Strategic focus, high-quality data expedite implementation
Executive summary
CFOs are turning in large numbers to AI to drive growth, efficiency and resilience across finance operations. With 96% of CFOs increasing tech investments this year, AI is reshaping processes from forecasting to compliance. To take full advantage of AI’s capabilities, finance leaders need to promote high-quality data, strong governance and alignment with business goals. By integrating AI thoughtfully, finance teams can transform into agile, innovation-driven partners in enterprise leadership.
Thoughtful deployment is critical
A substantial majority of CFOs today are finding that strategic implementation of new technologies and automation mechanisms such as AI offers a powerful path to long-term growth, operational efficiency, and even resilience and compliance.
Ninety-six percent of CFOs are increasing their investment in technology this year, and 60% say their organizations are using generative AI, according to Grant Thornton’s Digital Transformation Survey. Meanwhile, more than three-fourths (78%) of CFOs said improving financial operations and processes is one of the top three priorities for technology enhancement in their organizations.
AI, which represents the next frontier of societal and business advancement, is becoming a critical element for improvements throughout finance operations. While still in its early stages, AI’s potential is vast — and so are the uncertainties around how best to prepare for, implement and use it.
“Because AI systems are designed to continuously learn and evolve, thoughtful and strategic deployment is critical to ensuring long-term value and impact,” said Grant Thornton Business Consulting Partner Ronald Gothelf. “With the right strategy, CFOs can create substantial benefits by deploying emerging technologies such as AI.”
In addition to enhancing cost control, AI enables more precise modeling and forecasting while supporting continuous adaptation to evolving business needs. AI offers a strategic edge — driving operational efficiency through automation, improving financial planning accuracy, and fostering a culture of innovation. By integrating advanced technologies, replacing legacy systems and aligning platforms across functions, CFOs can position their organizations for sustained performance and agility.
Technology practices for long-term growth
As organizations mature, they adopt cloud computing, automation and advanced analytics, ultimately using generative AI to drive strategic insights and sustainable growth. This transformation has repositioned finance as a proactive, innovation-focused partner in business leadership.
However, successful AI adoption requires more than technological readiness. It starts with governance. To safeguard sensitive financial information and ensure that AI use conforms with organizational principles, leadership must establish clear governance and usage policies before deploying AI tools. These policies should define acceptable use parameters, outline responsibilities for oversight and set boundaries for how AI systems interact with confidential data. Without such guardrails, AI models — especially those that learn from user inputs — can inadvertently expose proprietary insights or personal financial details.
Once sound governance is established, finance leaders must carefully evaluate the cost of implementation against the expected return, considering infrastructure, talent and change management investments. Equally important is identifying high-impact use cases — such as financial planning and analysis (FP&A), demand forecasting, anomaly detection and operational automation — where AI can deliver measurable value in the finance function.
After these foundational variables are defined, organizations can assess their current position on a technology maturity model to determine the appropriate tools and platforms needed to support AI integration. This evaluation helps identify gaps in existing systems, prioritize investments and align technology choices with business objectives — ensuring that AI is deployed where it can generate the greatest impact. Using Grant Thornton’s strategic maturity model approach, we can evaluate how various forms of automation are being used in finance and accounting processes:
Developing Automation Mechanisms: At the foundational level, the developing phase includes data analytics and visualizations and robotic process automation (RPA). These technologies represent the earliest steps in automation maturity, relying on structured data and rule-based logic. Data analytics tools are used to generate dashboards and calculate KPIs, providing visibility into business performance. RPA automates repetitive, manual tasks such as data entry and invoice processing, but lacks the ability to learn or adapt. These tools offer efficiency gains but are limited in intelligence and flexibility.
Defined Automation Mechanisms: The defined phase introduces more structured and scalable automation capabilities, including low-code/no-code applications and intelligent document processing (IDP). These tools allow organizations to streamline workflows and digitize manual processes with minimal technical expertise. Low-code platforms enable rapid development of applications for tasks such as invoice capture and approval routing. IDP enhances document handling by extracting data from unstructured formats using predefined rules. While still rooted in simple data manipulations, these technologies lay the groundwork for more intelligent automation.
Advanced Automation Mechanisms: In the advanced phase, organizations begin to integrate AI-driven insights into their operations through process mining, predictive analytics and machine learning. Process mining applies data science to map and analyze workflows, identify inefficiencies and ensure compliance. Predictive analytics and machine learning use historical data to forecast trends, enabling more accurate financial planning and decision-making. These tools mark a shift from reactive to proactive operations, analyzing data to anticipate outcomes and optimize performance.
