Building Your Finance Team’s AI Literacy

This is the second installment in my series “Financial Leadership in the AI Era.” If you missed it, check out the previous post where we explored separating AI hype from reality in finance departments.

The Literacy Gap in Finance

Six weeks into my role as a finance manager, I’ve identified a significant challenge: the varying levels of AI literacy within our team. During a recent discussion about potentially implementing an AI-assisted forecasting tool, I noticed reactions ranging from unrealistic enthusiasm (“This will solve all our forecasting problems!”) to deep skepticism (“I don’t trust any black-box system”) to anxiety (“Will this replace my job?”).

This experience isn’t unique. According to a 2023 survey by the Association of International Certified Professional Accountants (AICPA), 78% of finance leaders cited “inadequate understanding of AI capabilities and limitations among team members” as a major barrier to effective AI implementation (AICPA, 2023).

The literacy gap creates real problems: it leads to poor technology decisions, ineffective implementation, resistance to valuable tools, and missed opportunities to enhance finance operations. Building a shared foundation of AI literacy has become my priority before we attempt any significant AI initiatives.

Essential AI Concepts Finance Professionals Need to Understand

After consulting with both technology experts and finance leaders who’ve successfully implemented AI, I’ve identified the core concepts that every finance professional should understand:

1. The AI Spectrum: From Automation to Intelligence

Finance teams often conflate basic automation with true AI capabilities. Understanding the spectrum is essential:

  • Rules-based Automation: Predefined instructions for handling specific scenarios (e.g., basic AP matching)
  • Robotic Process Automation (RPA): Software that mimics human actions for repetitive tasks (e.g., data extraction from invoices)
  • Machine Learning: Systems that learn from data to identify patterns and make predictions (e.g., anomaly detection in expenses)
  • Natural Language Processing: Ability to understand and generate human language (e.g., extracting key terms from contracts)
  • Deep Learning: Advanced neural networks that can handle complex, unstructured data (e.g., forecasting models that incorporate multiple data sources)

Understanding this spectrum helps finance teams set realistic expectations and choose appropriate solutions for specific challenges.

2. The Data Foundation

Many finance teams underestimate the importance of data quality for AI success. Essential concepts include:

  • Data Requirements: Different AI applications have different data needs in terms of volume, variety, and quality
  • Data Cleaning: The process of identifying and correcting errors or inconsistencies in datasets
  • Training Data: The historical information AI systems learn from
  • Bias in Data: How historical biases in data can be perpetuated or amplified by AI systems
  • Data Governance: Policies and procedures that ensure data accuracy, consistency, and security

According to IBM’s Institute for Business Value, organizations with strong data governance are 83% more likely to exceed expectations in their AI initiatives (IBM, 2023).

3. How AI Makes “Decisions”

Demystifying AI decision-making processes helps build appropriate trust:

  • Probabilistic vs. Deterministic: Understanding that many AI systems provide probability-based recommendations rather than certain answers
  • Pattern Recognition: How systems identify meaningful patterns in large datasets
  • Explainability: The degree to which AI decisions can be understood and explained by humans
  • Confidence Levels: How to interpret confidence scores in AI outputs
  • Edge Cases: Understanding situations where AI performance may degrade

A McKinsey study found that finance teams with basic understanding of AI decision-making were 45% more likely to successfully implement AI solutions compared to teams without this knowledge (McKinsey, 2024).

4. AI Ethics and Governance

As stewards of financial data and decision-making, finance teams need to understand:

  • Algorithmic Bias: How bias can enter AI systems and impact financial decisions
  • Transparency Requirements: Regulatory and ethical standards for AI transparency
  • Human Oversight: Best practices for maintaining appropriate human supervision
  • Audit Trails: Requirements for documenting AI-influenced decisions
  • Model Drift: How AI systems can become less accurate over time without proper oversight

The Financial Stability Board’s 2023 report emphasizes that financial institutions using AI must maintain clear accountability and governance frameworks regardless of algorithm complexity (Financial Stability Board, 2023).

Developing a Common AI Vocabulary

One of the first challenges I encountered was the lack of shared language around AI. Technical teams would use jargon like “supervised learning” or “feature engineering,” while finance team members struggled to articulate their requirements in terms the tech team could understand.

To address this, I created a simple “AI in Finance Glossary” for our department. Here are some key terms we’ve included:

  • Algorithm: A process or set of rules followed to solve a problem or perform a task
  • Artificial Intelligence (AI): Technology that enables computers to perform tasks that typically require human intelligence
  • Machine Learning (ML): A subset of AI where systems learn from data to improve performance
  • Training: The process of teaching an AI model using historical data
  • Model: A specific representation of patterns learned from data
  • Feature: An individual measurable property used as input for a machine learning algorithm
  • Supervised Learning: Training an algorithm on labeled data to predict outcomes
  • Unsupervised Learning: Finding patterns in unlabeled data
  • Confidence Score: A measure of how certain an AI system is about its prediction
  • Model Drift: The degradation of model performance over time as conditions change

Having this shared vocabulary has significantly improved our discussions with both vendors and IT partners. The CFA Institute offers an excellent expanded glossary specifically for finance professionals that we’ve drawn from (CFA Institute, 2023).

