Who’s Watching the AI? AI Auditors and Other Emerging Roles

AI is not just replacing jobs—it’s creating new ones that demand cross-disciplinary expertise. We’ll soon start seeing job descriptions explicitly requiring "AI proficiency" for finance roles. In fact, it’s already happening in some positions I’ve come across.

In this issue, I’m exploring what types of roles will emerge as AI takes on a bigger share of our operations, how professionals can master these new opportunities, and what organizations need to do to hire and train for these roles effectively.

Free Webinar: How to Streamline Your Year-End Closing with AI

As the holiday season approaches, year-end closing is top of mind for many finance professionals. Before we celebrate, I’m hosting a free webinar on how AI can simplify and streamline your year-end closing.

This will be more of a live discussion than a formal presentation. I want to hear your challenges, questions, and ideas. Everyone’s invited to join, so feel free to send me your questions in advance!

📅 Date: December 17th
🕒 Time: 11 am US Central time
📍 Location: via Zoom (Registration Link)
 

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If you’ve been thinking about joining the AI Finance Club, now is the perfect time! As a special offer for my subscribers, you can get 50% off your membership using this link.

The AI Finance Club is a fantastic resource for professionals eager to leverage AI in finance. It offers:

• Learning paths for beginners to advanced users.

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Don’t miss out on this opportunity to stay ahead in the AI-driven future of finance.

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The Balanced View: AI's Impact on Job Roles in Finance and Operations

AI is transforming industries, and finance and back-office operations are no exception. Some roles may be replaced, some will be augmented, and new opportunities will emerge. To harness AI's potential, organizations need specialists who understand how to integrate AI into business processes.

Here are the roles that will be in high demand as AI adoption accelerates across finance and operations:

1. AI Auditor

   • What They Do: Audit AI tools to ensure their accuracy, fairness, and alignment with company goals. This involves testing algorithms for biases, monitoring performance, and validating outcomes.

   • Why It’s Important: Auditing ensures transparency and accountability as AI systems influence financial decisions.

   • Example Task: Validate a machine learning model used for fraud detection to confirm it doesn’t disproportionately flag legitimate transactions.

   • Best Starting Point: Background in auditing, business process analysis, or financial compliance.

2. AI Compliance Manager

   • What They Do: Oversee compliance with legal and ethical standards related to AI use, such as data privacy regulations.

   • Why It’s Important: Regulatory scrutiny is increasing, making compliance and ethical adherence essential.

   • Example Task: Develop internal guidelines for AI usage that align with privacy laws, create an internal process to monitor AI usage, and ensure compliance with regulatory guidelines.

   • Best Starting Point: Experience in legal compliance, data privacy, or regulatory affairs.

3. AI Analyst

   • What They Do: Act as the bridge between AI tools and business teams, interpreting outputs and translating them into actionable business insights.

   • Why It’s Important: Human expertise is crucial for extracting value from AI-generated insights and driving decision-making.

   • Example Task: Assist business units in building dynamic AI-powered forecasts and integrating data collection.

   • Best Starting Point: Background in data analysis, business intelligence, or finance operations.

4. AI Implementation Expert

   • What They Do: Lead the introduction of AI tools into existing systems, manage AI projects, train teams, and troubleshoot issues.

   • Why It’s Important: Successful AI adoption requires seamless integration, user training, and continuous troubleshooting.

   • Example Task: Integrate an AI-powered expense management platform and train staff to use it effectively.

   • Best Starting Point: Experience in project management, IT integration, or systems implementation.

5. AI Workflow Designer

   • What They Do: Redesign processes to incorporate AI, ensuring an optimal balance between automation and human intervention.

   • Why It’s Important: AI delivers the best results when thoughtfully integrated into workflows that still leverage human judgment where necessary.

   • Example Task: Automate invoice approvals while adding a manual checkpoint for exceptions.

   • Best Starting Point: Expertise in process design, operations management, or workflow automation.

Becoming an AI Specialist: Steps to Take

To capitalize on the growing demand for AI specialists in finance and operations, here’s how professionals can position themselves:

1. Build a Strong AI Foundation

   • Take courses or certifications in AI and machine learning to understand the fundamentals.

   • Learn about AI applications specific to finance and operations, including automation, forecasting, and compliance.

   • Understand ethical considerations, such as data privacy and algorithmic bias.

2. Gain Practical Experience

   • Experiment with AI tools in your current role to gain hands-on experience.

   • Volunteer for internal projects involving AI and actively seek opportunities to integrate AI into your workflow.

3. Stay Informed

   • Join AI-focused communities, attend relevant webinars, and follow industry leaders to stay updated on AI advancements and their impact on business practices.

