Enterprises are adopting artificial intelligence much faster than they are redesigning operations to realize AI’s full business value, according to an Everest Group research report released by Lean Solutions Group.
The research finds that 69% of organizations have moved beyond isolated AI use cases, yet only 19% have significantly or extensively redesigned workflows to support collaboration between people in the form of experts in the loop and AI. Just 15% report comparable redesign of workforce roles.
The report, “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 decisionmakers 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. “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.”
“The goal is not maximum AI autonomy,” Freker added. “It is a faster, more reliable path from insight to accountable action. AI should handle work within clearly defined boundaries, while experts provide the judgment and ownership that complex supply chain operations require.”
Looking ahead for leading opportunities for Human + AI transformation in two to three years, survey respondents identified transportation planning and execution (53%), workforce scheduling and labor management (49%), and supply chain planning and coordination (48%).
In other findings, 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% prioritize AI platforms and 66% data and integration, compared with 33% prioritizing workforce upskilling and 17% each for governance and change management.
- Accountability lags: Only 26% have established formal structures for AI accountability.
- Talent pressures are acute: 58% of logistics and transportation organizations report 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.
