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This article provides an in-depth analysis of how Artificial Intelligence (AI), particularly Microsoft Copilot integrated with Dynamics 365 Sales, enhances Customer Relationship Management (CRM) systems. A key focus is the relationship between data quality and AI performance, emphasizing that even advanced AI tools are only as good as the data they work with.
We will be discussing AI and CRM, and provide examples for Microsoft Copilot and its integration with Dynamics 365 Sales but what applies to Copilot in the Microsoft world also applies to any AI included in a CRM system.
AI improves decision-making and user efficiency across all CRM systems by analyzing data and offering insights, such as lead scoring and opportunity analysis. However, these benefits are compromised if the underlying CRM data contains duplicates, inaccuracies, or missing information.
Good data quality is so important and even more so now with the explosion of AI, and the industry experts agree:
“Data is useful, but high quality, well understood, auditable data is priceless”
Ted Friedman, Gartner.
Microsoft Copilot offers quick insights into sales activities like pipeline status and leads directly from a "Homepage," launching in February 2025. The ability to use natural language queries to extract data from the CRM adds to its utility.
If your CRM data is incomplete, inaccurate, or filled with duplicates, your AI snapshot is unreliable and will not contain the meaningful insights you are looking for.
AI-powered tools such as Copilot help sales teams prioritize leads and opportunities in Dynamics 365 based on various metrics like revenue potential, purchase intent, and timeline.
However, bad data can make these powerful insights incorrect and misleading, leading to distorted pipeline summaries, inaccurate revenue forecasts, and skewed performance metrics.
Copilot uses predictive scoring for both Dynamics 365 leads and opportunities to highlight the ones most likely to close.
But duplicates or wrong information can distort these scores. Duplicates in particular can distort the predictive scoring because critical information, such as notes, activities, and engagement data, is scattered across multiple records instead of being centralized on one true Opportunity or Lead. Without accurate, clean data, you cannot trust the AI's predictive scoring, which could result in missed sales, wasted resources, and reduced overall effectiveness.
Copilot can gauge customer sentiment from activities and conversations, helping sales teams understand the emotional tone of interactions.
However, as with other AI-driven tools, bad data can severely impact sentiment analysis. If your CRM contains duplicate records for accounts, opportunities, or cases, the AI could magnify certain sentiments, distorting the results. Inaccurate analysis could prompt incorrect decisions, such as overestimating customer dissatisfaction or missing key feedback, ultimately leading to misguided actions regarding your products, services, or staff.
Copilot helps users maintain an updated view of Dynamics 365 customer accounts, but poor data quality—such as duplicates or missing account type—can skew this analysis and obscure the real customer status.
For example, if there are duplicate Accounts in the CRM, it's difficult to know which record accurately reflects the current status of the customer. This scattered information distorts the customer view, preventing you from having a Single Customer View (SCV), which is critical for proper analysis and decision-making.
Copilot’s deeper integration with tools like Outlook and Teams enables seamless data access during meetings and email threads. However, inaccurate data can lead to embarrassing mistakes, such as creating emails with the wrong customer information, damageing customer loyalty and trust.
And, linking an email to the wrong record, as could be the case when there are duplicates, is also frustrating and ultimately pollutes your data even more and further restricts you from having that Single Customer View. So linking the email to a duplicate means it's now spread across multiple records, making the AI even more unreliable.
When you’re in a meeting with a customer and the information you have is wrong—such as outdated notes, incorrect budget figures, or irrelevant status updates—it reflects poorly on you and your organization. It suggests that you're either unprepared or indifferent, which is not the kind of impression anyone wants to leave in a professional setting.
High-quality data is essential to ensure that AI tools like Copilot deliver accurate insights. Common data quality issues—such as duplicates, incomplete information, and outdated records—can severely impact the performance and efficiency of AI-driven CRM systems and business decisions.
To maintain data quality, tools like Paribus 365™ help in cleansing duplicates and preventing new ones. Address management and data enrichment tools from vendors like Smarty, SumSub, Zoom Info, and Clearbit can further improve CRM data quality by auto-filling addresses and enriching customer profiles with relevant organizational or demographic details.
Finally, implementing a data governance framework is crucial for ensuring long-term data quality. This framework should include well-defined guidelines for data entry, merging, and reporting issues, ensuring that AI capabilities are maximized through clean, reliable data.
AI in CRM systems can revolutionize decision-making and efficiency, but it is only effective when based on clean, accurate data. Businesses must invest in tools and processes to maintain high data quality to unlock the full potential of AI, like Microsoft Copilot, and avoid errors that can harm customer relationships and business outcomes.
For organizations looking to enhance their data management, tools like Paribus 365™ are recommended for removing duplicates and keeping data accurate.
For further details on this topic, tips, and recommendations, with detailed scenarios for each capability above, watch our 30-minute recorded webinar: Artificial Intelligence (AI) and Dynamics 365 is Improved with Clean Data