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.