Skipping AI Policy? Prepare for Disaster

Welcome to this week’s edition of Balanced AI Insights!

I see many organizations implementing AI, and sometimes, teams try to skip the policy and training phase and jump straight into implementation. I hate unnecessary bureaucracy as much as anyone. But in this case, I’m convinced that preparation is the key to success. 

In today’s issue, we’ll explore why skipping policy and training can lead to chaos, costly mistakes, and lost trust. We’ll also examine the first seven days of OpenAI’s "12 Days of OpenAI," where groundbreaking announcements are redefining how AI can transform workflows.

Let’s dive in!

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The Balanced View: Is AI Policy Just Bureaucracy—or the Key to Success?

Nobody likes policies. When you're excited about AI's potential to transform your business, the last thing you want to hear is "Let's write a policy first." I get it. As a CFO who's helped companies navigate AI adoption, I've seen firsthand how tempting it is to dive right in.

By jumping into AI without a framework, you might make quick progress at first, but things will eventually fail.

Yes, in some cases—like automating simple invoice processing—you might get away with a quick implementation. But as AI becomes the backbone of your operations, this shortcut can lead you down a risky path.

Real-World Examples of AI Implementation Failures Due to Lack of Strategy and Training

1. Zillow’s iBuying Model

   • What Happened: Zillow’s real estate pricing algorithm, part of its "iBuyer" program, was designed to make quick, data-driven decisions about purchasing homes. However, the AI failed to account for unpredictable market shifts and overpaid for properties. This mistake cost Zillow over $500 million and forced the company to shut down the program entirely.

   • Key Lesson: Without a policy to ensure human oversight and manage market risks, AI implementation can result in massive financial losses and reputational damage.

2. Amazon’s AI Hiring Tool

   • What Happened: Amazon deployed an AI-driven hiring tool that exhibited bias against female applicants due to historical bias in the training data.

   • Key Lesson: A lack of ethical guidelines and regular audits allowed biased outputs to persist, damaging trust in AI within the organization.

3. Chevrolet’s $1 Tahoe

   • What Happened: A Chevrolet dealer’s chatbot gave away a 2024 Chevy Tahoe for $1 and claimed that its action was legally binding due to a misconfigured AI system. Although no lawsuits followed, the bot was quickly taken down to prevent further errors.

   • Key Lesson: Rushing to implement AI without proper safeguards and human oversight can lead to costly public relations disasters and undermine trust in automation.

AI, when implemented without a clear strategy and adequate training, can lead to serious consequences, including financial losses, legal liabilities, and reputational damage. 

Key Components of an Effective AI Policy

1. Accepted Use Cases

   • Clearly define where and how AI can be applied to prevent misuse.

   • Example: Automate data reconciliation but require human oversight for sensitive customer interactions.

2. Mandatory Checkpoints

   • Establish review stages to ensure human oversight in high-stakes scenarios.

   • Example: Require manual validation for financial forecasting or real estate appraisals.

3. Data Governance

   • Implement standards for data quality, bias mitigation, and compliance with privacy laws.

   • Example: Use diverse and representative datasets to avoid biased or inaccurate outcomes.

4. Ethical Guidelines

   • Set parameters for ethical AI use, including bias audits and fairness assessments.

   • Example: Regularly review hiring algorithms for potential discrimination.

5. Training Programs

   • Equip employees with the knowledge and skills to work effectively with AI tools.

   • Example: Train staff on interpreting AI outputs and understanding system limitations.

6. Accountability and Transparency

   • Define roles and responsibilities for monitoring, auditing, and improving AI systems.

   • Example: Assign a dedicated AI oversight team to ensure compliance with policies.

While skipping policy and training might seem faster, it’s a short-term mindset with long-term consequences. Without a strategy, even the best AI tools can fail spectacularly.

Organizations can harness AI's transformative power responsibly and effectively by starting with a clear framework and investing in training. Skipping this step doesn’t eliminate inefficiency—it invites disaster.

To help you get started, I’ve gathered AI Policy Templates from trusted sources that you can adapt to your organization’s needs:

1. TrustCloud's Trust Community AI Usage Policy Template: A framework for ethical and responsible AI use.

2. NFPS.AI’s AI Policy Template: Tailored for nonprofits and not-for-profit organizations.

3. Microsoft Responsible AI Standard: Guidelines for responsibly implementing AI across industries.

Need help crafting AI policy?

This is part of my service offering, designed to save you time, reduce risks, and accelerate your AI implementation journey. Whether you need guidance or a full framework, I’m here to help.

Book a discovery call with me to get started!

News of the Week: The 12 Days of OpenAI – A Recap of Days 1 to 7

OpenAI’s “12 Days of OpenAI” is delivering exciting updates, transforming how AI integrates into our personal and professional lives. Here’s a day-by-day breakdown of the announcements so far, highlighting innovations that finance professionals can leverage:

Day 1: ChatGPT Pro and the o1 Model

• What’s New: The launch of ChatGPT Pro, a $200/month subscription tier, includes unlimited access to OpenAI’s most advanced o1 reasoning model.

