AI and data visualization

1. What does the FGCU article say about AI’s role in data visualization?

AI is transforming data visualization by enhancing how data is processed, analyzed, and presented.

2. What takes most of the time when creating data visualizations?

The data analysis process—not the actual making of visuals.

3. How does AI help with data preparation?

AI scans large datasets to clean data and find patterns, saving enormous human resources.

4. Can AI replace humans in software like Power BI or Tableau?

No. AI can suggest visualization types and generate some visuals, but it does not replace human creation or interpretation.

5. What prompt did the group give Microsoft Copilot?

They gave Copilot the same project background and asked it to discuss:
– How AI affects the topic now
– Near-term changes
– Benefits and concerns
– Preparation for these changes
– Nuances, examples, perspectives
– Lessons learned from using AI.

6. What guiding questions were suggested for AI/data visualization research?

Examples:
– How is AI integrated?
– What is changing because of AI?
– Benefits/challenges (general + accounting)?
– Ethical/professional issues?
– Risks and how to manage them?
– Needed skills?

7. What research suggestions were given to the group?

Each member should research independently, consider all stakeholders, use AI thoughtfully, fact-check, think critically, and experiment with different tools and prompts.

8. What are examples of natural language querying tools?

OpenAI’s GPT, AI chatbots, text summarization, speech recognition, legal & healthcare document processing.

9. What are examples of predictive forecasting use cases?

Predicting customer behavior, detecting fraud/security risks, supply chain optimization.

10. How is AI currently used in accounting firms?

Audit document review (Deloitte), unifying tech stack (EY), tax research, and accounting/bookkeeping automation.

11. How will AI change data prep?

Faster analysis and less manual prep—AI cleans data instantly, giving more time for interpretation.

12. What does “always-on dashboards” mean?

Dashboards that auto-update and give alerts, shifting teams from monthly/quarterly reporting to continuous oversight.

13. What features are expected to grow?

More in-depth explainable AI, personalized dashboards for any industry, and immersive visualizations.

14. How does AI improve efficiency?

Automates reconciliations, invoice processing, and categorization.

15. How does AI enhance fraud detection?

It identifies irregular transactions humans may overlook, improving accuracy and audit assurance.

16. What is a major concern with relying on AI output?

Over-reliance can lead to incorrect assumptions—human oversight is needed to validate results.

17. How does AI impact the accounting workforce?

Routine tasks decline; accountants must shift toward analytics, visualization, and model supervision.

18. What is the risk of poor explainability, and how is it managed?

If AI logic isn’t understood, it can cause errors.

Management: ensure AI’s logic for processing/storing/protecting data is understood.

19. What is automation bias, and how is it mitigated?

It’s blindly trusting AI outputs.

Mitigation: regular AI audits + human oversight.

20. How does historical data cause biased visuals? How to address this?

AI models trained on old data produce biased or unfair predictions.

Mitigation: back up data, update algorithms, monitor performance.

21. What is data storytelling?

Explaining results clearly and connecting data to goals and opportunities.

22. What are visualization skills?

Choosing the right charts, labeling clearly, and using simple visuals that support the data.

23. What is governance awareness?

Knowing what data can be used in AI tools, following permissions, and documenting data sources and transformations.

24. What is explainability as a skill?

Recognizing anomalies, trends, and forecasting patterns; explaining limitations; translating insights into plain English.

25. What is the overall impact of AI on visual analytics?

AI scans for patterns far faster than humans, creating automated and streamlined analysis and supporting generative visuals.

26. Is AI taking jobs in this field?

Not exactly—it’s shifting work away from data cleaning and toward interpreting results and applying insights to business goals.