Effectiveness Not Volume: Q&A with Pooja Lal

Pooja Lal
VP of strategy and
commercial content
Veeva Systems

It’s been several years since AI took over the tech spotlight, and companies and workers across various industries have had time to test it and see where it best applies. Pooja Lal, VP of strategy and commercial content at Veeva Systems, spoke with Pharmaceutical Executive about some of the ways the pharmaceutical industry is going beyond content creation with AI.

Pharmaceutical Executive: With AI on the minds of content leaders, where do you see companies focusing?

Pooja Lal: With or without AI, the key has always been to make the content more effective. Yet, in recent years, we have seen volume increase rather than usefulness. Biopharmas are creating record-high volumes of material to engage healthcare professionals (HCPs) in relevant ways across channels. Production is up as much as 30% but field teams rarely or never use 77% of approved content.¹

Now, enter AI as a way to help, but as companies experimented with solutions, they focused on how to shortcut the generation of content. When asked how life sciences field reps add the most value, over 60% of HCPs said it’s by understanding their needs and sharing only relevant content to make interactions actionable to drive better patient outcomes. I meet with biopharma content leaders often and they are telling us that more content generation is not their immediate priority—they need AI to produce more relevant content that can be approved faster.

PE: How can companies use AI for effectiveness and speed rather than volume?
Lal: We have top 20 biopharma customers that rely on highly skilled medical, legal, and regulatory (MLR) experts to create relevant and compliant material. They found they tap into these reviewers’ expertise too late in the cycle. Without a more proactive role or visibility into content creation, reviewers can be perceived as a bottleneck.

AI promises to help accelerate content, but it is just one lever. If you can start generating the right compliant content from the very beginning, the entire process gains speed rather than just relieving the bottleneck at the end. An example that supports AI success is automated claims processing that auto-links to a centralized library, removing 90% of the manual work. AI solutions that improve reviewers’ experience—without removing them—is key.

PE: How can AI solutions improve the experience for reviewers?
Lal: Reviewers are adapting to a new way of working. This starts with having an AI product that is intuitive to help instill trust to build industry adoption. MLR teams communicate back and forth with marketing operations, brand teams, agency partners, and other stakeholders. Bringing teams together early in the content lifecycle with AI solutions is a significant shift for teams to co-create content initially and reduce rework later, important for expedited launches. Adopting AI capable of delivering pre-reviewed content that’s 90% of the way there is a proactive step in reimagining the content supply chain.

Ensuring compliance with FDA requirements remains an important responsibility of MLR teams, which AI alone cannot handle. Customers tell us that while automation aids in approaches like tier-based reviews, human oversight is crucial to maintaining accuracy and compliance.

For example, with Veeva MLR Bot, AI prioritizes quality, speed, and trust by focusing first on MLR pre-review efficiency. Using an organization’s large language model (LLM), the application is a chat-like assistant that performs quality checks before MLR review and approval. It checks for adherence to editorial standards, brand guidelines, market guidelines like automated black box warnings and compliance with changing local health authority regulations, and channel rules like unsubscribe options and accessibility.

These initial checks are foundational to get right so that AI can better learn and solve for more compelling challenges like claims management, risk-based reviews, content scoring, and content reuse strategies.

PE: With so many ways to apply AI, where and how should companies get started?
Lal: They are getting started now, and the learnings are first to innovate ‘upstream.’ GenAI and process enhancements can proactively prepare all reviewers to create more compliant, personalized content faster, reducing rework at the end of the cycle.

Second, improve the reviewer experience by using AI for repetitive, administrative, and manual tasks and reduce in-person review meetings.

Third, prioritize scalability. Avoid custom AI tools not designed to work within content platforms or that require complex integrations with business intelligence tools. Consider high-speed access to the data needed for AI and the essential groundwork, such as standard taxonomy, critical for deploying AI at scale.

The most important way to get started is to focus on a single impact area. Carefully select an initiative to get the best ROI from AI use. AI for MLR pre-review, which prioritizes content quality over quantity, can reduce cycle times by up to 80%.

Sources

1. Veeva Pulse Field Trends Report Q4 2022. Veeva. https://www.veeva.com/resources/veeva-pulse-field-trends-report-4q22/