A growing number of executives see artificial intelligence as critical to staying competitive, but data integration issues are holding them back. A new survey by analytics firm Qlik found that 87% of business leaders now view AI as a strategic necessity. Still, 74% say they’re struggling with data challenges that are slowing progress. Chief among them: fragmented systems and data silos that prevent AI tools from functioning effectively.
Big Promises, Operational Hurdles
Qlik’s survey of 500 executives, conducted in April, points to a clear divide between AI aspirations and operational readiness. While most respondents view AI as essential to growth, only a minority have managed to fully deploy it within their supply chains.
Disconnected data and slow ingestion processes were cited as top hurdles, particularly in sectors burdened by legacy systems and complex supplier networks. “Organizations clearly recognize that merely investing in AI is insufficient; what matters now is delivering tangible outcomes,” said Qlik CEO Mike Capone. “Yet, the road to production AI remains blocked by persistent hurdles—cost, complexity, and data fragmentation.”
For supply chain executives, the stakes are high. With pressure mounting to show returns on AI investments, and economic uncertainty adding urgency, clean, connected data is no longer optional.
Confidence in AI Still Wavers
Beyond technical issues, trust in AI remains fragile. While 88% of leaders express some confidence in AI-generated insights, only 42% say they have full, audit-ready trust. Among directors, that number falls to 37%, compared with 48% at the C-suite level.
The caution is warranted. AI outcomes are only as reliable as the data behind them. Gaps in governance or fragmented systems can easily undermine decision-making rather than support it.
Some firms are responding by rethinking their data strategies. ZF Friedrichshafen, a German automotive supplier, recently consolidated more than 200 data streams onto a single cloud-based platform, accelerating deployment of AI tools in maintenance and logistics.
Where to Start
Progress with AI may depend less on algorithms than on the underlying architecture of data. For supply chain leaders, the first step is often diagnostic: identifying where fragmentation is most acute. That could mean siloed inventory systems, supplier data stored in incompatible formats, or transportation platforms that don’t sync with demand planning tools.
From there, executives can begin mapping a path toward integration, consolidating systems, establishing data standards across departments, and investing in platforms that support real-time interoperability. Governance frameworks must evolve in parallel, ensuring that as AI tools grow more sophisticated, the data feeding them remains trustworthy, up to date, and auditable.
Companies that treat data integration not as a one-off project but as a core operational capability will be better positioned to extract measurable value from AI. Those that don’t may find themselves with advanced tools but unreliable foundations, an increasingly risky position in an environment where performance is being closely watched.
