Get more from your data with AI

You’re already collecting useful data. AI can help you analyse it, spot patterns, and uncover opportunities you can act on this quarter. Start small, pick one question that matters, and use the checklist below to keep things safe and effective. 

AI can help you plan and grow 

Most teams have data scattered across email, spreadsheets, job systems, online stores, and CRMs. It is hard to stitch together a clear picture, let alone act on it. AI changes that. With the right guardrails, you can use tools like Microsoft Copilot, Excel, Power BI, and Power Automate to: 

• Pull insights out of messy spreadsheets. 
• Spot trends earlier. 
• Forecast demand and cash flow. 
• Find where you are leaking time or money. 
• Turn insights into simple next steps.
   

What data can AI work with? 

• Sales and invoices. 
• Website, ads, and social metrics. 
• Customer enquiries and support tickets. 
• Job logs and timesheets. 
• Inventory and supplier data. 
• Appointment and service history. 

You don’t need perfect data to begin. You need a useful question, a tidy starting file or report, and clear rules about what you will not upload. 

Quick wins with Microsoft AI tools 

• Copilot in Excel: Ask natural questions like “Which five products grew the fastest in the last 90 days” or “Highlight customers whose spend dropped more than 30 percent.”  

• Power BI: Build a simple dashboard and use AI visuals to explain spikes and dips. Schedule a weekly view for your team.  

• Copilot for Microsoft 365: Summarise customer email threads, extract dates and amounts, or draft a follow-up that includes key figures from a spreadsheet.  

• Power Automate: Trigger alerts when a value crosses a threshold, for example when average response time exceeds 2 hours or when stock on a key item drops below 10.  

• Forms + SharePoint or OneDrive: Standardise how data is captured so your analysis is cleaner next month than it was last month.
   

Five practical scenarios to spark ideas 

1) Retail and ecommerce: find the money you are leaving on the table 

Goal: Increase repeat purchases.
Data: Orders, returns, email list, product categories.
AI prompts to try: 

• “Cluster customers by purchase frequency and average order value. Recommend an offer for each group.”  
• “Which products are commonly bought together in Q4. Suggest three bundle ideas.” 

What to do next: Create a segment of lapsed customers and schedule an email with a relevant bundle. Use Power Automate to alert you when lapsed customers return.
 

2) Trades and field services: book smarter, not longer 

Goal: Reduce idle time and travel.
Data: Job bookings, technician locations, timesheets.
AI prompts to try: 

• “Find routes or days with excessive travel time. Suggest schedule changes that keep travel under 45 minutes between jobs.”
• “Which job types exceed quoted time by more than 20 percent.”  

What to do next: Adjust booking rules and time estimates. Add a job-type checklist in Forms to capture the extras that cause blowouts.
 

3) Professional services: keep your pipeline healthy 

Goal: Forecast cash flow and resource load.
Data: Proposals, win rates, project timelines, billed hours.
AI prompts to try: 

• “Based on the last 6 months, forecast likely billings for the next 8 weeks by service line.”
• “Identify clients with decreasing engagement who previously spent over $X per quarter.” 

What to do next: Schedule weekly pipeline summaries in Power BI. Ask Copilot to draft a check-in email for at-risk clients with a helpful next step.
 

4) Clinics and wellbeing: reduce no-shows, increase continuity of care 

Goal: Improve attendance and follow-ups.
Data: Appointments, reminder logs, cancellation reasons.
AI prompts to try: 

• “Which appointment times have the highest no-show rate. Recommend a reminder plan that reduces this by 20 percent.”
• “Find patients overdue for follow-up by more than 6 weeks.” 

What to do next: Use Power Automate to send timely reminders. Test a confirmation message 48 hours before and a same-day SMS.
 

5) Not-for-profit: focus effort where it works 

Goal: Raise more with the same team.
Data: Donations, campaign responses, volunteer hours, event attendance.
AI prompts to try: 

• “Compare cost per dollar raised by channel. Recommend which two channels to prioritise this quarter.”
• “Identify volunteer shifts with the biggest impact per hour. 

What to do next: Reallocate budget to the top channels and build a simple impact dashboard for the board.
 

