In the rush to digitize, life sciences companies may be missing the most important ingredient: actual science. A new report from Everest Group, highlighted by Zifo, a global leader in AI-driven informatics, warns that AI and IT initiatives in biopharma will flounder unless scientific expertise is embedded from the ground up.
For years, biopharma and other science-focused industries have faced a silent mismatch: scientific breakthroughs are accelerating faster than the IT systems meant to support them. Legacy data handling, outdated processes, and generic IT infrastructures can’t keep pace with the complexity of modern research. The result: investments in AI, cloud platforms, and analytics often fail to deliver meaningful outcomes.
“Speed alone isn’t enough,” says Zifo. “If your digital platforms don’t understand the language of biology, chemistry, or physics, they’re not really delivering value.” Everest Group’s research backs this claim, showing that even sophisticated AI models stumble when they aren’t anchored in scientific reality. Simply knowing technology isn’t enough—success now depends on combining IT skill with scientific domain knowledge.
Why generic IT falls short
Treating science-driven IT like standard corporate IT carries real risks:
- Compliance exposure: Blackbox AI models can trigger regulatory breaches.
- Outcome risks: Clinical trials and manufacturing decisions can become inconsistent.
- Loss of trust: Scientists and regulators may abandon platforms that don’t meet their standards.
- Reputational damage: Flawed results can derail publications and corporate credibility.
- Wasted investment: AI pilots fail to scale when scientific depth is missing.
- Scaling failures: Poorly grounded proofs of concept pile up, reducing ROI.
According to the report, embedding scientific expertise delivers tangible returns. Drug discovery (77%) and clinical development (73%) show the biggest gains, with manufacturing also benefiting (37%). Nearly 80% of pharma leaders now view scientific expertise as critical, and 40% see it as a potential competitive differentiator.
A blueprint for science-first IT
The Everest Group report outlines a practical four-step approach for companies ready to scale a science-first digital strategy:
- Assess: Evaluate current operations to measure how well science is integrated.
- Embed: Establish robust data and governance frameworks to support science-driven execution.
- Scale: Build hybrid teams that combine tech skills with scientific backgrounds.
- Institutionalize: Make the science-first approach standard across the organization.
For biopharma, the message is clear: AI isn’t a magic wand. Without scientific expertise baked into IT systems, digital transformation can stall, costing companies money, time, and credibility. As competitors increasingly adopt science-first strategies, those who ignore the report’s findings may find themselves falling behind in both innovation and compliance.
The AI revolution in life sciences isn’t just about faster computation—it’s about smarter, science-aware computation.
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