The firm, which provides enterprise labelling and artwork management software to global organisations including Kenvue, Cardinal Health and Procter & Gamble, says many regulated manufacturers are rushing to deploy generative AI and automation tools without first establishing a reliable data foundation.
The warning comes as pharma companies face mounting regulatory pressure, evolving global labelling requirements and increasing demands to bring products to market more quickly.
These challenges, combined with ongoing skills shortages, are driving greater investment in AI-powered automation across manufacturing and regulatory operations.
Industry analysts have similarly highlighted data readiness as one of the biggest barriers to successful enterprise AI adoption.
Gartner, for example, predicts that organisations lacking AI-ready data will abandon the majority of AI projects, while poor data quality continues to undermine returns on AI investment.
According to Gurdip Singh, CEO of Kallik, AI can only deliver reliable results when built upon structured, validated information.
"Trying to run autonomous agents over poor legacy data is creating a compliance time bomb," said Singh.
When you ask an AI tool to query unchecked, fragmented data sources, it starts connecting the wrong dots.
He warned that seemingly minor errors, such as incorrect regional date formats, altered mandatory fonts or inaccurate label content, could trigger regulatory non-compliance, product recalls and potential risks to patient safety.
Rather than viewing AI as a standalone solution, Singh argues that manufacturers should first establish a "single source of truth" for product data before introducing advanced automation.
To support this approach, Kallik is promoting a data-first workflow built around its AI-powered Assisted Tool of Migration (AToM), which extracts and structures information from legacy systems before transferring it into the company's cloud-native Veraciti labelling and artwork management platform.
Veraciti stores approved content as version-controlled, reusable assets, allowing pharmaceutical manufacturers to integrate their preferred large language models (LLMs) through secure APIs while maintaining auditability and regulatory control.
The platform, the company says, is designed to support multilingual labelling updates, artwork management and lifecycle compliance across global product portfolios.
Singh believes organisations that invest in data governance before AI deployment will be better positioned to meet evolving regulatory requirements while improving operational efficiency.
"Achieving a true return on your AI investment doesn't start with the technology itself," he said. "It starts with the data."
