New findings in the April 2026 edition of The 2026 Certainty Project from PYMNTS Intelligence reveal that as geopolitical instability accelerates, that divide is becoming impossible to ignore.

The report highlighted that while 27% of firms overall report high levels of uncertainty, that figure rises to nearly half of goods firms (47%).

For companies that manufacture, transport, warehouse or sell physical goods, volatility is no longer episodic. It is structural. Tariffs shift with elections and court decisions, shipping lanes close because of regional conflict, while industrial policy rewrites sourcing economics overnight.

Even modest political tensions can reverberate through inventory planning, freight pricing and supplier reliability within days.

The burden of absorbing that instability is falling disproportionately on goods companies.

The Physical Economy is Becoming the Front Line of Instability

Software firms can often reroute around disruption. Goods companies cannot.

A software platform selling subscriptions internationally may face regulatory pressure or currency fluctuations, but it rarely has inventory trapped at ports or components stranded by export controls. A consumer packaged goods manufacturer, by contrast, operates inside a constant collision between geopolitics and physics.

For goods companies, this creates a uniquely difficult operating reality. They must simultaneously manage inflation risk, transportation volatility, supplier concentration, labor shortages, inventory exposure and policy unpredictability, all while preserving margins in increasingly price-sensitive markets.

The report found that the companies adapting most effectively are not attempting to eliminate uncertainty altogether. Instead, they are redesigning operations around flexibility itself.

In more stable eras, executives were rewarded for decisiveness and long-range certainty. Today, the most effective leaders often display a different set of capabilities: adaptability, probabilistic thinking and comfort operating without complete information.

Read the report: Forecasting Under Pressure: New Data Shows Uncertainty Is Still Running High

Technology is also changing how goods companies respond to instability. Artificial intelligence, once discussed primarily through the lens of consumer applications and productivity tools, is becoming deeply embedded in logistics and operational forecasting.

Forecasting systems can now ingest real-time shipping data, weather patterns, commodity pricing, port congestion metrics and geopolitical developments simultaneously. Machine learning models can identify demand fluctuations earlier and simulate alternative sourcing or routing scenarios before disruptions escalate into crises.

Executives are discovering that forecasting accuracy depends not only on computational sophistication but also on organizational adaptability. Algorithms can detect patterns, but they cannot eliminate structural uncertainty.

That capability matters because uncertainty punishes delayed reactions more severely than imperfect ones.

The End of the Stable Forecast

The broader transformation is philosophical as much as technological. For years, supply chains were viewed primarily as cost centers. Increasingly, they are becoming strategic intelligence systems.

And while traditional procurement models emphasized leverage and cost negotiation, the emerging model prioritizes collaboration, transparency and mutual resilience. Firms are sharing more demand data with suppliers, coordinating inventory strategies more closely and investing in longer-term partnerships that improve stability across the network.

The companies that succeed in this environment will not necessarily be the ones with the lowest costs. They will be the ones capable of adapting fastest while maintaining continuity under pressure.

For goods companies, that may feel less like an opportunity than an obligation. But firms that redesign successfully for resilience could ultimately gain something increasingly rare in the modern economy: the ability to keep moving when everything else stops.

Because the real lesson emerging from today’s forecasting environment is not that prediction has become impossible. It is that certainty has become expensive, fragile and potentially dangerous.