Enterprises are adopting artificial intelligence faster than they are redesigning the workflows, workforce roles and governance structures needed to realize its full value, according to new research from Everest Group commissioned or released in partnership with Lean Solutions Group. The study, Designing the Human + AI Enterprise: A Transformation Agenda for Supply Chain-Intensive Industries, surveyed more than 200 U.S.-focused senior decision-makers across transportation, logistics and related industries. It found that while 69% of organizations have moved beyond isolated AI use cases, only 19% have significantly or extensively redesigned workflows for human–AI collaboration.

AI deployment is advancing faster than operational change

The findings point to a gap between implementing AI tools and restructuring how work gets done. According to the study, only 15% of surveyed organizations have made comparable changes to workforce roles, suggesting that many businesses are layering AI onto existing operating models rather than redesigning processes around the technology.

That distinction matters in supply chain-intensive industries, where decisions often involve incomplete information, time-sensitive exceptions and coordination across customers, carriers, warehouses and other stakeholders. Automating an individual task may improve speed, but broader operational gains can depend on how AI systems interact with employees, business rules and connected data.

The report also found that decision-making remains primarily human-led at 72% of organizations, while 60% rely primarily on people for exception handling. These figures suggest that human judgment continues to play a substantial role even as AI adoption expands.

For enterprise leaders, the challenge is therefore not just selecting AI platforms. It is determining which activities can be automated, where human review remains necessary, and who is accountable when an AI-assisted decision affects operations or customers.

Investment priorities reveal a readiness gap

The research highlights an imbalance between technology spending and organizational preparation. Some 67% of respondents prioritize AI platforms, and 66% prioritize data and integration. By comparison, 33% prioritize workforce upskilling, while just 17% prioritize governance and another 17% change management.

This investment pattern may create execution risks. AI tools depend on reliable data and integration, but successful deployment also requires employees to understand new workflows, managers to define decision rights, and organizations to establish oversight mechanisms.

Only 26% of respondents reported having formal structures for AI accountability. Without clear ownership, businesses may struggle to evaluate AI-generated recommendations, investigate errors and ensure that automated processes operate within approved boundaries.

The study also identifies a talent challenge specific to logistics and transportation: 58% of organizations in these sectors report talent shortages, compared with 35% in other surveyed industries. Human–AI collaboration could help employees focus on complex work, but realizing that benefit requires deliberate role design and training.

AI returns remain uneven despite high expectations

The study found that 84% of respondents report or expect meaningful AI value within two years. However, 30% said returns were below expectations, while 24% reported returns above expectations. The figures illustrate the difference between anticipated benefits and value already demonstrated.

External research points to a similar challenge. In October 2026, Gartner reported that only 13% of surveyed CIOs said their enterprises had achieved significant or transformational value from AI. Gartner also emphasized the importance of defining business value, establishing governance and setting criteria for scaling or stopping initiatives.

For supply chain operators, relevant measures can include delivery reliability, exception-resolution time, labor utilization, cost per shipment and customer satisfaction. These operational metrics give organizations a more concrete basis for assessing AI than adoption rates or the number of deployed tools alone.

Four stages for human–AI transformation

Everest Group’s report outlines a four-stage transformation path: Rewire, Replicate, Orchestrate and Adapt. The framework is intended to help organizations progress from individual AI applications toward coordinated execution across functions.

Lean Solutions Group connects these findings to its Experts in the Loop model, which combines AI-enabled technology with experienced professionals and redesigned workflows. Under this approach, AI handles suitable tasks within defined boundaries, while people provide oversight, resolve exceptions and retain responsibility for consequential decisions.

The announcement also describes a case study involving MOD Delivery Services, a final-mile logistics provider. According to Lean Solutions Group, the collaboration focused on standardizing processes, improving data visibility and supporting automation, while allowing logistics professionals to concentrate on exceptions, customer relationships and complex decisions. The announcement does not provide quantified results for the case study.

Looking ahead two to three years, respondents identified transportation planning and execution (53%), workforce scheduling and labor management (49%), and supply chain planning and coordination (48%) as leading opportunities for human–AI transformation.

The research was introduced alongside FreightWaves F3, held October 26–28 in Chattanooga, Tennessee. Its central message is that AI transformation requires more than deploying new tools: organizations must also redesign workflows, develop employee capabilities and establish clear accountability for results.

Market Landscape

The findings reflect a broader enterprise AI challenge: scaling adoption does not automatically produce measurable business value. Organizations need to connect AI investments to operating models, workforce capabilities, data infrastructure and governance.

Gartner’s October 2026 research similarly highlights operating-model redesign as a key factor in realizing AI value, rather than simply adding tools to existing processes.

In logistics, the stakes are particularly practical. AI can support planning, scheduling, forecasting and exception detection, but unreliable data, fragmented systems and unclear decision rights can limit its operational impact. Human oversight remains important where disruptions require contextual judgment or customer-specific decisions.

Strategic Outlook

For supply chain leaders, the report suggests evaluating AI transformation through three connected questions:

• Workflow redesign: Has the organization changed how work moves between AI systems, employees and business functions?

• Accountability: Are decision rights, escalation procedures and governance responsibilities clearly defined?

• Business value: Are AI initiatives improving measurable operational outcomes, not just adoption or task completion?

Lean Solutions Group’s Experts in the Loop approach is one model for combining automation with human expertise. Its effectiveness should ultimately be assessed through independently measurable outcomes, such as reduced processing times, improved delivery performance and lower operating costs.

The broader implication is that the next phase of enterprise AI competition may depend less on who deploys the most tools and more on who redesigns work most effectively around them.

Top Insights

• AI adoption is outpacing workflow transformation: 69% of surveyed organizations have moved beyond isolated use cases, but just 19% have significantly or extensively redesigned workflows.

• Workforce redesign remains limited: Only 15% report comparable changes to workforce roles, highlighting the organizational challenge of integrating AI into daily operations.

• AI returns remain inconsistent: While 84% report or expect meaningful value within two years, 30% report below-expectation returns and 24% report above-expectation returns.

• Governance and skills need more attention: Only 26% report formal AI accountability structures, while workforce upskilling ranks below technology and integration priorities.

• Logistics offers several transformation opportunities: Respondents identified transportation planning, workforce scheduling and supply chain coordination as leading areas for human–AI collaboration.

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