Choose Claude for the Microsoft 365 task in front of you
When you’re working through invoice data, refining a proposal, or building an Excel tracker, the right AI model can help you get to a useful first draft faster. If your Microsoft 365 Copilot setup offers Anthropic’s Claude models, you can select one for those tasks instead of relying only on OpenAI’s GPT models.
The goal isn’t to crown one model the winner. It’s to choose a model that fits the work, give it clear instructions, and check the result before you use it. Below, you’ll see how to try Claude for invoice analysis, proposal review, Excel tracker creation, and research—and what to verify along the way.
Check your access before you share work files
Claude availability can depend on your Microsoft 365 license, administrator settings, region, and feature rollout. Model names and interface labels can also change.
In the setup described here, Claude Sonnet appeared in the model selector in Copilot Chat’s Work tab for users with a paid Microsoft 365 Copilot license. It wasn’t available in the Web tab in that setup.
Before attaching work documents:
- Check which models your organization permits.
- Review the applicable data-processing and compliance terms.
- Confirm that the files are appropriate to use with the selected model.
The rollout described also included restrictions affecting UK and EU customers, where Claude experiences weren’t enabled by default because of data-processing arrangements. Check your organization’s current settings and Microsoft’s documentation. Don’t assume every model has identical safeguards.
Find collection risks in your invoice spreadsheet
An invoice workbook can show more than how much customers owe. You can ask Claude to summarize payment patterns, overdue balances, and possible concentrations of collection risk.
In Copilot Chat, select Claude if it’s available, then attach your workbook using the file picker. Depending on your setup, you may be able to upload a file or select content from OneDrive, SharePoint, or Teams.
Try this prompt:
Analyze the attached invoice workbook. Summarize invoice counts and values by payment status, identify the customers with the largest overdue balances, and compare collection patterns across service categories. Flag unusual patterns and distinguish facts from possible explanations. Explain what additional information you need before judging our cash-flow health.
In an illustrative analysis using 100 rows of fictional invoice data, Claude produced a headline summary, a status table, customer-level overdue balances, and category comparisons.
The output reported a 44% collection figure and suggested that a healthy business should collect 70–80% of invoice value within terms. Treat that benchmark as something to verify, not a universal rule. Payment terms, invoice age, billing cycles, and industry practices can all change the interpretation.
The analysis also found a consulting category with a 30% collection rate and no overdue invoices. That combination is worth investigating, but it doesn’t automatically signal poor performance. The invoices might not be due yet.
Do this next: Ask Claude to separate what the spreadsheet shows from what the figures might mean. Then check the underlying rows and relevant payment terms yourself.
Compare your proposal with a long strategy document
When you review a proposal, matching keywords isn’t the same as demonstrating a genuine fit with your organization’s priorities. Claude can help you look for supported alignment, weak evidence, and potential conflicts.
One example compared a 100-page London Growth Plan PDF with a fictional Project Green Space proposal. The task was to identify alignment, tensions, opportunities, and risks.
Adapt this prompt to your documents:
Compare the attached proposal with the attached strategy document. Identify where the proposal directly supports its priorities, where the evidence is weak, and where there may be conflicts. For each finding, identify the relevant source passage or section. Finish with the strongest opportunity, the biggest risk, and five recommended revisions.
In that example, Claude identified gaps around economic contribution, specific funding mechanisms, and smart technology or data integration. It also flagged the importance of identifying a site already designated for open space.
Use those findings to guide a focused follow-up:
Draft an alignment section using only claims supported by these documents. Connect the proposal to the relevant priorities, and mark missing evidence or unconfirmed commitments in brackets.
Then review the draft and incorporate the material into your proposal in Word. Polished wording doesn’t turn an aspiration into an established fact. Check each claim against the source documents.
Build an Excel tracker that works, not just one that looks tidy
Analyzing an existing spreadsheet and creating a tracker are different jobs. For the latter, specify the fields you need and the behavior you expect.
In the Excel experience described, the route was through the Copilot sidebar, then Tools → Edit with Copilot, followed by the model selector. Claude Opus 4.6 was available in that setup.
Try a prompt like this:
Build a phase-one project tracker for Project Green Space. Include workstream, task, owner, start date, due date, duration, percentage complete, priority, status, red/amber/green rating, dependencies, blockers, and notes. Use dropdowns for categorical fields. Add a summary dashboard showing overall progress, total tasks, red/amber/green counts, and the workstream with the most overdue items. Include a legend and freeze the header row. Mark sample tasks as illustrative.
The example took approximately 5–10 minutes and involved 23 reported steps. The resulting workbook included a dashboard, dropdowns, in-cell visualizations, frozen rows, and a legend.
A comparison using the same prompt with GPT-5.2 reported nine steps and produced a simpler tracker, including some status fields as plain text rather than dropdowns.
That’s one comparison—not proof that more steps mean better reasoning, or that Claude always builds better spreadsheets. Check the workbook itself:
- Do the dropdowns work?
- Are the dashboard formulas correct?
- Does overdue logic handle completed tasks?
- Do summaries update when you add rows?
Use Researcher to get oriented, then verify the details
Where Claude is available in Microsoft 365 Copilot’s Researcher agent, you can attach work files and request a project-grounded briefing.
Give the agent a clear scope, geography, timeframe, and intended audience. If it asks useful clarification questions, answer them instead of leaving every decision to the model.
In the comparison described, Claude produced a relatively high-level, bullet-led briefing. The GPT-based result took longer and included more extensive detail. Both outputs were useful for different purposes: one supported orientation and further writing, while the other came closer to a report-style draft.
Before sharing either result, verify citations, dates, and important claims against the original sources.
Pick the model that fits your result
Start with one recurring task: reviewing invoices, strengthening a proposal, building a tracker, or preparing a research briefing. Give Claude the relevant source material, define what a useful result should contain, and check the output against that definition.
If another model is available, compare both using the same prompt and files. The value isn’t just another model in a dropdown; it’s choosing a fit for your work and knowing how to check the result.
Ready to build practical AI skills for your work? Explore beginner-friendly learning on Tixu.ai.
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