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Santiago Gonzalez Perez

Preparing Your Sage CRM Data for AI: How I Use Microsoft Copilot to Get Better Results

3 minute read time.

Artificial Intelligence is rapidly becoming one of the most talked-about technologies in CRM. Every vendor is promising smarter insights, better automation, and faster decision-making.

But after spending considerable time exploring AI capabilities, one thing has become very clear:

AI is only as good as the data you give it. 

As a Sage CRM Support Consultant, I regularly work with customer data, support cases, user requirements, survey feedback, and CRM databases. I've found that Microsoft Copilot can be incredibly valuable when preparing data for AI initiatives, but it doesn't replace the need for clean, structured, and reliable CRM information.

In fact, some of the best AI outcomes I've achieved came from focusing on data quality before focusing on AI itself.

Why Data Quality Matters More Than AI Features

Many organisations jump straight to asking:

"What can AI do for our CRM?"

A better question is:

"Is our CRM data ready for AI?"

AI can summarise records, identify trends, suggest actions, and uncover insights that would take hours to find manually.

However, if your CRM contains:

  • Duplicate records
  • Outdated contact information
  • Incomplete customer profiles
  • Inconsistent data entry practices
  • Missing activity history

then AI may generate misleading recommendations or miss opportunities entirely.

Before implementing any AI solution, your CRM should be treated as the foundation.

How I Use Copilot to Assess CRM Data Quality

One of the first things I use Copilot for is identifying patterns that would otherwise be difficult to detect manually.

For example, when reviewing CRM exports or survey datasets, I often ask Copilot questions such as:

  • What duplicate patterns exist in this data?
  • Which fields have the highest percentage of missing information?
  • Are there inconsistencies in customer naming conventions?
  • Which records appear incomplete?
  • What trends can you identify across this dataset?

Instead of manually reviewing thousands of rows, Copilot helps surface potential issues quickly so I can focus on resolving them.

Importantly, I still validate the findings myself. AI accelerates the review process, but human oversight remains essential.

Turning CRM Data Into Actionable Customer Insights

One area where Copilot has been particularly valuable is customer feedback analysis.

Working with NPS and survey data, I often need to identify:

  • Common customer frustrations
  • Recurring support themes
  • Product enhancement requests
  • Churn risk indicators
  • Positive experiences worth replicating

Traditionally, reviewing hundreds of survey comments could take several hours.

With Copilot, I can quickly group feedback into themes, highlight recurring patterns, and identify the underlying business drivers being mentioned by customers.

This allows me to spend less time categorising information and more time understanding what actions should be taken.

Finding Hidden Gaps in Customer Information

AI can often reveal problems that users don't realise exist.

For example, when analysing CRM records with Copilot, I've discovered:

  • Accounts with no recent activity history
  • Key customer contacts missing from records
  • Opportunities lacking next actions
  • Cases with insufficient documentation
  • Customer segments with inconsistent data capture

None of these are necessarily AI problems.

They're CRM discipline problems.

However, AI helps expose them much faster than traditional reporting methods.

AI Doesn't Replace Good CRM Practices

One of the biggest misconceptions I encounter is that AI will somehow "fix" poor CRM data.

It won't.

If users aren't maintaining customer records, documenting interactions, or following established processes, AI simply has less information to work with.

The organisations that gain the most value from AI are often the same organisations that already have strong CRM fundamentals:

  • Consistent data entry standards
  • Clearly defined business processes
  • High user adoption
  • Reliable customer information
  • Active data governance

In many ways, AI rewards good CRM habits.

Responsible AI Starts With Human Validation

Another important lesson I've learned is that AI-generated insights should never be accepted without review.

Copilot can identify trends, summarise information, and make recommendations, but accountability still sits with the user.

Before acting on AI-generated outputs, I always recommend:

  • Reviewing the source data
  • Validating recommendations
  • Checking business context
  • Confirming customer-specific nuances
  • Applying professional judgement

AI is an assistant, not a decision-maker.

The final decision should always remain with a person who understands the customer, the business, and the broader context.

My Advice for Any Sage CRM User Looking at AI

Before investing significant time exploring AI capabilities, spend some time assessing the quality of your CRM data.

Ask yourself:

  • Are customer records complete?
  • Is duplicate information under control?
  • Are users consistently capturing activities?
  • Can we trust the information in our CRM?
  • Would AI have enough quality data to generate meaningful insights?

If the answer to those questions is "not yet", then your greatest AI opportunity may actually be improving data quality first.

The exciting reality is that AI tools such as Microsoft Copilot can help you do exactly that.

Let's Discuss

Many businesses are currently exploring how AI can improve CRM adoption, customer engagement, and team productivity.

I'm interested to hear from the Sage CRM community:

Has AI helped you improve the quality of your CRM data, or has it highlighted data issues you didn't know existed?

What has been your biggest challenge when preparing CRM data for AI?

Let's share experiences and learn from each other. The success of AI in CRM doesn't start with AI itself. It starts with the quality of the data behind it.

  • Good post, and the core message on data quality holds up well, that part I don't disagree with at all.

    Where I'd push back a little is on the idea that AI in Sage CRM has been waiting on the Enriched Semantic Schema landing in 2026 R2. We've been running local LLM integrations against Sage CRM for quite a while now, and in practice, most of what that semantic schema is meant to solve, giving a model a proper understanding of entities, relationships and the underlying SQL structure, has already been achievable with the API and metadata Sage CRM exposes today.

    Our approach has been to run a self hosted LLM stack and feed it context on the CRM's entity model and SQL schema directly, rather than wait for a vendor supplied semantic layer. That grounding is what lets the model reason about joins, field meanings and relationships, instead of just pattern matching on an exported spreadsheet. Once a model has that context, it can do a lot more than flag data quality issues. It can answer questions in the context of a live record, summarise a case history against the real schema rather than an export, and surface things like missing next actions or stale opportunities in place, inside the CRM, rather than as a separate Copilot exercise afterwards.

    That's really where I think the AI proposition gap sits today. The Copilot workflow in the post is genuinely useful, and the emphasis on human validation is right, but it's a manual, out of band process: export or query the data, review it in Copilot, bring the findings back by hand. What Sage CRM developers like us have built ourselves is the always on version of that, embedded directly in the CRM, working from live data rather than an export, and running on infrastructure the customer controls rather than sending case and customer data out to a third party model.

    That last point matters more than it might sound for a fair few of our Sage CRM clients. Some of them hold data that simply cannot leave their own network under any circumstances, and others are less about hard restrictions and more about wanting data sovereignty and control over their own costs, rather than being tied into an ongoing third party subscription just to get any AI benefit out of their CRM at all.

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