How to think about AI analysis: a simple checklist 

1) Start with a real question

Write one problem in plain English. Examples: 

• “Why is our average sale lower in January” 
• “Which services have the best margin after labour” 
• “Where are projects slipping against plan”  

2) Pick the smallest useful dataset

Export a CSV or Excel with only the columns you need. Add a short data dictionary at the top sheet that explains each column. 

3) Clean before you analyse 

• Remove duplicates and blank rows. 
• Standardise dates, product names, and customer names. 
• Convert currency and units into one format.  

4) Choose the right tool for the job 

• Questions and quick summaries: Copilot in Excel or Copilot for Microsoft 365. 
• Visual trends and sharing: Power BI. 
• Alerts and follow-ups: Power Automate. 
• Repeatable data capture: Forms feeding SharePoint or OneDrive.  

5) Ask clear questions

Use direct, testable prompts: 

“Show a month-over-month chart of revenue for the last 12 months, flagging months more than 15 percent below the 12-month average. Explain likely causes using available fields.” 

6) Sense-check the output 

• Does the insight make business sense? 
• Can you replicate it with a simple pivot or chart? 
• If you changed the date range, would the trend hold?

7) Turn insight into a next step

Every finding needs an owner, a due date, and a tiny experiment. For example: “Create a 2-email win-back sequence for lapsed buyers by Friday, review results in 14 days.” 

Prompts you can copy and paste 

• “From this Excel table, list the top 10 customers by growth rate over the last 90 days, and add one sentence on what they have in common.”
• “Explain the three biggest drivers of margin change since last quarter using the columns provided. Keep the explanation under 120 words.”
• “Group products into 3 to 5 clusters based on price, margin, and return rate. Name each cluster and suggest how we should market it.”
   

Data security: what not to paste into AI 

AI can be safe and compliant when used correctly. It becomes risky when you upload sensitive data to tools that are not covered by your company tenancy or your agreements. As a rule of thumb, do not put the following into general AI tools: 

• Personally identifiable information: full names combined with addresses, phone numbers, birth dates, driver’s licence or passport numbers.
• Financial data: credit card numbers, bank account details, tax file numbers.
• Health information: diagnoses, treatment notes, or anything that could identify a person’s health status.
• Secrets and credentials: passwords, API keys, internal access tokens, private URLs, security configs.
• Confidential contracts and legal disputes: anything covered by NDAs or attorney-client privilege.
• Student or child information: anything that identifies minors.
   

Safer ways to work with sensitive data 

• Use your Microsoft 365 tenant with Copilot so data stays within your organisation’s compliance boundary.
• Mask or aggregate data first. Replace names with IDs, summarise numbers by week or segment.
• Store working files in OneDrive or SharePoint with the right permissions.
• Keep an approved tools list and a simple policy that says which AI tools are allowed for which tasks.
• Log who exported data, why, and where it was analysed.
   

How to get started this week 

1. Pick one business question that would move the needle. 
2. Export a clean, minimal dataset. 
3. Ask 3 to 5 focused questions in Copilot or Power BI. 
4. Choose one action you can deliver in 14 days. 
5. Review the result and decide whether to automate, expand, or park it.
    

Key takeaway 

AI is most valuable when it helps you make one better decision at a time. Start with a question that matters, use the smallest dataset that answers it, and keep your security standards high. Do that, and you will find patterns and opportunities that you can actually use. 

FAQ about using AI to get more from your data 

Can AI work with messy spreadsheets

Yes, but you will get better results if you standardise dates, names, and categories first. Copilot in Excel can help profile and clean columns. 

Do I need Power BI to do this

Not always. For quick questions, Copilot in Excel works well. Use Power BI when you want repeatable dashboards and shared views. 

How accurate are AI insights

AI is good at surfacing patterns quickly, but you should verify important findings with a simple chart or pivot and a common-sense check. 

What if our data is in different systems

Start by exporting the minimum from each system into one sheet or use Power Query to connect them. Standardise column names for easier analysis. 

How do we keep this safe

Use approved, tenant-bound tools, avoid uploading sensitive data, mask where possible, and follow least-privilege access on SharePoint or OneDrive. 

If you’d like help setting up or getting more from Microsoft 365 – from Copilot to Power BI and everything in between – get in touch. We’ll help you connect your tools, clean your data, and start using AI confidently to uncover the insights that drive smarter decisions. 

Book a call.