The global manufacturing sector is uniquely positioned to capitalise on artificial intelligence technologies, according to Tim Long, global head of manufacturing at cloud-based data platform company Snowflake.
In a statement, Long outlined three key predictions for how AI will transform the sector by 2026, addressing key critical challenges while creating competitive advantages for early adopters.
Prediction #1: AI to combat skilled labour shortages
The manufacturing industry is seeing a global shortage of skilled workers, as major industrial economies struggle to fill critical positions in various fields, from pipe filters to essential technical roles.
Long predicts that by 2026, manufacturing companies will deploy AI solutions that augment skilled workers in complex tasks while automating routine processes.
“As labour costs rise worldwide and skilled workers become increasingly scarce, AI-driven efficiency in both production and supply chain operations will separate competitive manufacturers from those struggling to maintain output and manage costs,” the exec says.
“Companies that master AI-powered productivity gains will capture market share from competitors still relying on traditional labour-intensive approaches.”
Prediction #2: Manufacturing’s controlled environment: Perfect for AI investment validation
While many industries struggle to measure AI return on investment, Long believes manufacturing’s unique ability to create controlled experiments gives it a critical advantage in validating AI investments.
By 2026, manufacturing teams will increasingly leverage this natural testing capability to demonstrate clear performance improvements before scaling AI deployments. Long explains that the industry will shift from “experimental pilots to production applications only after controlled trials prove measurable outcomes—whether in defect reduction, output improvements, or operational efficiency gains.”
This disciplined, evidence-based approach will position manufacturing as a leader in demonstrating concrete AI value, potentially providing a model for other industries, the Snowflake exec explains.
Prediction #3: Agentic AI to drive operational optimisation
Long’s third prediction focuses on agentic AI, autonomous AI systems that can make decisions independently, becoming a significant factor in manufacturing logistics and production optimisation.
According to Long, by 2026, manufacturing companies will deploy AI agents to make autonomous operational decisions that directly impact efficiency and cost reduction.
These applications will include “expediting product lots to meet delivery deadlines, optimising inventory routing based on real-time demand signals, and automatically routing products for quality inspection or determining optimal manufacturing sequences.”
This business-outcome-driven approach will accelerate the adoption of agentic AI, with early adopters gaining significant operational benefits as these systems prove their value in controlled production environments.
