Artificial Intelligence (AI) is transforming CRM across the Life Sciences industry, creating new opportunities to improve customer engagement, automate workflows and support better decision-making. At the same time, organisations are recognising that the success of AI depends on the quality of the data behind it. As a result, data quality is no longer viewed as an operational responsibility, but as a strategic business priority.
From operational responsibility to strategic capability
For many years, data quality was considered the responsibility of IT teams. The focus was on maintaining customer records, eliminating duplicate data and ensuring CRM systems functioned correctly. While these activities remain essential, the role of data has expanded significantly. Today, CRM platforms support customer engagement, commercial excellence and omnichannel execution, making high-quality data a critical business asset rather than simply an operational requirement.
Customer data now supports every stage of the commercial journey. Sales representatives rely on it to prepare for healthcare professional interactions, commercial teams use it to identify trends and opportunities, and AI-powered applications depend on it to generate reliable recommendations. As CRM continues to evolve, data is no longer supporting a single business process, it underpins the entire commercial ecosystem.
This shift is reflected across the industry. Salesforce, for example, positions trusted and unified data as the foundation for enterprise AI, emphasising that organisations need accurate, connected and well-governed data to generate reliable insights and scale AI responsibly. As a result, responsibility for data quality has naturally extended beyond IT, becoming a shared responsibility across business functions, from commercial teams and CRM administrators to data stewards and business leaders.
Why AI demands better data
Poor data quality has always created business challenges. Incomplete customer profiles, duplicate records and inconsistent data standards have long affected reporting, customer segmentation and operational efficiency. The difference today is that AI amplifies these challenges. AI systems rely on existing data to generate recommendations, automate processes and support decision-making. When that data is inaccurate, incomplete or inconsistent, the quality of AI-generated outputs inevitably declines, reducing trust in both the technology and the insights it provides.
This growing dependency on trusted data is increasingly recognised across the Life Sciences industry. With the introduction of Data Cloud, Veeva describes trusted and standardised data as “the data foundation for AI”, highlighting the importance of connected, reliable data to support AI at scale. The same principle is reflected in Bayer’s AI journey. Speaking about the company’s approach to AI adoption, Hari Krishna Iyer, Business Capability Lead at Bayer AG, explained:
Data is the backbone for AI. We started this journey to get our basics right, which meant building a foundation of trusted, high-quality data, long before we moved to scale our AI strategy.
His observation reinforces an important lesson. Organisations that successfully scale AI rarely begin with the technology itself. They begin by building confidence in the data that powers it.
Building a trusted data foundation
Building high-quality data is rarely the result of a single technology implementation. As organisations expand their digital ecosystems, customer information is continuously created, updated and shared across multiple platforms and business functions. Without clear ownership, consistent governance and standardised processes, maintaining reliable data becomes increasingly difficult.
Organisations that consistently realise value from CRM recognise that data quality is a business capability rather than a technical task. They establish clear ownership of business-critical data, define governance frameworks and encourage user adoption so that maintaining accurate data becomes part of everyday business operations rather than an administrative exercise.
Technology plays an important role by automating validation, identifying inconsistencies and enriching customer information. However, sustainable data quality is ultimately created through the combination of people, processes and technology working together. Technology can support data quality, but it cannot replace the organisational discipline required to maintain it over time.
Turning data into business value
Investing in data quality delivers value far beyond maintaining accurate CRM records. Trusted data enables organisations to make better decisions, strengthen customer engagement and increase confidence in AI-driven insights. It also improves collaboration across business functions by ensuring teams work from the same reliable information, creating a stronger foundation for commercial excellence.
As AI continues to evolve, this foundation will become increasingly important. Organisations that invest in trusted data today are better positioned to scale AI, adapt to changing business needs and realise greater value from their CRM investments. Rather than viewing data quality as a technical objective, leading organisations increasingly recognise it as a strategic capability that supports long-term business growth.
For organisations investing in the future of CRM, data quality is no longer simply about maintaining accurate information. It has become a business priority that will continue to shape the success of digital transformation initiatives in the years ahead.
References
Salesforce. (2024). Einstein 1 Platform: Trusted AI Starts with Trusted Data.
Veeva Systems. Veeva Data Cloud – The Data Foundation for AI.
Veeva Systems. Getting the Basics Right: Bayer AG’s Data Foundation for AI.
