The practical path to AI: Boosting field productivity in biopharma

Despite valuing field teams as a key source of new treatment insights, 65% of healthcare professionals (HCPs) restrict their access to three or fewer biopharmas. HCPs seek meaningful exchange, but less than 20% find content tailored to their needs, often receiving repetitive messages and experiencing information overload from disconnected commercial teams organised into functional silos. Territory managers, account managers, medical teams, and other specialists frequently deal with incomplete customer information, manual prep work, and time-consuming data entry leading to fragmented engagement.

AI has the potential to drive more impactful HCP interactions by providing engagement planning intelligence and automating routine tasks that free up time for strategic engagement. A practical AI approach centres on equipping customer-facing teams with AI-powered tools that directly inject insights into their existing workflows, enabling personalised HCP experiences and increased field productivity. But integrating AI seamlessly into commercial processes requires a deep application and the right data foundation. Through a connected ecosystem, biopharmas are evolving their commercial models for greater efficiency and customer-centric interactions.

Embedding pervasive and context-aware AI in commercial workflows

AI helps sales, marketing, and medical teams deliver timely, personalised engagements that better address HCP needs. Using large language models (LLMs) and an organisation’s broad data set, AI synthesises customer-specific information with high accuracy and speed, delivering tailored insights, proactive recommendations, and automating repetitive tasks.

"We’ve been talking about AI as an enabler. Finding the right balance between automation and human touch allows us to create those personalised relationships.”

– Richard Palizzolo, executive director and head of customer experience, Sobi

Integrating Agentic AI into daily field workflows with secure access to company data provides contextual intelligence based on business rules and user intent. Pervasive AI agents identify opportunities in real time to deliver relevant information and content exactly when it’s needed. Through a chat-based interface, AI agents can provide conversational answers, helping field teams make faster, more informed decisions. Users can act on AI recommendations with a single click, creating a true insights-to-action system. This simplifies processes for commercial teams, driving greater efficiency and personalised HCP experiences.

Four ways AI improves commercial productivity

The greatest AI potential lies in its practical application in everyday workflows. Here are four use cases that demonstrate how AI helps biopharma commercial teams engage customers more effectively:

1. Better engagement planning

AI optimises field teams’ engagement planning and execution by proactively providing pre-engagement intelligence and suggesting next-best actions based on current customer insights, including market activity, interaction data, and behavioural trends. AI agents also enable semantic content search, making it easier to find relevant, approved materials on the spot so that field teams can respond quickly to customer questions.

2. Hands-free CRM and voice-to-data entry

With voice control AI-powered capabilities, field teams operate CRM systems using spoken commands in hands-free mode. Integrating voice as an input method eliminates the need to transcribe call notes for more timely detailed information capture and significant time savings. As field teams record valuable insights, other functions instantly see what was discussed, how to follow up, and how to build on each interaction to deliver better customer experiences.

3. Real-time compliance checks

AI can auto-check text and voice notes for compliance with regulatory and company-specific policies, reducing the risk of free-form notes. AI scans for sensitive phrases, keywords, and potential risks in real time, flagging anything that may need human review. This proactive monitoring helps field teams stay compliant while maintaining the speed and ease of capturing insights on the go.

4. Content quality and speed

As content volume grows, AI helps medical, legal, and regulatory (MLR) teams be more efficient. Integrated into content management systems, AI can pre-check assets against editorial standards, brand and market guidelines, and channel rules, freeing up skilled MLR professionals to focus on high-risk materials while retaining final approval. AI also provides customer-specific insights to create personalised content tailored to HCP preferences, facilitating meaningful engagement, and ensuring that relevant, compliant content reaches the market faster.

Laying the foundation for AI success with connected data, software, processes, and teams

Effectively integrating AI into commercial workflows and realising productivity gains requires connecting software, data, processes, and teams. A simplified, unified engagement model, powered by AI, lays the foundation for true customer centricity with less manual effort.

1. Data: enabling AI-driven insights and automation

High-quality data is essential for AI to generate accurate insights, predict next-best actions, surface the right content, and automate processes. When data is poorly managed – uncatalogued, untagged, and not standardised – it limits the real potential of AI. But AI doesn’t just consume data, it can also help biopharmas build and maintain a strong data foundation by identifying gaps, improving structure, and driving smarter data practices. For example, AI can accurately predict and automatically apply relevant tags within content management systems, significantly streamlining data governance efforts.

Structuring data for fast, real-time analysis requires a common data architecture (CDA) and the ability to access large, complete data sets in near-real time through an API. Together, they enable secure, high-speed access to trusted data for AI agents to deliver instant insights and actions.

2. Software: the core of compliant and contextual AI

Successfully deploying AI in biopharma commercial operations requires a connected ecosystem for seamless data flow and collaboration. When deep software applications share a unified database and a single customer record, AI can connect the dots across previous engagements and coordinate personalised, timely interactions across all touchpoints. This results in an automatic flow of information that synchronises commercial teams.

AI that is built into the core of life sciences-specific software already has the industry’s logic, structure, and regulatory guardrails in place. This ensures AI operates with both compliance and context, allowing biopharma companies to scale AI-driven engagement with confidence and precision.

"It’s important to have the foundational elements in place, like the right data structure and technology. If you don’t, moving toward anything AI-related will be more challenging.”

– Janis Witzleb, director, platforms management, medical affairs, CSL Behring

3. Processes and teams: change management

Beyond connected data and software, successfully integrating AI also requires a cultural shift. Comprehensive change management is key to helping commercial teams embrace AI and adapt to new ways of working. This includes rethinking processes to fully capitalise on AI's automation and analytical capabilities, while improving AI literacy for field teams to strike the right balance between human intuition and AI-powered insights.

Practical AI to maximise engagement while minimising prep and data entry

When embedded in commercial workflows, AI enables customer-centric, coordinated engagement. AI processes large, complex data sets to improve engagement planning, voice-to-data entry, compliance monitoring, and content review. AI-driven insights and automation minimise field prep work for faster decision-making and strategic engagement.

A simplified, connected ecosystem of high-quality data and deep software applications creates the best foundation for practical AI. Integrating AI into users' workflows makes it possible to act on insights with a single click, improving productivity and collaboration across functions. Life sciences-specific AI safeguards compliance while driving operational efficiencies, enabling commercial teams to deliver personalised and synchronised HCP experiences. Those that master practical AI today will set the pace for an industry where insights need to move as fast as innovation.

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About the author

Matt Farrell is president of Commercial Cloud at Veeva Systems. He is responsible for the global success of the Veeva Commercial Cloud suite of applications. He is also the overall Vault CRM strategy lead. Prior to joining Veeva, Farrell was a managing director at Accenture, where he specialised in cloud-based industry solutions. He worked closely with companies, such as Veeva, as they launched new products into the market, and advised customers on how to achieve maximum value from those solutions.

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