Key Takeaways
Kallik warns that regulated industries like pharmaceuticals and medical device manufacturers are rushing to adopt AI without establishing proper data foundations, creating a compliance timebomb that can trigger costly recalls and regulatory failures. Organizations should prioritize building a single source of truth for their data before deploying AI tools to avoid silent errors and ensure successful automation.
- 48% of enterprise leaders rank data-related issues as their greatest challenge to successful AI implementation, according to NVIDIA research
- Fewer than 28% of AI initiatives meet ROI expectations due to poor data quality, with most failing within 12 months, per Gartner
- AI tools querying fragmented, unchecked data sources can cause silent compliance errors—like dropping European date formats into US templates or altering mandatory fonts—triggering recalls and regulatory failures
- Kallik recommends a data-first framework using its AToM platform to intelligently extract and clean legacy data before deploying AI, eliminating manual errors and hallucinations
- A single source of truth enables enterprises to plug any corporate LLM or external AI tool securely via APIs while maintaining compliance and operational excellence
Kallik, the global leader in enterprise labelling and artwork management software, has issued a stark warning to regulated industries rushing to adopt the next generation of AI.
The company, which works with brands like Kenvue, Cardinal Health and Procter & Gamble, has warned that blindly trusting AI platforms without the ‘operational basics’ is a high-stakes gamble for regulated industries like pharmaceuticals and medical device manufacturers.
A changing regulatory landscape is creating an operational squeeze, with deadlines for US and European overhauls looming and a growing skills shortage leaving already understaffed teams struggling to manage increasingly complex product lifecycles.
This pressure, combined with a need to protect margins and get products to market fast, has triggered a race to automate; motivating businesses to adopt AI tools that can help to manage intricate labelling updates, multi-language translations and shifting regulations that manual, under resourced teams can no longer handle.
Kallik officials, though, warn that ‘panic buying’ is driving companies to deploy the wrong solutions, leading to them layering unsuitable AI checking mechanisms on top of fragmented, siloed data and opening the door to serious compliance errors. Yet, they highlight that organizations who use this ‘market anxiety’ to fix their data foundation first, can turn a regulatory headache into a significant competitive advantage.
“There’s a massive amount of noise in the market right now about what AI can do, but trying to run autonomous agents over poor legacy data is creating a compliance timebomb," says Gurdip Singh, CEO of Kallik. "One that pharma and med dev manufacturers will absolutely want to avoid. It’s a problem felt not only in those sectors, but further afield, too—in fact, a recent NVIDIA enterprise study showed that as many as 48 percent of leaders rank data-related issues as their single greatest challenge to successful implementation. After all, you can’t build a skyscraper on quicksand.
“Despite that though, we’re seeing highly regulated sectors rush past this hurdle, without considering the dangers it poses. When you ask an AI tool to query unchecked, fragmented data sources, it starts connecting the wrong dots—like silently dropping a European date format into a US template or subtly altering a mandatory font. In these high-risk environments, a tiny hallucination like that doesn’t just jam up the system; it can break the entire chain, triggering immediate compliance failures, potential reputational damage and multi-million dollar product recalls. Each putting patients in harm’s way.”
According to Singh, this data gap is the exact reason why enterprise AI projects routinely hit a wall. A global Gartner study backs up this trend, revealing that fewer than 28 percent of AI initiatives meet ROI expectations due to poor data quality, leading to the majority failing—or being quietly abandoned—within 12-months.
Instead of viewing this as a barrier to success, Kallik officials recommend that businesses frame this as an opportunity.
“Achieving a true return on your AI investment doesn’t start with the technology itself. It starts with the data. Advanced automation can handle massive migrations and instant multi-language updates at lightning speed, but it requires an absolute single source of truth to pull from," adds Singh.
“By establishing that ironclad data foundation first, businesses can put a stop to flying blind into a regulatory storm. Instead, they turn a compliance risk into a genuine pathway to operational excellence, maximum ROI and a significant competitive advantage.”
To solve the bottleneck that so many businesses are facing, Kallik is leveraging its established AToM (Assisted Tool of Migration) platform to provide a data-first framework. As a pure AI-driven engine, AToM intelligently reads and extracts data from legacy formats at the onboarding stage, entirely eliminating manual input error and duplicates that feed AI hallucinations.
That clean data then feeds directly into Kallik’s Veraciti platform, a cloud-native, single source of truth that atomises content into pre-approved, version-controlled building blocks within its central asset and phrase manager. Because the data layer is fully structured, Veraciti remains entirely vendor-agnostic, allowing enterprises to securely plug and play any corporate LLM or external AI tool into a validated environment via secure APIs.
