Geopolitical uncertainty and shifting trade rules are widening the gap between supply chains that have fully embraced artificial intelligence and those still lagging. New survey data by Ivalua shows AI-equipped organizations are better prepared to absorb cost shocks, adapt strategy, and keep operations moving when disruption strikes, and the reasons run deeper than just having better technology.
Why AI Maturity Correlates with Readiness
Ivalua’s research reveals that 98% of companies with fully deployed AI systems feel prepared to manage geopolitical risks, with nearly half calling themselves “very prepared.” The drop-off is steep among those still implementing AI (21%) and nearly nonexistent for those with no plans to deploy it.
One explanation is that mature AI adoption forces companies to centralize, clean, and integrate their data, which in turn improves visibility into supplier performance, cost structures, and logistics dependencies. That visibility becomes critical when sanctions shift, trade corridors close, or tariffs change overnight.
AI leaders also tend to have scenario modeling baked into their decision-making processes, not just reacting to disruption, but running constant “what if” analyses across currencies, tariffs, and supplier lead times. In practice, that means they can pivot sourcing or production faster, without needing weeks of manual analysis.
While 77% of respondents report actively rolling out AI for procurement or supplier management, only 36% list AI as a top supply chain priority today. For U.S.-based firms, 73% of executives agree they need to invest more in technology to anticipate and offset geopolitical risks, but 65% admit trade policy uncertainty has caused them to delay or scale back investments. This tension, between recognizing the value of innovation and hesitating to commit, suggests that in a volatile world, adoption speed becomes as important as the capability itself, because risk exposure compounds while laggards are still implementing.
Investment Hesitation Meets Cost Pressures
Mature AI adopters are also weathering inflationary pressures more effectively. Among companies unprepared for disruption, 78% expect rising costs to dent profits, compared with just 50% of well-prepared organizations.
A key driver here is that AI-enabled organizations can re-optimize sourcing and inventory strategies in near real time, using live cost data and supplier risk scores to avoid locking in unfavorable terms. They also tend to automate low-value procurement tasks, freeing teams to focus on renegotiating contracts or securing alternative supply when costs spike.
Yet innovation remains on the agenda: 59% of all respondents still view transformation as a priority, even if timelines are being recalibrated. The survey also highlights an execution gap inside organizations. While two-thirds of owners and more than half of C-suite executives believe their company is “very prepared,” only 17% of senior managers and 10% of junior managers agree. That disconnect may be because AI strategies are often framed as technology projects rather than operational shifts, leaving frontline managers unclear on how predictive models or supplier risk engines actually change their day-to-day playbook.
The Overlooked Risk in AI Adoption
One under-discussed challenge is that AI’s resilience benefits are not automatic, they depend heavily on data quality, governance, and integration across the value chain. Companies rushing to deploy AI without aligning it to real-time supplier, logistics, and geopolitical data feeds risk building systems that give false confidence.
In practice, the organizations that are both resilient and innovative are those embedding AI into cross-functional decision-making and continuously stress-testing their models against live disruption scenarios, from port closures to currency devaluations. This discipline ensures that AI recommendations remain relevant when the real world shifts. In the next disruption cycle, the advantage will go not to the companies that merely “have AI,” but to those that have institutionalized the muscle memory of using it to drive operational decisions at speed.