Kallik has issued a caution to pharmaceutical manufacturers about the risks associated with deploying AI technologies without a solid data foundation. The company, known for its labeling and artwork management solutions, points out that many in the industry are hastily adopting generative AI and automation to meet increasing regulatory demands and accelerate market entry.
This urgency is compounded by skills shortages and evolving global labeling requirements, prompting a surge in investments in AI-driven automation. However, analysts warn that inadequate data readiness poses a significant barrier to the successful implementation of AI in enterprises. Gartner has indicated that organizations without AI-ready data may abandon most of their AI initiatives, as poor data quality can severely impact the returns on these investments.
Gurdip Singh, CEO of Kallik, emphasizes that AI's effectiveness hinges on structured and validated data. He cautions that utilizing AI on fragmented or unverified data can lead to compliance issues, product recalls, and safety risks. Minor errors, such as incorrect formatting or label inaccuracies, can have serious implications for regulatory compliance.
Singh advocates for a "single source of truth" in product data as a prerequisite for implementing advanced AI solutions. Kallik promotes a data-first workflow through its Assisted Tool of Migration (AToM), which organizes legacy data for integration into its Veraciti platform. This approach not only ensures compliance but also enhances operational efficiency, positioning organizations to better adapt to regulatory changes. As Singh notes, the journey to realizing AI's full potential begins with robust data governance.