AI Maturity Models: Guiding Finance into an Intelligent Future

Maturity models provide organizations with a roadmap for adopting new technologies and processes. As artificial intelligence (AI) transforms the finance sector, understanding and leveraging these models becomes crucial for success.

What is a maturity model?

Maturity models are frameworks that describe the typical stages an organization goes through as it implements and optimizes new capabilities. These models outline key characteristics, best practices, and measurable milestones for each stage of development.

Importance of maturity models in business

Organizations in finance and accounting use maturity models to guide their technology investments and overall adoption. These are some ways maturity models can be used:

1. Assess capabilities: Evaluate current tech integration, from basic automation to advanced AI analytics.

2. Identify improvement areas: Pinpoint processes needing enhancement, like manual data entry or inefficient reporting.

3. Set realistic goals: Establish achievable targets, such as reducing invoice processing time by 50%.

4. Prioritize investments: Decide whether to focus on upgrading core systems or implementing new AI tools.

5. Measure progress: Track improvements in KPIs like cost reduction, accuracy, or time savings.

These steps ensure strategic tech adoption aligned with organizational goals, maximizing ROI and driving digital transformation in finance. By providing a clear path forward, maturity models help businesses adopt complex technologies like AI in a structured, strategic manner.

Leveraging maturity models for technology adoption

When adopting new technologies, a maturity model serves as a guide through the implementation process. They typically outline stages from initial awareness and experimentation to full integration and optimization.

For AI adoption, a maturity model might include phases such as:

1. No AI capabilities

2. Basic AI experimentation

3. Targeted AI applications

4. Expanded AI integration

5. AI-driven transformation

Each stage builds upon the previous one, gradually increasing an organization's AI capabilities and the value derived from the technology.

The AI revolution in finance and accounting

Finance departments are increasingly turning to AI to streamline operations, improve accuracy, and gain deeper insights. AI can automate repetitive tasks, detect anomalies, forecast trends, and support complex decision-making.

Key drivers for AI adoption in finance include:

- Pressure to improve efficiency and reduce costs

- Need for more accurate and timely financial reporting

- Desire for deeper, data-driven business insights

- Increasing complexity of regulatory compliance

‍

How AI enhances finance and accounting

Artificial intelligence is transforming finance in numerous ways:

- Automating invoice processing and accounts payable workflows

- Improving accuracy in financial forecasting and budgeting

- Enhancing fraud detection and risk management

- Providing real-time financial insights and analytics

- Streamlining audit processes and improving compliance

As AI capabilities mature, finance teams can shift focus from routine tasks to strategic decision-making and value-added activities.

The Gartner^® AI maturity model for finance

In the research report, “Propel AI Capabilities With the Finance AI Maturity Model,” Gartner outlines five phases of finance AI maturity and provides guidance for building an AI roadmap unique to your team. This research provides valuable insights for organizations looking to adopt this technology.

According to Gartner, "The five phases of finance AI maturity [guide] finance transformation leaders along a steady progression of maturity that incrementally delivers new capabilities and value".

Gartner also emphasizes that "A one-size-fits-all approach will not work for every company. Each finance organization will require a tailored AI strategy that reflects an enterprise's unique circumstances".

Implementing the AI maturity model

To leverage the Gartner AI maturity model effectively, what we believe finance transformation leaders should do is:

1. Assess current maturity: Evaluate your organization's AI capabilities across all operational aspects.

2. Build a balanced roadmap: Address lagging areas to ensure even progression across all dimensions.

3. Set realistic expectations: Align organizational goals with the capabilities available at your current maturity level.

4. Anticipate and mitigate risks: Proactively address potential challenges associated with each maturity phase.

5. Continuously reassess and adjust: Regularly review progress and update your AI strategy as needed.

Gartner advises finance leaders to "Develop a five-phase, dual-path approach to building AI maturity. Start by leveraging AI embedded in standardized back-office software platforms. Simultaneously, start developing the skills needed for customized solutions that ensure long-term and strategic success."

Propel forward with AI

AI maturity models offer a structured approach for finance departments to adopt and optimize artificial intelligence technologies. By understanding and applying these models, organizations can navigate the complexities of AI implementation, set realistic goals, and maximize the value derived from their AI investments.

As finance continues to evolve in the age of AI, maturity models will play a crucial role in guiding organizations toward a more efficient, insightful, and strategically focused future.

To learn more about propelling your finance department's AI capabilities forward, access the complete Gartner research report, “Propel AI Capabilities With the Finance AI Maturity Model.”

Gartner, Propel AI Capabilities With the Finance AI Maturity Model, Mark D. McDonald, 26 June 2024.

GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.