Is Your Organization AI-Ready? Assess Before You Implement!

Welcome to This Week’s Issue!

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.

That’s why this edition is all about AI readiness—what it really means and why it should be the first step in any AI initiative.

The Balanced View: Why AI-Readiness Assessment is an Essential Step

Rolling out AI without evaluating readiness is like trying to run a marathon without training—you’ll end up frustrated, inefficient, and unlikely to reach the finish line. That’s why conducting an AI-readiness assessment is crucial before making any major investments.

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 deeper 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 is a huge 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 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 developed a process in which a human was always involved in critical decisions, and the decisions were 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.

How One AI Champion Can Accelerate AI Implementation

One of the biggest success factors in implementing AI is not just the right technology but also having the right person leading the charge.

My advice to fellow CFOs: Identify a high-potential team member who is eager to learn, open to change, and can bridge the gap between finance and technology. Then, invest in structured AI education for them.

This is a small investment with big payoffs, allowing you to:
✔️ Accelerate AI adoption without bottlenecking everything at the CFO level.
✔️ Upskill your team in a way that aligns AI with finance needs.
✔️ Build internal expertise instead of always relying on external consultants.
✔️ Foster long-term engagement by integrating AI education into career development plans.

Where to Start: Curated AI Courses for Finance Teams

If you’re looking for the right AI learning programs for your finance champion, here are some excellent options:

For Hands-on AI Workflow Automation: AI for Finance – Advanced (Live Course)
Cost: $599 (one-time)
Duration: 4 hours (live sessions) + lifetime access to materials
A cohort-based course designed for CFOs, FP&A managers, and analysts. Focuses on using generative AI and Python to automate finance workflows—no coding experience required.

For AI Strategy & Leadership: Artificial Intelligence: Implications for Business Strategy (MIT)
Cost: $3,850
Duration: 6 weeks (6–8 hours per week)
A deep dive into AI-driven business models, strategy, and innovation, taught by MIT experts. Ideal for finance leaders looking to implement AI at an organizational level and drive long-term transformation.

For AI Applications in Finance: AI in Finance Specialization (CFTE)
Cost: £600 one-time or £200/month
Duration: Self-paced (6-month access)
Covers AI’s role in banking, trading, and risk management, with real-world case studies. Designed for finance professionals who want to understand AI’s industry impact and practical applications.

For AI Fundamentals & Ethics: University of Pennsylvania’s AI for Business Specialization (Coursera)
Cost: Free
Duration: 1 month (10 hours per week)
A beginner-friendly program covering machine learning, AI ethics, and Big Data in finance. Features case studies from JPMorgan Chase and DBS Bank, plus hands-on assessments to reinforce learning.

Next Steps for CFOs

1️⃣ Identify a finance team member who is adaptable, analytical, and eager to grow.
2️⃣ Enroll them in an AI course and provide space for hands-on experimentation with AI tools.
3️⃣ Track their progress—set milestones, have them share learnings, and assess how AI is impacting their work.
4️⃣ Incorporate AI education into career goals—make AI skills part of their professional growth plan.
5️⃣ Empower them to lead AI adoption within your finance team, positioning them as an internal AI expert.

Closing Thoughts

Over the next four editions, I’ll be breaking down each pillar of AI readiness—Process, People, Data, and Governance—and sharing real-life examples from my work.

I’ll also walk you through the exact AI readiness framework I use with finance teams so you can apply it in your own organization.

AI can transform finance—but only if it’s built on the right foundation. More on that next time.

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Until next Tuesday, keep balancing!

Anna Tiomina 
AI-Powered CFO