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How the Best Supply Chain Leaders Set Every Transformation Up to Win

10 min read
Abstract illustration of a four stage transformation sequence running left to right, with a shortcut path that skips the first two stages

Quick answer. The transformations that hold up under pressure are not the ones that picked a better platform. They are the ones that settled the operating model question, how this operation needs to run and whether the current structure supports it, before any vendor RFP went out.

At a Glance

The gap

  • 88% of enterprises are deploying AI. 81% show no measurable P&L impact.
  • 56% of chief supply chain officers say integrating AI with legacy systems is their primary barrier to scale.
  • The WMS market is projected at $11 billion by 2031 at a 17.98% CAGR. Go-live is not the outcome.
  • Operating model design is skipped in most transformations. The value gap forms there.

The differentiator

  • High performing organizations resolve the operating model question before any vendor RFP is issued.
  • AI-native supply chain frameworks require building workflows around AI from the ground up, not retrofitting AI onto legacy ERP and WMS.
  • Process design and decision rights must precede platform selection.
  • 55% of supply chain leaders expect agentic AI to reshape workforce structure by 2030.

Supply chain transformations that produce durable results, measurable gains in fulfillment accuracy, inventory record precision, and documented P&L impact, tend to follow a specific sequence. They do not open with a platform selection conversation. They open with an operating model question: how does this operation need to run, and does the current structure support it?

The World Economic Forum's Global Value Chains Outlook 2026, drawing on more than 300 senior executives across industry and government, described the current period as one of structural volatility rather than a cyclical downturn expected to correct on its own. 74% of business leaders now identify supply chain resilience as a source of competitive advantage rather than a cost center to manage. Operating models built for a more predictable environment are not well suited to the one supply chain leaders are navigating today.

Where Most Supply Chain Investments Stall

Most enterprise transformation programs open with vendor evaluation. Given the market activity, that approach is not unreasonable on its face:

  • The WMS market will reach $11 billion by 2031 at a 17.98% CAGR, according to Mordor Intelligence.
  • Manhattan Associates crossed $1 billion in annual revenue in 2025 and posted 21% year over year cloud subscription growth for the full year.
  • 88% of enterprises are now deploying AI in at least some operations.

The investment case for supply chain technology is well established. The delivery record is more complicated. Organizations implement capable WMS platforms and find, well after go-live, that fulfillment accuracy sits roughly where it was before the program began. AI modules are deployed across operations, and leadership cannot identify where the impact appears on the income statement. The technology performed to specification. The operating environment it was installed into was not prepared to support it.

The greatest friction point in scaling AI today is not the technology itself, but the legacy environments in which it is being deployed.

Gartner, survey of 140 senior supply chain leaders, April 2026

That survey identified where the friction concentrates:

  • 56% of chief supply chain officers identified integrating AI with legacy systems as a major operational challenge.
  • 50% reported that their organizations lack the internal expertise to implement and manage AI at scale.

What Happens When Platform Selection Leads

When a company leads its transformation with vendor evaluation, the RFP gets written to describe the operation as it currently runs. Requirements reflect existing workflows. The result is software selected to optimize a business as it stands today, not the one leadership is working to build.

81%
of organizations deploying AI report no meaningful improvement to the bottom line, in McKinsey's State of Organizations 2026, which drew on more than 10,000 executives across fifteen countries. In the same research, 88% are deploying AI in at least some operations and 86% say their organizations are not prepared to run it effectively day to day.

Treating AI adoption as a technology procurement project, rather than an operating model redesign, is the most consistent explanation for those numbers. The technology performs. The workflows surrounding it were not designed to use it effectively, because the workflow redesign never happened.

In warehouse operations, the same dynamic shows up in labor decisions. The US recorded 490,000 open logistics positions in 2024. European distribution centers reported staffing shortfalls reaching 25% in some markets. Automation investment follows as a response. When the workflows those systems will run have not been redesigned first, the equipment underperforms against its technical specifications. The shortfall is not in the technology.

What the Best Transformations Do Differently

High performing supply chain organizations hold the platform conversation until the operating model work is complete. On paper, that sequencing looks straightforward. In practice, it requires resisting significant internal pressure to move directly to vendor selection, particularly in organizations where software implementation is the established model for change.

Operating model first, platform third Four stages run left to right: operating model design, data readiness assessment, platform selection, and implementation. A separate path shows the common shortcut, which skips the first two stages and opens directly at platform selection, carrying current state requirements into the RFP. THE SEQUENCE THAT HOLDS UP 01 Operating modeldesign 02 Data readinessassessment 03 Platformselection 04 Implementation THE SHORTCUT MOST PROGRAMS TAKE Skipped. Requirements are neverseparated from the current state. RFP opens here, describing theoperation as it runs today
The order is the differentiator, not the platform. When stages one and two are skipped, the RFP encodes the current operation, and the software gets selected to optimize a business the organization is trying to move away from.

Gartner defined the strategic destination in 2026 as the AI-native supply chain: an operating model built from the ground up to run on AI, rather than a legacy architecture with AI modules attached afterward. Grafting AI capabilities onto existing ERP and WMS structures preserves the operational assumptions those systems were designed around. Local efficiency gains are possible within that structure. Structural improvement is not.

