Overview:

This session works through the most common problems professionals actually run into when using Claude inside Excel, and pairs each one with a specific, practical fix - not a general orientation to AI, and not a feature tour.

It opens with the errors and frustrations users report most often: imprecise formulas, inconsistent formatting on cleanup requests, generic outputs from vague prompts, and summary tables that miss important context. Each of these is traced back to a specific gap in how the request was structured, giving attendees a clear before-and-after view of what changes the outcome.

From there, the session introduces a consistent framework for structuring spreadsheet-specific requests - one that applies whether the task is formula construction, data cleanup, or report generation - so the fix isn't tied to one example but becomes a repeatable habit. Particular attention is given to verification: how to check a suggested formula or cleaned dataset before relying on it, and how to recognize when a result needs a second look rather than being pasted straight into a live file.

The session closes with a reusable prompt library addressing the exact problem areas covered earlier, along with a simple checklist attendees can use to catch these issues before they reach a shared document. The goal is for attendees to leave with fixes for the specific mistakes they're most likely already making.

Why you should Attend:
Every user who has tried Claude for a spreadsheet task and gotten an unreliable result has learned the wrong lesson from it - not "I need a better prompt," but "this doesn't work for Excel." That misdiagnosis is costly: it means giving up on a capability that, prompted correctly, could meaningfully cut the time spent on formula-building, data cleanup, and reporting.

The more serious risk sits with the users who don't give up, but also don't correct course - the ones who keep using vague prompts, keep getting inconsistent results, and start pasting AI-suggested formulas or summaries into shared spreadsheets without checking them first. That habit is how a single unnoticed formula error ends up in a report that reaches a client or a leadership review, at which point the cost is no longer about wasted time - it's about credibility.

There's also a widening gap between users who've quietly figured out what works through trial and error and users who haven't - a gap most teams have no visibility into until it shows up as inconsistent report quality across otherwise similar roles. As AI-assisted spreadsheet work becomes an expected baseline rather than a novelty, the users who never corrected their approach risk falling further behind, not because the tool failed them, but because no one showed them what a working prompt actually looks like.

This session addresses the actual problems users report, not hypothetical ones, and gives every attendee a direct fix for each.

Areas Covered in the Session:

• The most common problems users report when using Claude in Excel
• Why vague prompts produce unreliable formulas and generic outputs
• Fixing imprecise formula requests using a structured prompting approach
• Solving inconsistent formatting and data cleanup results
• Preventing summary tables and reports that miss key context
• Verifying AI-suggested formulas before relying on them
• Recognizing when a result needs a second look before use
• A reusable prompt library addressing these specific problem areas
• A checklist for catching these mistakes before they reach a shared document

• Financial Analysts?
• Operations Managers
• Business Analysts
• Administrative Professionals
• Project Coordinators
• Data Coordinators
• Team Leads