Every major technological shift eventually reaches a point where experimentation gives way to execution. The pharmaceutical industry now faces a practical question that will shape its next decade: should companies build AI systems in-house, or buy platforms already designed for the complex, compliance-driven realities of life sciences? ACTO, a global AI-first software company that enables intelligent field excellence in life sciences, has made its position clear. It believes purpose-built AI systems — built for pharma from the start — deliver faster results, safer workflows, and stronger teams.
ACTO’s platform was created exclusively for the unique demands of sales, medical, and market access professionals who operate in the highly-regulated life sciences industry. It unites human intelligence with what the company calls empathetic AI, helping field teams improve engagement, documentation, and performance without sacrificing compliance.
“The purpose and goal of AI is to make humans more effective, not to replace them and go down the path of blindly pursuing efficiency,” said Parth Khanna, ACTO’s CEO and co-founder. This belief defines the company’s approach to product design, ensuring technology remains an amplifier of expertise rather than a substitute for it.
Pharma organizations that attempt to build their own AI solutions often find that the barriers go far beyond development. Maintaining data security, validating compliance, and continuously retraining models can drain both time and resources. The ACTO platform is designed to eliminate those obstacles.
As a SOC 2 Type II certified platform that complies with FDA 21 CFR Part 11, ACTO’s Intelligent Field Excellence platform ensures data security, regulatory compliance, and control for its customers. The result is technology that understands how life sciences teams actually work, allowing them to focus on patient impact and professional engagement rather than software and infrastructure.
As Dr. Sarah Barrett of Alnylam Pharmaceuticals explained, “Out-of-the-box AI lets your team focus on what matters — strategy, engagement, and impact.”
Still, ACTO recognizes that not every situation calls for an out-of-the-box solution. To help organizations decide when to build and when to buy, the company created its “Build vs. Buy Scoring Framework.” The tool assesses nine variables, from time to value and compliance sensitivity to clarity of the business goal, in order to provide a structured, data-driven recommendation.
“Build or buy is context-dependent. If the use case is clear, time-sensitive, and compliance-heavy, then buy. If it’s exploratory and loosely defined, then build,” said Khanna. The framework reflects ACTO’s practical philosophy: innovation should be guided by evidence, not instinct.
Compliance remains a decisive factor in that calculation. “If a vendor understands the space … it eliminates a lot of those fundamental questions [baseline requirements for pharma-specific AI solutions]. You don’t want to go two levels in and realize the solution doesn’t even fulfill first-level compliance,” said Kishin Kumar of Novartis.
ACTO’s technology is built precisely to prevent that outcome. Its infrastructure, content controls, and analytics are engineered to align with the industry’s strictest standards, allowing teams to innovate confidently within established regulatory boundaries.
ACTO’s concept of empathetic AI adds a personalized layer to the platform experience. The system learns how people work and adapts to support them, giving medical science liaisons, reps, and market access professionals tools that fit their distinct responsibilities and requirements. It surfaces insights, identifies knowledge gaps, and delivers contextual coaching when it’s needed most.
“It’s not about AI replacing field teams. It’s about enabling MSLs and reps to focus more on what they do best: engaging with HCPs in meaningful, compliant ways,” the company emphasized in its discussion.
The build-or-buy decision now defines AI adoption in pharma because it touches every core tension in the industry, including speed versus safety, innovation versus oversight, and automation versus human connection. For specialized, confidential R&D use cases, in-house systems may still make sense. But for commercial and medical operations, where compliance, scale, and efficiency are equally important, purpose-built platforms are proving their worth.
For pharma, AI maturity will be defined not by who builds the biggest system, but by who builds — or buys — the smartest one.
To learn how to evaluate, prioritize, and act on the right AI use case, click here.
