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.
The Standard Work Challenge
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.
What Standard Work AI Verification Does
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.
How Cost Savings Take Shape
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
- Right First Time: Through AI verification, you're assured that each task is executed correctly. Verification records and audit trails ensure accountability across every shift and facility. The alternative — telling people through email, written instructions, or a conversation — is highly error-prone.
- Done consistently everywhere: Smart Videos and Smart Instructions, which are used as part of the process, define the gold standard for execution. Even the most experienced operators often develop their own ways of working, which can vary across plants. By using Smart Videos and Smart Instructions within the process, operators are guided to use the correct practice. The result is fewer errors, less rework, and safer operations.
Reducing Unplanned Downtime
- Faster Issue Resolution: When equipment fails or processes stall, time is money. Frontline teams equipped with videos and step-by-step instructions have the knowledge they need in the moment of work. They no longer need to wait for the one SME who may be off-site, not on the shift, or on vacation. Issues are diagnosed and resolved more quickly, preventing extended stoppages and reducing the costs of unplanned downtime.
- Early Warning: When an issue is found that causes downtime, tasks can be sent out to all sites as a precaution. Preventative measures can be taken to keep the machines running, which may have otherwise stopped.
Onboarding and Retention
- Higher Proficiency | Better Retention: A supported workforce is a stable workforce. When new hires can access knowledge directly on the floor, they learn more quickly, retain information longer, and gain confidence sooner. This reduces the stress and frustration that often drive turnover. Additionally, when tasks provide immediate feedback, operators see what they did wrong and correct it on the spot. A centralized view of this allows operators to be assigned a skill, along with video instructions, to improve for the next time they perform tasks.
- Reinvestment: Shorter onboarding cycles, stronger retention, and higher operator capability all contribute to a measurable financial impact. Savings from lower recruiting and retraining costs can be reinvested into high-value priorities such as advanced equipment, process improvements, or competitive wages. Over time, this approach creates a more resilient and engaged workforce, while also improving the plant's overall efficiency.
Modeling The Financial Impact
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:
- Direct labor ≈ 10% of revenue (U.S. Census Bureau and Deloitte Smart Factory)
- Quality costs ≈ 5% of revenue (ASQ: Cost of Poor Quality)
- Unplanned downtime ≈ 3% of revenue (Aberdeen Group, Deloitte Smart Factory)
- Training/onboarding ≈ 5% of labor costs (ATD benchmarks)
For a $500M manufacturer, as a benchmark, this would equate to the following annual costs:
- Labor: $50M
- Quality Rework: $25M
- Unplanned downtime: $15M
- Onboarding and upskilling: $2.5M
With these assumptions, we have calculated both low-end and high-end cost savings.
Low-End Cost Savings Assumptions
- SME/operator time savings: 5% of $50M = $2.5M
- Quality defects reduction: 2.5% of $25M = $0.63M
- Unplanned downtime reduction: 5% of $15M = $0.75M
- Onboarding and retention: 15% of $2.5M = $0.38M
- Total = $4.26M
High-End Cost Savings Assumptions
- SME/operator time savings: 10% of $50M = $5.0M
- Quality defects reduction: 5% of $25M = $1.25M
- Unplanned downtime reduction: 15% of $15M = $2.25M
- Onboarding and retention: 30% of $2.5M = $0.75M
- Total = $9.25M
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.
The Measurable Opportunity
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.
Curious about applying AI to standard work? Schedule a conversation with an expert.