Fostering AI Talent Within Organizations

If you are hiring managers or department leaders, cultivating AI expertise internally can set your organization apart. Here’s how to do it effectively:

1. Identify AI Champions: Look for team members who are enthusiastic about technology and comfortable with new tools. These individuals are ideal candidates for AI upskilling.

2. Invest in Training: Provide budgets and time for AI-focused education through formal courses, certifications, or hands-on workshops.

3. Set AI-Related Goals: Encourage team members to apply their AI skills by including AI projects in their goals and KPIs. Track progress and reward their contributions.

4. Create a Safe Space for Experimentation: Foster an environment where employees feel encouraged to experiment with AI, learn from failures, and iterate without fear.

5. Acknowledge Their Efforts: Developing AI skills requires dedication. Recognize the value of their time and effort and offer incentives to retain these new AI champions, who may become attractive to competitors.

Hiring AI Specialists

Keep in mind that there are no ready specialists on the market with “5 years of AI implementation experience”—you will have to compromise and augment the expertise.

• Seek Out Specialized Training: Target candidates with specific AI certifications or coursework that aligns with your business needs.

• Consider a Hire-and-Train Approach: Hire motivated professionals and provide them with AI training, a strategy that can effectively fill skill gaps.

• Partner with Universities: Collaborate with educational institutions that offer AI programs to identify and hire promising graduates.

• Utilize Freelancers: Use freelance platforms to bring in AI expertise for specific projects, helping build internal knowledge.

The future of finance and operations is increasingly AI-driven, with new roles focused on maximizing AI’s impact. Organizations can stay ahead of the curve by building internal expertise and hiring strategically. For finance professionals, now is the time to adapt, upskill, and be proactive in shaping an AI-enabled future

Debunking the Myth: AI and the Realities of "Clean" Data

One of the most persistent myths about using AI in finance is that you need "perfectly clean" data to get started. While data quality is undeniably crucial, it's not as black-and-white as many think. AI can be a powerful ally in cleaning, formatting, and consolidating data—but there are important nuances to consider.

What AI Can Do With "Messy" Data

AI excels in handling certain types of imperfections in your data, especially when it comes to:

1. Reformatting: If your data is reasonably clean but stored in inconsistent formats (e.g., multiple currencies, date styles, or file types), AI tools can harmonize it. For example:

   • Tools like ChatGPT can reformat financial reports to match templates.

   • AI-powered ETL (Extract, Transform, Load) solutions like Alteryx or Tableau Prep can consolidate and reformat datasets.

2. Filling Gaps: Missing values in structured datasets can often be filled using AI-based interpolation or predictive algorithms. For example, if sales data is incomplete, AI can estimate gaps using patterns from existing data.

3. Normalizing Data: AI can resolve small inconsistencies like duplicate entries or incorrect categorizations, saving hours of manual corrections.

When AI Falls Short

However, AI isn't a silver bullet. Here are scenarios where human oversight and judgment are still essential:

1. Uncertain Data Sources: If your data comes from multiple, conflicting sources, AI struggles to determine which source is accurate without explicit guidance. For example:

   • Mismatched revenue numbers from CRM and ERP systems.

   • Discrepancies in vendor payment records across multiple platforms.

2. Undefined Relationships: When the relationships between datasets are unclear (e.g., which expenses belong to which departments), AI can't make reliable assumptions without human input.

3. Lack of Context: AI may not understand qualitative nuances, like why a certain expense was categorized as an "exception" or why specific data from a partner might carry inherent biases.

Bottom Line: AI is a valuable tool for handling repetitive tasks and standardizing data, but it’s no substitute for human judgment in addressing nuanced or conflicting information.

Recommendations from the AI CFO: A Video and a Book

Video: AI is Transforming the World of Work, Are We Ready for It? | FT Working It

This video explores how AI is changing the workplace across industries, including finance. It raises critical questions about whether companies and individuals are truly prepared for the challenges and opportunities that AI brings.


Book: AI Foundations for Finance Professionals by Glenn Hopper

This book is a must-read for anyone looking to build a solid understanding of how AI applies specifically to the finance world. Glenn Hopper does an excellent job breaking down complex AI concepts into relatable and actionable insights.

Closing Thoughts

I know AI can feel a little intimidating—after all, change always is. But I truly believe it’s opening up exciting opportunities for all of us, even for the auditors! Imagine spending less time on tedious reconciliations and more time on valuable, strategic work (or maybe even leaving work on time for a change).

Let the machines handle the grunt work so we can focus on what humans do best: solving problems, making decisions, and adding real value.

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Until next Tuesday, keep balancing!

Anna Tiomina 
AI-Powered CFO