Build the Business Case for Manufacturing AI. Start with One Operational Problem.

How focused investigations and measurable results help manufacturers make better investment decisions.

Recurring downtime, inconsistent changeovers, and unexplained quality losses give manufacturing teams a concrete starting point for evaluating AI. Each connects technology investment to a problem the business already recognizes.

A manufacturing AI business case should explain the problem, quantify the improvement opportunity, account for implementation costs, and define how results will be measured.

Two customer examples illustrate how evidence supports investment: a process manufacturer quantified hidden capacity before investing in a broader data foundation, while a global electronics manufacturer demonstrated financial returns at one facility before expanding.

Choose an issue tied to a priority such as capacity, quality, cost, or delivery. Establish a baseline and identify who can act on the findings.

In guidance published by the National Institute of Standards and Technology (NIST), researcher Michael Sharp explains:

“The successful impact of using an AI-enhanced product should result in benefits in terms of system-level performance or financial measures.”

That gives the business case a practical focus: define what better performance would look like, then determine how AI could help achieve it.

Manufacturer: Process manufacturer
Scope: One production line
Evidence: Identified and modeled opportunity

The problem

Frequent changeovers consumed valuable production time. Total duration was tracked, but the team lacked visibility into the individual activities contributing to variation.

The approach

The process manufacturer worked with Arch Systems to connect existing manufacturing signals and reconstruct changeover activities in operational context. AI-supported analysis compared changeovers to identify the activities contributing to recurring delays and opportunities for targeted improvement.

The quantified opportunity

The investment decision

The pilot supported the manufacturer’s decision to build a broader, trusted manufacturing data foundation for future analytics and AI use cases.

Business case lesson: A focused investigation can quantify an opportunity and establish a reason for further investment before financial returns have been demonstrated.

Manufacturer: Global electronics manufacturer
Scope: Seven surface mount technology (SMT) lines at a lighthouse facility
Evidence: Reported operational and financial results

The problem

Fragmented machine data and inconsistent downtime tracking made it difficult to improve overall equipment effectiveness (OEE) and establish a repeatable approach across facilities.

The approach

The lighthouse facility for global electronics manufacturer connected equipment, standardized downtime data, embedded performance analytics into daily operations, and integrated operational workflows.

The reported results

The expansion decision

The lighthouse deployment became a model for broader rollout. The manufacturer subsequently expanded across multiple facilities, with the case study reporting more than $750,000 in annual savings across its global SMT network.

These results reflect the combined data foundation, analytics, and operational workflows described in the deployment.

Business case lesson: Demonstrated returns and a repeatable operating model provide evidence for expansion.

Recovered time creates a capacity opportunity. Its financial value depends on demand, whether the affected line constrains throughput, and whether downstream operations can accommodate additional output.

Other improvements require different calculations. Lower scrap may reduce material costs. Faster investigations may free engineering time. Document the assumptions behind each estimate and update them as measured results become available.

Include the work needed to achieve the benefit: connectivity, data preparation, integration, training, and ongoing support.

This makes the business case reviewable and helps teams distinguish potential value from documented returns.

Before starting a pilot, define what evidence will justify continuing, adjusting, or expanding the project.

Research published by the World Economic Forum connects successful scaling with maturity across processes such as quality, maintenance, and workforce development. Repeatable execution matters alongside technology.

A practical business case should capture:

• Baseline: What is happening today?
• Expected benefit: What improvement could create value?
• Total cost: What will implementation and ongoing operation require?
• Operational owner: Who will test changes and sustain adoption?
• Success measures: What evidence will support the next investment?

The next investment depends on your current capabilities. It may involve connecting equipment, creating consistent operational context, applying AI, or expanding an approach already delivering results.

Manufacturers need to answer three questions:

• Where are we today?
• What are we trying to improve?
• What do we need next?

Plan your next move in manufacturing AI. Use the data maturity online assessment to identify priorities for your next investment.

What should a manufacturing AI business case include?

Include the operational problem, baseline, expected improvement, financial assumptions, implementation costs, ownership, and success measures. Define the evidence needed to justify further investment.

How should manufacturers choose their first AI use case?

Choose a recurring problem with meaningful business impact, relevant data, and a team able to act on the findings. A focused scope makes investigation, testing, and measurement more manageable.

What is the difference between modeled opportunity and demonstrated ROI?

Modeled opportunity estimates potential value using assumptions about improvement and business impact. Demonstrated ROI compares documented benefits with investment costs over a defined period. A capacity estimate alone does not establish ROI.

Which costs should manufacturers include?

Include software, connectivity, integration, data preparation, internal staff time, training, and ongoing support. Separate initial costs from recurring costs and use a consistent evaluation period.

When should a pilot lead to broader deployment?

Further investment may be justified by a credible opportunity and a feasible improvement plan. Broader rollout should also consider sustained results, adoption, and whether the approach can be repeated under conditions at other lines or sites.