Winning commercial’s AI race with connected CRM and data

AI, particularly generative AI (GenAI), is rapidly transforming pharma. Its adoption in marketing and sales has more than doubled, with use cases ranging from intelligent content generation and next-best-action suggestions to healthcare professional (HCP) engagement optimisation. AI promises to deliver true customer-centricity, enabling insights that consider the full context of HCPs and their environment. Yet, as companies adopt these tools, a reality emerges – you can only go as far as your data allows.

Pharma’s vast data holds significant potential, but realising it is often hindered by poor data quality and siloed systems. Seemingly minor data quality issues – like conflicting HCP role names (‘prescriber’ vs ‘physician’) or managing excessive variations (eg, one customer reported 30,000+ ‘specialty’ values) – can prevent a unified customer view, impede effective cross-functional collaboration and introduce ‘noise’ that degrades AI insights, ultimately undermining a cohesive customer approach.

As the industry deploys AI at scale, the key challenge often lies not in refining the AI models but in harmonising and structuring the underlying data foundation. Consistency here is essential for enabling reliable analytics and AI initiatives. For instance, without a unique HCP identifier, AI might treat one HCP, even with the same name, as two separate individuals, skewing insights.

Even as Gen AI improves handling inconsistencies and unstructured information, it fundamentally requires reliable data foundations. This alignment is crucial for generating trustworthy intelligence that empowers field teams to build upon their direct customer knowledge and strengthen vital relationships.

A globally unified data backbone is a reliable foundation for powering AI-driven customer-centric insights and effective team collaboration. Achieving global data standardisation requires focused action in three areas:

1. Establish global customer master data: create common standards, identifiers and definitions for core entities like HCPs and HCOs (eg, mapping global specialties to local terms). These standards provide consistency and enable connected customer data to power AI insights independent of geography.
2. Connect software and data: use these master data standards to integrate information across internal functional systems (eg, sales, marketing, medical) and external sources, building a single customer view and enabling AI to automate costly manual data integration.
3. Embed insights across workflows: surface data, AI insights and recommendations within CRM processes, to drive smarter, more adaptive engagement. For instance, provide sales teams with visibility into marketing activities, allowing them to refine their customer interactions accordingly.

One company that has already proven this approach is Bayer AG. Bayer set out to give its field teams a 360-degree view of HCPs – an insight-rich CRM experience that could drive more meaningful, personalised engagements. But siloed data across geographies made that goal elusive.

“Our global data landscape was fragmented – different countries relied on different sources,” explains Stefan Schmidt, group product manager at Bayer. “To see the full picture, we needed a unified customer master.”

Under Schmidt’s leadership, Bayer began harmonising data across markets and systems – consolidating CRM, engagement history and customer master data into one connected ecosystem. Within weeks, it delivered a single source of truth that field teams valued and adopted.

Now, Bayer’s CRM doesn’t just store data – it delivers insights powered by clean, connected data. The company can scale AI-driven tools and analytics globally, consistently and confidently.

Bayer demonstrates how connecting consistent data with connected platforms like CRM delivers faster, actionable insights. This example highlights a crucial point – achieving commercial impact demands robust integration between the data foundation and the systems driving execution.

Leading companies aren’t waiting – they are gaining a competitive edge today by proactively investing in global data consistency and connecting data and software to scale AI, streamlining system integrations and lowering costs. This foundation enables CRM platforms, data and AI engines to work together, automating insight generation, personalising engagement and delivering measurable commercial impact.

Because in the end, pharma’s AI race won’t be won by AI only, but by the best end-to-end approach with a proper data foundation to scale AI and integrate insights into end users’ tools and work routines.