Manufacturers can improve profitability by achieving a margin lift of 85 to 185 basis points by scaling standardized work processes through the use of AI. For a $500 million manufacturer, that margin lift translates into $4.3 to $9.3 million in annual savings by reclaiming SME and operator time, reducing quality losses, lowering downtime, and accelerating onboarding.
Standard work is designed to ensure that every task is performed correctly, every time. Yet leaders struggle to verify that operators consistently follow procedures. Even the best operators make mistakes, yet spot checks and audits cover only a fraction of the real work.
As a consequence, manufacturers face wasted SME bandwidth, lost operator time, inconsistent execution, and costly rework.
An assignor can create a task that includes multimodal instructions with video support, required equipment, PPE, and safety warnings for each step. The operator receives the task through the app in a format that is easy to follow and understand, completes the work, and submits photo or video evidence as proof. AI checks the execution against the defined standard, gives immediate feedback, and logs each step in a complete audit trail.
The alternative is to rely on a SME to do the work themselves or have someone manually check each operator. Both are slow, resource-intensive, and take experts away from more valuable priorities. Standard Work AI Verification alleviates this burden by automating verification, ensuring consistency, scalability, and reduced dependence on SME oversight.
Because performance is tied to each operator, managers can assign skills, attach instructions, and require a quiz or additional training if gaps show up. Operators can practice, get feedback, and build confidence through repeated validation, turning standard work into a process that is measurable, trackable, and easy to improve over time.
Leaders understand that improving labor operations drives performance, yet it is often challenging to connect these improvements to measurable outcomes. By combining structured processes with AI-driven validation, Standard Work AI Verification directly addresses the hidden costs that erode efficiency, quality, and workforce stability. Below explains how cost saving start to emerge from the use of DeepHow. These are not all the ways, but a sampling.
Quality Impacts
Reducing Unplanned Downtime
Onboarding and Retention
Standard Work AI Verification delivers measurable improvements in operations and efficiency. In this section, we modeled out the possible gains delivered by the improvements mentioned above. Every manufacturer is different, and there are significant variations in cost depending on the industry and size. However, for this evaluation, we employed conservative measures rooted in industry benchmarks from Deloitte, McKinsey, ASQ, the Aberdeen Group, and the Association for Talent Development.
For building out the savings, assume the following:
For a $500M manufacturer, as a benchmark, this would equate to the following annual costs:
With these assumptions, we have calculated both low-end and high-end cost savings.
Low-End Cost Savings Assumptions
High-End Cost Savings Assumptions
The assumptions for savings are also conservative. Many of our clients have reported real-world results that exceed these benchmarks. Multiple manufacturers have reported reducing onboarding time by 30%. Anheuser-Busch InBev achieved even greater results, cutting onboarding time by nearly 80% across several facilities. Other companies report significant time and expense savings, such as reducing a 5-hour process to 46 minutes, keeping 3rd shift lines running when SMEs aren't present, and no longer requiring expert operators to travel to every facility in a region to perform maintenance on a machine.
Below is a table visualization of the savings and margin impact for a $500M manufacturer and a 10% margin baseline.
Standard Work AI Verification proves that every task is completed correctly and enables standardization at scale. Operators receive clear instructions, submit evidence of their work, and get immediate feedback, while managers gain verifiable proof and a complete audit trail. The outcome is quantifiable: millions in annual savings, margin lifts of up to 185 basis points, and consistency across teams and facilities.
The primary advantage is the recovery of SME time and ensuring ongoing production. Instead of spending hours retraining or checking work, experts can focus on improving processes and spreading best practices. For manufacturers, this creates a direct path to increased efficiency, margins, and profitability.
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