74% of business leaders now prioritize resilience as a driver of growth. Competitive advantage comes from foresight, optionality, and ecosystem coordination.

World Economic Forum, Global Value Chains Outlook 2026, with Kearney

During 2025, tariff escalations reshaped more than $400 billion in global trade flows, and container shipping rates peaked 40% above the prior year level. The organizations that absorbed those conditions without significant disruption were running operating models built for reconfiguration. Their technology choices followed from the operating model. Organizations that struggled had systems optimized for an environment that no longer exists.

What Changes When the Sequence Is Right

Correct sequencing has specific, observable effects on how implementations unfold and what they produce.

Late-stage scope changes drop substantially

Requirements defined before a program launches, rather than discovered deep into vendor design workshops, reduce the volume and cost of late changes. In WMS implementations, scope changes arriving after system configuration is underway carry consequences beyond the change itself. Schedule slippage, budget overrun, and loss of team confidence compound in ways that are difficult to recover from before go-live.

Workforce strategy becomes a deliberate decision

Gartner's survey of 509 supply chain leaders found that 55% expected agentic AI to reduce entry-level hiring needs. Organizations pausing recruitment in anticipation of that shift may create a different problem: a shortage of AI-native operational talent at the point when it becomes essential, with premium hiring costs above today's market.

Success criteria exist before the program starts

Order accuracy rate, units per labor hour, inventory record accuracy, on-time shipment percentage. These KPIs should be defined, baselined, and formally agreed before implementation begins. When organizations establish them after go-live, the window for low cost course correction has typically already closed.

AI is no longer an optional experiment. It is rapidly being incorporated into supply chain processes as a differentiating layer atop enterprise systems.

Gartner Supply Chain, Year in Review 2025

Four Questions Before Any RFP Goes Out

Before a vendor evaluation opens, four questions are worth working through at the leadership level, without a vendor in the room.

  1. Where in the supply chain are operational costs or service failures concentrated, and is the root cause understood with specificity?
  2. Which operational decisions are consistently slow or variable in outcome, and does that reflect process design, data availability, or both?
  3. Is this implementation designed to run the operation as it currently exists, or as it needs to operate as the business changes?
  4. What does success look like once the system has run through a full peak, and which team is accountable for measuring it?

Vendor conversations benefit from clear answers to all four. When those answers are absent going into an RFP, they tend to resurface downstream as scope disputes, implementation delays, and post go-live performance gaps that are expensive to address after the system is live.

The organizations that resolve these questions before vendor selection enters the picture are the ones where every subsequent decision, platform choice, integration design, change management approach, and post go-live measurement, runs on a more stable foundation.

Where Everest Comes In

Everest Technologies specializes in Manhattan Associates WMS implementations and supply chain transformation programs. The pattern that surfaces most frequently in our engagements is the one described throughout this article: operating model decisions get deferred, the platform gets selected against current state requirements, and the gap between expected and actual outcomes gets managed quietly after go-live.

Our work concentrates in three areas where implementation risk is highest.

Pre-RFP operating model design

Current state process mapping, decision rights analysis, data readiness assessment, and KPI framework definition. This is the work that precedes vendor selection, and it is where most transformations are structurally won or lost before a contract is signed.

WMS and ERP integration architecture

Manhattan Associates implementation, legacy ERP integration design, WES configuration, and UAT governance. The integration layer is where most WMS programs slip on timeline and scope, often because the architectural decisions were deferred past the point where they can be made cleanly.

Post go-live outcome measurement

Order accuracy benchmarking, labor productivity analysis, inventory record audits, and continuous improvement cycles tied to KPIs agreed before the program began. Go-live is a milestone, not the finish line.

Preparing a WMS evaluation?

If a prior implementation underperformed and the root cause has not been clearly identified, we are happy to compare notes. We have seen this pattern across 300+ go-lives.

Let's talk

Sources

  1. World Economic Forum. Global Value Chains Outlook 2026. With Kearney, 300+ executive responses. January 2026.
  2. Gartner. Technology Integration and Talent Perceived as Key Roadblocks to Scaling AI in Supply Chain. Survey of 140 senior supply chain leaders. April 2026.
  3. Gartner. Supply Chain Organizations Pausing Entry-Level Hiring for AI Will Face Higher Costs by 2030. Survey of 509 supply chain leaders. February 2026.
  4. Gartner Supply Chain. Year in Review: Supply Chain 2025. Stan Aronow, Wade McDaniel. December 2025.
  5. McKinsey and Company. The State of Organizations 2026. 10,018 executives across 15 countries.
  6. McKinsey and Company. The State of AI in 2025. QuantumBlack. November 2025.
  7. Mordor Intelligence. Warehouse Management System (WMS) Market Report, 2026 to 2031. January 2026.
  8. Manhattan Associates. Full Year 2025 Financial Results.
  9. PIIE and WEF. Trade flow reshaping from 2025 tariff escalations, cited in the WEF Global Value Chains Outlook 2026.
  10. IMF. Shipping cost data, cited in the WEF Global Value Chains Outlook 2026.
  11. US Bureau of Labor Statistics. Logistics sector job openings, 2024.
Supply Chain Transformation Operating Model WMS Implementation Manhattan Associates Vendor Selection