It also means thinking creatively about interim solutions. Many organisations are turning to interim finance leaders with experience in digital transformation to bridge capability gaps and accelerate progress.

Benchmarking Your Digital Readiness

Most finance leaders are well aware that the use of AI in finance and accounting has already changed how they and their teams are, and will, work. The question is, are you and your team prepared?

Finance leaders who act now will position their teams for success. Their teams will not just be using AI for efficiency, but in leading strategy and driving value across the business.

Want to know how ready your finance function really is?

Take Cedar’s benchmarking tool today and assess your digital readiness. It only takes a few minutes and could help define your next strategic move.