• Why It Matters: The o1 model enhances reasoning and reliability, enabling better financial forecasting, risk modeling, and complex analysis for professionals looking to take AI-assisted decision-making to the next level.

Day 2: Reinforcement Fine-Tuning Research Program

• What’s New: OpenAI expanded its Reinforcement Fine-Tuning Research Program, inviting researchers, universities, and enterprises to apply.

• Why It Matters: This program encourages the development of specialized AI tools for finance, such as fraud detection algorithms and tailored predictive models, offering transformative opportunities for those working on complex tasks.

Day 3: Sora – AI Video Generation

• What’s New: Sora, OpenAI’s video generation model, is moving out of research preview, allowing users to create, enhance, and remix video content directly from text inputs.

• Why It Matters: While Sora’s applications may not seem directly relevant to finance professionals—for instance, it’s hard to imagine generating videos for management presentations—this technology is undeniably fun to experiment with. Who knows? It might find its way into creative use cases like crafting engaging client communications or visualizing complex market trends in a whole new way. 

Day 4: Canvas – Collaborative Writing and Coding

• What’s New: Canvas, OpenAI’s side-by-side interface for writing and coding, received a spotlight. It facilitates seamless collaboration between users and ChatGPT.

• Why It Matters: Canvas redefines how users interact with AI by offering a dynamic, interactive workspace, unlike the traditional linear chat mode. Instead of back-and-forth prompts, Canvas allows you to view and edit responses side by side with ChatGPT, making it ideal for tasks that require structure and iteration. Its real-time Python execution capability sets it apart, enabling users to test scenarios and analyze data directly within the interface. Canvas’s unique layout fosters collaboration, making it easy to refine drafts, iterate on ideas, and solve problems more efficiently than ever before.

I’ve been using Canvas in trial mode for a while now, and I can confidently say it’s one of my favorite ways to interact with an LLM. The organized, intuitive layout transforms the way I approach collaborative work, especially when managing multiple tasks or projects. If you haven’t tried it yet, I highly recommend giving it a shot.

Day 5: ChatGPT in Apple Intelligence

• What’s New: ChatGPT is now integrated into Apple Intelligence, making it accessible on iPhones and iPads.

• Why It Matters: Mobile integration allows finance professionals to analyze data, create models, or draft reports on the go, enhancing productivity and flexibility in decision-making.

Day 6: Advanced Voice and Santa Mode

• What’s New: OpenAI unveiled advanced voice capabilities, showcasing smoother and more natural voice interactions. For a playful twist, Santa Mode was also introduced, adding a touch of holiday fun to conversations.

• Why It Matters: Advanced voice capabilities have the potential to streamline tasks like dictating financial reports or setting up verbal queries for quick market updates, ideal for busy workflows. While I’m still adapting to using voice in my daily tasks, this feature has significant potential for making interactions faster and more intuitive.

Interestingly, while I’m still finding my rhythm with voice interactions, my teens have already mastered the feature and are having lengthy, casual conversations with ChatGPT. If you’re hesitant to try it, take a cue from them—it’s surprising how natural this technology can feel once you get used to it!

Day 7: Projects in ChatGPT

• What’s New: OpenAI introduced “Projects,” a feature that lets users create structured, collaborative workspaces within ChatGPT.

• Why It Matters: Projects eliminate the frustration of managing scattered chat histories by centralizing related tasks into one organized workspace. This makes it easier to handle complex workflows like budgeting, forecasting, or client reporting.

Personally, this is the most exciting feature I’ve seen so far. I recently got access to Projects and plan to reorganize my ChatGPT workspace over the next week. As someone working with multiple clients and juggling various projects, this feature is a lifesaver—it helps me keep everything structured, accessible, and easy to manage. If you’re dealing with similar challenges, this tool could transform how you approach your workflows.

What’s Next?

The first seven days of announcements have been a mix—some features were genuinely exciting and packed with potential for finance professionals, while others felt a bit more niche or experimental. That said, it’s been fascinating to see the direction OpenAI is taking with these innovations. With five more days still to come, I’m eager to see what’s next and how these tools might continue to reshape the way we work with AI. Stay tuned!

Closing Thoughts

I often talk about how AI is moving at a tremendous speed, but in the past few weeks, that speed has gone into overdrive. With OpenAI’s constant announcements and updates from other companies like Google, it’s been a challenge to keep up, let alone process, how these innovations could fit into our workflows.

If you’re feeling the same, you’re not alone. The pace of change is thrilling, but it also reminds me how important it is to step back, prioritize, and focus on what truly matters for our teams and goals. Not every shiny new feature will be the right fit, and that’s okay. What’s important is staying grounded, strategic, and open to learning!

Until next Tuesday, stay balanced and stay intentional!

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Until next Tuesday, keep balancing!

Anna Tiomina 
AI-Powered CFO