Over the past year, I’ve been deeply focused on AI education—helping finance professionals understand AI’s potential. But recently, my work has shifted. More and more finance teams are moving from learning about AI to actively implementing it.

And while I’m excited to see this adoption happening, I’m also more convinced than ever that finance needs a structured approach to AI implementation.

Why? Because finance isn’t like other functions. We handle critical processes, sensitive data, and high-risk decisions. AI can be a powerful tool, but if it’s applied without assessing process structure, data integrity, security, and governance, it can just as easily create new problems as it solves existing ones.

The Four Pillars of AI Readiness

AI readiness isn’t just about having access to the latest technology. It’s about ensuring your organization has the right foundation to successfully integrate AI into your finance workflows. This foundation consists of four key pillars:

1. Process Readiness

Are your finance processes structured and standardized enough for AI integration?

AI thrives in environments where data and workflows follow clear, repeatable structures. If your processes are inconsistent or unstructured, AI will struggle to deliver reliable results.

Key considerations:

• Are financial workflows well-documented and consistently followed?
• Are there clear inputs and outputs for processes like forecasting, reporting, and compliance?
• Can existing processes be streamlined before introducing AI?

Example: A company eager to implement an AI-driven budgeting process was already evaluating technical solutions. However, when we examined their financial workflows, we discovered a 25-30% variance between budgeted and actual costs, indicating deep process inefficiencies. They lacked a structured month-end closing process and weren’t properly accounting for mid-year accruals. If AI were introduced into this setup, it would only amplify existing inconsistencies. We took a step back to fix these foundational gaps first, ensuring AI could later be leveraged effectively.

2. People Readiness

Is your team prepared to work alongside AI?

AI adoption isn’t just a technical upgrade—it’s a cultural shift. If your team isn’t comfortable using AI tools, implementation will stall.

Key considerations:

• Does your team understand AI’s role in finance?
• Are finance professionals open to AI, or is there resistance?
• Have employees received training on responsible and effective AI use?

Example: A construction company wanted to implement AI-powered expense reconciliation but faced immediate resistance. Employees were concerned about errors, but even more so about job security. To address this, the company launched AI literacy workshops and held career development discussions, showing employees how AI would enhance their roles rather than replace them. With these efforts, adoption became much smoother.

3. Data Readiness

Do you have the right data infrastructure to support AI initiatives?

AI relies on clean, structured data. If your data is fragmented, inconsistent, or inaccessible, AI-driven insights won’t be reliable.

Key considerations:

• Is your financial data clean, structured, and stored in an accessible format?
• Are data silos preventing AI from pulling insights across departments?
• Do you have a clear strategy for data governance and security?

Example: A multinational company wanted to implement AI-driven revenue forecasting. However, when we reviewed their data, we found that different locations used different revenue recognition methods, making meaningful forecasting impossible. Instead of enforcing a standardized accounting method across locations, we introduced an AI-powered layer to convert cash-based revenue to an accrual basis for better forecasting while maintaining compliance with local financial regulations.

4. Governance Readiness

Do you have the right policies and oversight frameworks to support AI adoption?

AI implementation requires clear governance policies to manage compliance, ethical considerations, and risk mitigation.

Key considerations:

• Are there established AI governance policies addressing compliance, ethics, and security?
• Have you identified potential risks and mitigation strategies?
• Is there a structured framework for AI accountability and oversight?

Example: A finance department implementing AI for expense reporting needed to maintain a clear audit trail due to regulatory requirements. To comply, they created a process that involved human participation in critical steps. The decisions were thoroughly documented, ensuring audibility and accountability.

Why AI Readiness Matters More in Finance

AI readiness is especially critical in finance, where regulatory scrutiny is high and errors are costly. Finance processes are often mission-critical, meaning any mistakes could lead to compliance failures, financial misstatements, or reputational damage.

Skipping the readiness assessment stage can lead to AI amplifying existing inefficiencies rather than improving them.

Does AI Readiness Need to Be a Heavy Process?

Not necessarily. The depth of the assessment depends on your organization’s size, complexity, and AI ambitions. However, one thing is non-negotiable—every AI implementation in finance should include a structured readiness check before moving forward.

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