As AI adoption accelerates across manufacturing and services, vendors are embedding opaque premiums into their bids under the guise of “AI-driven value.” These markups, often presented as justifiable for automation, analytics, or predictive capabilities, are difficult to verify and even harder to benchmark. What this signals is not simply opportunistic pricing, but a broader shift in how suppliers are redefining value propositions in the age of algorithmic sourcing.
For sourcing leaders, the risk isn’t just overpaying, it’s losing cost transparency in categories where AI itself is meant to enhance clarity.
When Intelligence Becomes Intangible
AI-driven supplier improvements are very real in manufacturing and logistics. For example, Siemens’ MindSphere platform applies machine learning to IoT sensor data for predictive maintenance, detecting issues like motor misalignment or bearing wear before failure, thus reducing unplanned downtime and increasing operational efficiency.
However, when it comes to customized components, logistics services, or BPO engagements, many suppliers invoke “proprietary AI” or “machine‑learning optimization” to support higher bids, yet rarely provide transparent metrics linking AI deployment to concrete savings. According to Keelvar’s 2024 analysis, AI references in sourcing proposals surged in early 2024, but fewer than 30% of those bids included performance data such as cycle‑time improvement or cost-avoidance figures.
This introduces a new kind of quote asymmetry. Traditionally, cost opacity was confined to low-volume or highly customized categories. Now, even in standardized procurement, such as injection molding, LCL freight, or IT support, AI claims are distorting price comparability. Buyers find themselves negotiating against rhetoric rather than verified metrics, making it harder to enforce cost discipline or build robust total-cost models. Without a framework to validate AI-linked markups, the sourcing process risks shifting from fact-based to faith-based.
Reasserting Cost Discipline in the Age of AI-Enhanced Bids
AI Audit Trails: Require suppliers to detail how AI contributes to cost, quality, or lead time improvements. This should include specific technologies used, deployment maturity (e.g., pilot vs. production), and supporting metrics like cycle time reduction, yield improvements, or labor savings.
Surcharge Segmentation: Separate the AI-related components of pricing from base service or production costs. This enables comparative quote analysis and isolates whether vendors are inflating rates or genuinely delivering differentiated outcomes.
Should-Cost Anchoring: Use AI-enabled cost modeling platforms, such as aPriori or Fictiv, to independently estimate manufacturing or service delivery costs. These tools ingest CAD files, process logic, and regional labor indices to simulate what the part should cost, before AI premiums are applied.
Performance-Linked Premiums: Tie any “intelligence uplift” in pricing to measurable outcomes. For example, faster delivery or defect reduction should be contractually benchmarked. If promised gains don’t materialize, the premium doesn’t stick.
Scenario Testing with Digital Twins: Use sourcing simulations to model how AI-driven supplier offerings perform under stress. Procurement twins can test whether promised cost savings hold when demand surges, transport delays hit, or tariffs change, turning opaque promises into validated performance predictions.
From AI Fluency to AI Governance In Sourcing
The promise of AI in supplier operations is real, but it shouldn’t become a margin veil. As AI becomes a default part of supplier positioning, procurement must evolve from passive recipients of tech-laced bids to active validators of performance claims. This doesn’t mean rejecting innovation, it means pricing it properly.
What separates leading CPOs today isn’t their openness to AI, but their ability to distinguish between substance and markup. By embedding technical fluency, cost simulation, and supplier accountability into sourcing workflows, procurement can ensure that AI uplifts aren’t just accepted, but earned.