Leading Automation Mechanisms: The leading phase represents the highest level of automation maturity, characterized by advanced AI technologies such as natural language processing (NLP) and GenAI. NLP enables systems to interpret and act on human language, streamlining workflows and enhancing decision support. In finance, machine learning plays a critical role by analyzing historical data to identify patterns and predict outcomes such as market trends, credit risks and customer behavior — improving decision-making accuracy as more diverse data is processed.
GenAI builds on this by uncovering complex relationships between data sets and generating entirely new insights. It can simulate financial scenarios, create forecasts and even generate reports — transforming it from a supportive tool into a strategic enabler that enhances planning, innovation and enterprise-wide decision-making. Agentic AI further advances the maturity of GenAI by enabling autonomous, goal-driven agents that can initiate, monitor and adapt financial processes in real time. These agents can coordinate across systems, proactively resolve exceptions, and continuously optimize workflows — freeing up finance teams to focus on higher-value strategic activities.
This chart shows how companies proceed in their automation journeys. They start with simple technologies with limited AI and machine learning capabilities and mature to use tools with significant AI and machine learning influences that free up finance teams to focus on higher-value strategic activities.
How we can help you
SERVICE
SERVICE
AI use cases across the finance lifecycle
AI is reshaping core finance processes across order-to-cash, procure-to-pay, record-to-report and FP&A functionalities. Here’s how:
Implement with a strategic focus
Success in AI implementation, similar to all other technology implementations, hinges on taking a strategic approach — with a clear focus on data integrity, governance and alignment with business objectives.
“Before AI can deliver meaningful insights, organizations must establish a strong operational foundation to enable their technology,” said Grant Thornton Business Consulting Senior Associate Drew Grisemer. This foundation includes implementing standardized, well-documented processes, organizational design and governance mechanisms to empower financial systems to promote consistency, scalability and auditability. Streamlined processes reduce variability and ensure reliable inputs for AI models.
Comprehensive documentation supports automation, compliance and change management, while modern, flexible tools enable seamless integration with AI capabilities. Investing in these foundational elements positions organizations to deploy AI with confidence and realize meaningful, sustainable impact.
Equally important is the establishment of comprehensive data governance policies. These policies should define clear roles and responsibilities for data ownership and stewardship across the finance function. Data owners are accountable for the accuracy and relevance of financial data sets, while data stewards oversee the integrity, lineage and compliance of data assets. Together, they ensure that data is not only high quality but also secure and ethically managed.
Data governance frameworks must be established to ensure the availability of mechanisms for defining and storing key data, ongoing data quality monitoring, access controls, and audit trails. This is especially vital in finance, where regulatory compliance and risk management are paramount.
“By embedding governance into the data lifecycle, organizations can mitigate risks and build trust in AI-driven insights,” said Grant Thornton Business Consulting Senior Manager Steven Truant.
Streamlining systems to reduce redundancy simplifies AI integration and enhances scalability. Enterprise resource planning (ERP) platforms such as Oracle, SAP, Workday, Dynamics 365 and NetSuite offer built-in AI capabilities and support future enhancements. Centralized data warehouses — including Snowflake, Redshift, BigQuery and Microsoft Azure — enable efficient querying and analysis, supporting business intelligence and decision-making.
Supplemental technologies, including Alteryx, BlackLine, and a variety of FP&A tools, further enable finance processes within ERP environments. AI-specific solutions such as BlackLine, FloQast and MindBridge provide advanced capabilities such as anomaly detection and predictive analytics.
To enable successful AI adoption, organizations must define clear business and technical requirements, conduct thorough vendor evaluations, and implement robust change management plans. A phased rollout allows teams to test AI features and validate accuracy before full deployment.
Technology must be tailored to fit the organization’s unique needs and aligned with strategic goals. For finance teams, this means integrating AI to enhance processes such as the financial close, compliance and KPI tracking. Continuous optimization and user adoption are critical to maintaining compliance and ensuring that data governance policies are upheld.
Driving strategic value through AI
By embedding AI into finance operations, CFOs can unlock efficiencies, reduce risk and gain deeper insights. Finance leaders who align AI initiatives with business strategy, ensure data readiness and manage change skillfully have an opportunity to enhance growth, efficiency and resilience to generate an impact that can create a competitive edge.
Contacts:
Content disclaimer
This Grant Thornton Advisors LLC content provides information and comments on current issues and developments. It is not a comprehensive analysis of the subject matter covered. It is not, and should not be construed as, accounting, legal, tax, or professional advice provided by Grant Thornton Advisors LLC. All relevant facts and circumstances, including the pertinent authoritative literature, need to be considered to arrive at conclusions that comply with matters addressed in this content.
Grant Thornton Advisors LLC and its subsidiary entities are not licensed CPA firms.
For additional information on topics covered in this content, contact a Grant Thornton Advisors LLC professional.
Trending topics
No Results Found. Please search again using different keywords and/or filters.
Share with your network
Share