Manufacturing Part 1: AI Adoption and Business Strategy

03.23.2026

Manufacturing Part 1 AI Adoption and Business Strategy.png The “AI Adoption” Series: An Overview

This six-part series is designed for the owners, COOs, and operational leaders of Small to Mid-sized Business (SMB) manufacturing firms. We are moving past the theoretical “Factory of the Future” to discuss the practical “Factory of Right Now.”

Over the next six articles, we will trace the adoption of AI from a boardroom concept to a shop-floor reality:

The Strategic Roadmap (This Article): Defining the business outcomes that AI must serve (Yield, OEE, Throughput).
Subordinate Strategies: Aligning IT (OT/IT convergence), HR (The Connected Worker), and Operations.
The Data Foundation: Moving from clipboards and siloed PLCs to a unified data layer.
Analytics & Machine Learning: Predictive maintenance and quality control.
Automation & Efficiency: Using AI to drive robotics and optimize scheduling.
The Feedback Loop: Using AI to refine production planning and supply chain resilience.

The Industry Landscape: The “Squeeze” on SMBs

Before discussing strategy, we must ground ourselves in the current reality. SMB manufacturers are currently squeezed between a structural labor shortage and rising operational costs.

The Labor Gap: The U.S. manufacturing sector faces a structural workforce deficit. The National Association of Manufacturers (NAM) projects 2.1 million unfilled jobs by 2030. In 2025, the average manufacturer reports roughly 4.2% of roles remain unfilled, constraining growth.
The “Silver Tsunami”: As older master machinists retire, they take decades of “tribal knowledge” with them. Replacing this experience with entry-level hires is impossible without a technology bridge.
The Adoption Surge: Your competitors are moving. Investment in AI among SMBs surged to 57% in 2025, up from just 36% in 2023. The “wait and see” era is over.

Why This Matters Now:

For an SMB manufacturer, you cannot win on scale against global giants, and you cannot win on cheap labor against overseas competitors. You must win on Agility and Precision. AI is the only mechanism that allows a 50-person shop to quote as accurately and produce as efficiently as a 5,000-person enterprise.

The Strategy Template: Outcomes First, AI Second

The most common failure mode in manufacturing AI is “Pilot Purgatory”—installing sensors that collect data no one uses.

What we are providing here is a strategy template. You must fill this with the specific outcomes your plant needs to survive.

For SMB Manufacturing leaders, this template focuses on three practical buckets:

Asset Maximization (OEE): Using AI to ensure your expensive machines are running at optimal speed and quality, reducing unplanned downtime.
Labor Augmentation: Using AI to let one operator manage three machines instead of one, or to help a novice welder perform like a journeyman.
Supply Chain Resilience: Using AI to predict material delays and adjust production schedules automatically, preventing “starved” lines.

The Strategic Underpinnings

To execute this effectively, your strategy template must rely on five specific pillars.

1. Strategy Includes Governance (Safety First)

In manufacturing, a bad AI model doesn’t just lose money; it can hurt someone. If an AI controls a robotic arm or a high-speed press, the governance framework for safety limits must be part of the initial strategy. You must define the “Hard Stops” that AI cannot override.

2. Governance is Finalized Last

While safety is non-negotiable, the specific data governance evolves. You cannot write a policy for “Camera-based Quality Control” until you know if you are using thermal cameras or visual spectrum cameras. The strategy must allow for evolving rules as you select specific hardware.

3. The Goal is Better Outcomes

Every initiative must track back to the P&L. If you implement a “Predictive Maintenance” system but it costs more in software fees than the downtime it prevents, it is a strategic failure. The goal is Lower Cost per Unit.

4. KPIs & OKRs Are Essential

You cannot manage what you do not measure.

Bad Metric: “Number of IoT sensors installed.”
Good Metric: “Reduction in unplanned downtime hours” or “Percentage decrease in scrap rate.”

5. Execution is Essential

The strategy must be doable on a factory floor. If the solution requires an operator to remove their gloves and type on a delicate iPad every 10 minutes, it will fail. Execution means “robust, simple, and shop-floor ready.”

The Direction: From Reactive to Predictive

The manufacturing industry is undergoing a fundamental shift in how it handles problems.

Current State (Reactive): The machine breaks. We fix it. The schedule is delayed. We call the customer to apologize.
Future State (Predictive): The machine vibrates abnormally. The AI alerts maintenance to replace a bearing during the next shift change. Production continues uninterrupted.

The Strategic Shift:

The shift is from managing chaos to managing capacity. You stop fighting fires and start optimizing flow.

Next Step: Defining Your Subordinate Strategies

You now have a business strategy template focused on outcomes (e.g., “Improve OEE by 15%”). The next step is to determine how your IT (Information Technology) and OT (Operational Technology) teams must merge to support that goal.

In Manufacturing Part 2, we will break down these Subordinate Strategies—specifically, how to align your workforce, your legacy machines, and your digital infrastructure to execute the business mandate.

Salvatore Magnone is a father, veteran, and a co-founder, a repeat offender in the best way in fact, and a long-time collaborator at DOOR3. Sal builds successful, multinational, technology companies and runs obstacle courses. He teaches business and military strategy at the university level and directly to entrepreneurs and military leaders.

https://www.linkedin.com/in/salmagnone/