Training Resources and Approaches for Different Team Roles

Not everyone on a finance team needs the same level of AI knowledge. I’ve developed a tiered approach based on roles:

For All Finance Team Members: Foundational Literacy

Everyone needs to understand basic concepts and develop appropriate confidence in working with AI-assisted systems:

  • Resource: LinkedIn Learning’s “AI for Non-Technical Professionals” (2 hours)
  • Approach: Monthly lunch-and-learn sessions discussing real-world finance AI applications
  • Assessment: Basic quiz on AI terminology and capabilities

For Finance Analysts and Managers: Intermediate Knowledge

Those who will be specifying requirements or interpreting AI outputs need deeper understanding:

  • Resources:
    • Coursera’s “AI for Business” specialization by University of Pennsylvania (12-15 hours)
    • Harvard Business Review’s “AI Basics for Business” series
  • Approach: Hands-on workshops with sample data and simple AI tools
  • Assessment: Case study analysis of AI implementation in a finance context

For Finance Technology Specialists: Advanced Understanding

Team members who will serve as bridges between finance and technical teams require more technical knowledge:

  • Resources:
    • Google’s “Machine Learning Crash Course” (15-20 hours)
    • DataCamp’s “Machine Learning for Finance” track
  • Approach: Paired learning with data science team members on real projects
  • Assessment: Collaborative project applying an AI solution to a finance problem

According to Deloitte’s 2023 Global Finance Skills Survey, organizations that implement role-based AI training see 37% higher satisfaction with AI implementations compared to those with one-size-fits-all approaches (Deloitte, 2023).

Creating a Learning Roadmap

Based on my experience and research, I’ve created a six-month learning roadmap for our finance team:

Month 1: Awareness Building

  • AI foundations workshop for all team members
  • Introduction to the AI vocabulary guide
  • Assessment of current knowledge and attitudes

Month 2: Concept Exploration

  • Focused sessions on data quality and governance
  • Case study reviews of successful finance AI implementations
  • Introduction to ethical considerations

Month 3: Hands-On Exposure

  • Demonstration of AI tools already in use in the organization
  • Simple exercises with pre-built AI models
  • Discussion of how existing processes could be enhanced

Month 4: Application to Current Challenges

  • Identification of potential use cases within each team
  • Analysis of data readiness for identified use cases
  • Development of evaluation criteria for potential solutions

Month 5: Vendor Evaluation Skills

  • How to assess AI vendor claims
  • Questions to ask during demonstrations
  • Frameworks for comparing solutions

Month 6: Implementation Planning

  • Change management considerations
  • Success metrics development
  • Building an AI implementation roadmap

According to PwC’s Finance Effectiveness Benchmark Report, organizations with structured AI learning programs achieve 31% higher ROI on their AI investments in finance functions (PwC, 2023).

How to Assess Your Team’s Current AI Readiness

Before implementing any learning program, it’s important to assess where your team stands currently. I developed a simple assessment framework with three components:

1. Knowledge Assessment

A brief survey to gauge understanding of key concepts:

  • Basic terminology comprehension
  • Understanding of AI capabilities and limitations
  • Familiarity with data concepts

2. Skills Inventory

Identifying existing relevant skills:

  • Data analysis capabilities
  • Experience with automation tools
  • Process improvement expertise
  • Change management experience

3. Attitude Evaluation

Understanding emotional and psychological readiness:

  • Comfort level with technology change
  • Trust in algorithmic decision support
  • Concerns about job impact
  • Interest in developing new skills

When we conducted this assessment with our team, we discovered surprising insights: while technical knowledge was indeed limited, we had strong foundations in data analysis and process improvement that would transfer well to AI implementation. The biggest gaps were in understanding how AI systems make decisions and in confidence evaluating vendor claims.

Early Results from Our Learning Journey

Two months into our AI literacy initiative, we’re seeing promising signs:

  • Team members are asking more sophisticated questions about AI capabilities
  • Discussions with vendors are more productive and focused
  • Two team members have identified potential AI use cases in their areas
  • Anxiety about AI has decreased as understanding has increased

The most significant shift has been from viewing AI as either a threat or a silver bullet to seeing it as a tool with specific strengths and limitations that can enhance our existing processes.

My Learning So Far

The biggest surprise in my AI literacy journey has been realizing how much finance expertise is actually required for successful AI implementation. Far from being replaced by technology, finance professionals with AI literacy become more valuable because they can apply domain knowledge to shape how AI is used.

I’ve also learned that building AI literacy is as much about change management as it is about technical education. Addressing concerns, building confidence, and creating safe spaces for experimentation have been just as important as explaining technical concepts.

In my next post, I’ll explore “Ethical Considerations in Financial AI,” examining how finance teams can ensure AI implementations align with ethical standards and regulatory requirements. I’ll share the framework we’re developing to evaluate ethical implications of AI decisions in our finance department.

Your Turn

I’d love to hear about your experiences with AI literacy in finance teams:

  • What approaches have you found effective for building AI understanding?
  • Which concepts do finance professionals find most challenging?
  • What resources have you found most valuable?

Share your thoughts in the comments below or reach out directly.

Sources

  • Association of International Certified Professional Accountants (AICPA). (2023). Finance Function Digital Transformation Survey. AICPA.
  • CFA Institute. (2023). Artificial Intelligence in Investment Management: A Practical Guide. CFA Institute Research Foundation.
  • Deloitte. (2023). Global Finance Skills Survey. Deloitte LLP.
  • Financial Stability Board. (2023). Artificial Intelligence and Machine Learning in Financial Services. FSB.
  • IBM Institute for Business Value. (2023). The AI Data Imperative. IBM.
  • McKinsey & Company. (2024). Building AI Capabilities in Finance Functions. McKinsey Digital.
  • PwC. (2023). Finance Effectiveness Benchmark Report. PricewaterhouseCoopers LLP.