A new Everest Group report released by Lean Solutions Group finds that 69% of organizations have moved beyond isolated AI use cases, but only 19% have significantly or extensively redesigned workflows for human-AI collaboration, and just 15% report comparable redesign of workforce roles. Based on a survey of more than 200 U.S.-focused senior decision-makers in transportation, logistics, and other supply chain-intensive industries, the study also flags uneven investment, lagging accountability structures, acute talent shortages, and inconsistent returns.
According to www.dcvelocity.com, enterprises are deploying artificial intelligence far faster than they are redesigning operations around it, with 69% of organizations having moved beyond isolated AI use cases but only 19% having significantly or extensively redesigned workflows to support collaboration between human experts and AI, according to an Everest Group research report released by Lean Solutions Group.
Adoption Outpaces Process Redesign
The research finds a widening gap between deployment and operational readiness. While 69% of organizations have moved past isolated pilots, just 15% report a comparable redesign of workforce roles to match their AI investments.
The report, titled “Designing the Human + AI Enterprise: A Transformation Agenda for Supply Chain-Intensive Industries,” is based on a survey of more than 200 U.S.-focused senior decision-makers in transportation, logistics, and other supply chain-intensive industries.
“The challenge is no longer simply getting AI into the business. It is changing how work gets done,” said Jack Freker, CEO of Lean Solutions Group. He said technology can analyze, recommend, and automate at scale, but people remain accountable for the outcome.
“The organizations that get the most from AI will combine experts in the loop, intelligent technology, and redesigned processes in a way that improves performance for customers.” — Jack Freker, CEO of Lean Solutions Group
Where Human + AI Transformation Is Headed
Looking two to three years ahead, respondents identified the leading opportunities for Human + AI transformation. Transportation planning and execution topped the list at 53%, followed by workforce scheduling and labor management at 49%, and supply chain planning and coordination at 48%.
Freker framed the objective as accountability rather than autonomy. “The goal is not maximum AI autonomy,” he said. “It is a faster, more reliable path from insight to accountable action.” He added that AI should handle work within clearly defined boundaries, while experts provide the judgment and ownership that complex supply chain operations require.
Readiness Gaps Across Judgment, Investment, and Talent
The study identified several gaps between AI deployment and operational readiness. Human judgment remains essential: decision-making is primarily human-led at 72% of organizations, and exception handling at 60%.
Investment is uneven. 67% of respondents prioritize AI platforms and 66% prioritize data and integration, compared with 33% prioritizing workforce upskilling and 17% each for governance and change management.
Accountability lags as well: only 26% have established formal structures for AI accountability. Talent pressures are also acute, with 58% of logistics and transportation organizations reporting talent shortages, versus 35% in other surveyed industries.
Returns remain inconsistent. Although 84% report or expect meaningful AI value within two years, 30% report returns below expectations and only 24% report returns above expectations.
Source: DC Velocity
Compiled from international media by the SCI.AI editorial team.
