What specific data and governance issues does the pharma industry face with AI?

Roughly 73% of biopharma companies report significant data issues, 89% of AI pilots never reach production, and trust in AI systems has declined from 61% in 2019 to 53% today, according to Axtria CEO Jaswinder Chadha's keynote at the conference.

What are the four foundations Axtria recommends for AI deployment?

An AI-ready data supply chain, a semantic layer for agent accuracy, software guardrails for deterministic precision where stakes are highest, and a governance framework for digital workers at scale.

How has adoption of AI changed when framed as elevation rather than replacement?

A senior leader at a major pharma company reported that when her team made clear AI was meant to elevate marketers rather than replace them, adoption rose to over 90%.

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Top Companies' AI Moves

Life sciences executives gather to argue that AI agents need a trustworthy data foundation before deployment—not the other way around.

Top Companies AI — US (2/2)7h ago5 min read

Key takeaway

Axtria held a June 10–11 conference in Princeton for over 450 life sciences executives to address a persistent problem: pharma companies are deploying AI agents without first fixing underlying data quality and governance issues. With 73% of biopharma reporting significant data problems and 89% of AI pilots failing to reach production, the message was clear—build the foundation first, or trust in AI will continue to erode.

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3 Key Points

What happened
Axtria, an AI analytics provider for life sciences, hosted a conference on June 10–11 in Princeton, New Jersey, drawing more than 450 executives from over 100 life sciences, MedTech, and biotech organizations to discuss how enterprises can safely deploy AI agents at scale.
Why it matters
The pharma industry faces a credibility gap—roughly 73% of biopharma companies report significant data issues, 89% of AI pilots never reach production, and trust in AI systems has fallen from 61% in 2019 to 53% today, even as the sector races toward a projected 100,000-plus AI agents. Axtria's CEO argued that fixing data infrastructure, adding semantic accuracy layers, and establishing governance frameworks must come before agents are deployed; companies that succeed will be those that build trust, not those that move fastest.
What to watch
Axtria CEO Jaswinder Chadha outlined four foundations enterprises must establish: an AI-ready data supply chain, a semantic layer for agent accuracy, software guardrails for deterministic precision, and a governance framework for digital workers at scale. The conference highlighted a real-world example—Quest Diagnostics implemented an Axtria SalesIQ sales force alignment solution across 34 role types and a ~1,300-person field organization, described as one of the most complex sales force alignments in healthcare.

FAQ

What specific data and governance issues does the pharma industry face with AI?: Roughly 73% of biopharma companies report significant data issues, 89% of AI pilots never reach production, and trust in AI systems has declined from 61% in 2019 to 53% today, according to Axtria CEO Jaswinder Chadha's keynote at the conference.
What are the four foundations Axtria recommends for AI deployment?: An AI-ready data supply chain, a semantic layer for agent accuracy, software guardrails for deterministic precision where stakes are highest, and a governance framework for digital workers at scale.
How has adoption of AI changed when framed as elevation rather than replacement?: A senior leader at a major pharma company reported that when her team made clear AI was meant to elevate marketers rather than replace them, adoption rose to over 90%.

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Life sciences executives gather to argue that AI agents need a trustworthy data foundation before deployment—not the other way around.

Key takeaway

3 Key Points

FAQ

Discussion

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Stay ahead with AI news