Parth Khanna of ACTO explains that trust — defined as a combination of compliance and capability — and role-specific context determine whether AI systems actually produce value in pharmaceutical settings.
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Providing AI systems with role-specific context improves both performance and regulatory fit.
Digital health solutions are valuable because they combine larger data sets with the ability to derive actionable insights.
Generic or overly restricted AI tools fail: too capable without guardrails loses trust, too compliant without utility gets abandoned.
Why some AI projects in pharma show measurable results while others stall comes down to trust. In an interview with Pharmaceutical Executive, Parth Khanna, co-founder and CEO of ACTO, frames trust as the product of two necessary conditions: compliance and capability. When either is missing, users stop relying on the system and the project fails to deliver business value.
Khanna's formulation is practical: a capable AI that isn't compliant risks going "off script" and producing responses that can't be used in regulated contexts. A highly compliant system that lacks capability becomes a time sink — it refuses to answer or provides only boilerplate, and people stop using it. Both outcomes destroy the human confidence needed for adoption.
This approach has operational implications. Instead of starting with a general-purpose model and adding compliance filters afterward, design the agent around the role first: map the responsibilities, common interactions, red lines, and documentation expectations. That role map then informs data access, retrieval priors, response templates, and guardrails. The result is an agent that answers questions usefully while minimizing regulatory risk.
Khanna also ties this to digital health solutions more broadly. Digital health platforms collect larger datasets and create opportunities to extract actionable insights. When those platforms incorporate role-aware AI, the insights become more usable: the AI can tailor outputs to how people actually work and the decisions they need to make. The combination of more data plus role-aware interpretation is where Khanna sees substantial business impact.
Three practical takeaways for pharma teams planning AI deployments:
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