✨ Download this article as a PDF — perfect for offline reading or sharing with friends on social media!
Why AI-Driven Excel Data Analysis Can’t Replace Power BI or Looker Studio — Yet
By Kannan M | radhaconsultancy.blogspot.com
Tagline: AI makes analysis faster. BI makes it trustworthy. Smart corporations need both.
Introduction
The promise of AI dashboards is hard to ignore: upload a structured Excel file and instantly get charts, KPIs, and insights. For busy executives, this seems ideal—no pivot tables, no Power BI setup, no Looker Studio configuration.
But can these AI-generated dashboards be trusted for business decision-making?
To answer this, we ran a real-world case study using blog analytics data:
This study highlights where AI dashboards add value, where they fall short, and what corporate teams should know before relying on them.
How BI Tools vs AI Dashboards Handle Data
Traditional BI platforms such as Power BI or Looker Studio process every row of the dataset. They aggregate fields like views, categories, and dates with strict formulas, ensuring deterministic results. Calculated fields allow isolation of incremental metrics, and filters always recalculate data reliably.
AI dashboards, by contrast, interpret patterns and generate summaries instead of strict row-by-row aggregation. They are fast to generate, visually appealing, and sometimes surface insights that might be overlooked in manual analysis. The trade-off is accuracy: AI may skip rows, misgroup categories, or approximate totals, and interactive filters often adjust only the display without recalculating the underlying dataset.
To generate the AI dashboard, the following prompt (shortened version) was used in Perplexity Labs: “Analyze my blog Excel file and generate an interactive dashboard with key metrics, trends, top posts, and language/category insights. Include filters and visual charts for quick business interpretation.”
In short: BI guarantees precision; AI prioritizes speed and convenience.
Key Discrepancies Observed
A few examples from our case study illustrate the differences:
What AI Did Well
Instant dashboard creation without setup
Correct identification of top-performing post
Yearly patterns visually accurate
Easy filtering by category or language
Where AI Falls Short
Totals and key metrics often inaccurate
Missed incremental or new post views
Category breakdowns misaligned with raw pivots
Filters may not trigger full recalculation
Comparison with Looker Studio
Looker Studio delivers accurate, reliable dashboards directly connected to source data.
Perplexity Labs AI dashboards are fast and visually engaging, but prone to numerical errors.
Causes of AI Discrepancies
AI dashboards summarize data using pattern interpretation rather than strict aggregation, which can lead to small but meaningful errors:
Sampling or skipped rows: AI may process a subset for speed, omitting some entries.
Category misgrouping: Fields like “Finance” vs “Fintech” may be merged or partially excluded.
Missed new entries: Newly added data may not appear if AI processed the file earlier.
Filter limitations: Filters may appear interactive but only change the display, not the underlying calculations.
These differences do not render AI dashboards useless—they are often directionally correct, but numbers should be treated as indicative, not precise.
Recommendations for Corporate Users & Trainers
Always cross-check key metrics in Excel, Power BI, or Looker Studio before acting on AI outputs.
Use AI dashboards for trend spotting, brainstorming, or exploratory analysis, not critical KPIs.
Investigate discrepancies: is it a model limitation, prompt issue, or data formatting problem?
Document business logic for key metrics, and prompt the AI with explicit rules to improve reliability.
Treat BI tools as the source of truth; AI dashboards complement them during exploration.
Train teams on strengths and limitations: “AI can save time and generate insights, but it cannot replace precision.”
Conclusion
AI dashboards like Perplexity Labs excel at speed, visual insights, and idea generation, but they are not yet a replacement for traditional BI tools when accuracy matters. Always validate results, cross-check totals, and understand where AI may miscount, misgroup, or overlook data.
By combining AI’s exploratory power with BI’s precision, corporate teams can make smarter, faster, and more reliable decisions.
Pro Tip: Share this with colleagues who rely on data—awareness of AI’s limitations is just as important as its capabilities.
Contact and resources
Services: AI Visuals - AI Research - Workflow Training
Book: AI for the Rest of Us — by Kannan M
More Researched Perplexity pages on AI and More
✨ Download this article as a PDF — perfect for offline reading or sharing with friends on social media!
Connect with Kannan M
LinkedIn, Twitter, Instagram, and Facebook for more insights on AI, business, and the fascinating intersection of technology and human wisdom. Follow my blog for regular updates on practical AI applications and the occasional three-legged rabbit story.
For "Unbiased Quality Advice" Message me via blog
▶️ YouTube: Subscribe to our channel
Blog - https://radhaconsultancy.blogspot.com/
