Most of what I write focuses on mid-size and larger manufacturers. The case studies, the implementations, the success stories—they often involve companies with hundreds of employees and dedicated IT staff.

But what about smaller manufacturers? The shop with 30 employees? The family business with $5 million in revenue?

AI is relevant here too, though the approach needs to be different.

The small manufacturer reality

Let’s acknowledge the constraints:

Limited capital: You can’t spend $500,000 on an AI platform. Investments need to be smaller and quicker to pay back.

No dedicated technical staff: The owner wears multiple hats. There’s no IT department, let alone data scientists.

Different risk profile: A failed project hurts more when you’re smaller. You can’t absorb $100,000 mistakes.

Time pressure: Nobody has spare time to manage complex implementations.

These are real. AI approaches that ignore them will fail.

But smaller manufacturers also have advantages:

Faster decisions: You can try things without committee approval.

Closer to operations: The decision-maker knows every machine and process intimately.

Simpler systems: Fewer legacy systems to integrate with.

Practical AI starting points

Off-the-shelf AI tools

The most accessible AI for small manufacturers is embedded in tools you might already use:

Accounting software: Xero and MYOB increasingly include AI features—anomaly detection, cash flow forecasting, expense categorisation.

Inventory management: Modern systems have demand forecasting built in.

CRM and sales tools: AI features for lead scoring, email suggestions, customer insights.

General productivity: Microsoft 365 and Google Workspace include AI assistants.

These aren’t manufacturing-specific, but they address common business functions.

Cost: Often included in subscriptions you’re already paying.

Effort: Minimal—turn on features and learn to use them.

Cloud-based manufacturing solutions

Some cloud platforms offer AI capabilities designed for smaller manufacturers:

MES/quality platforms: Systems like Tulip, Factbird, or Guidewheel provide production monitoring with AI analytics, without massive infrastructure investment.

Maintenance management: Platforms like Fiix or UpKeep include basic predictive maintenance capabilities.

Energy monitoring: Services like Greensense or Watteo offer AI-powered energy insights.

Cost: Usually $500-$2,000/month depending on scope.

Effort: Implementation takes weeks, not months. Often self-service or light-touch vendor support.

AI-enhanced machine tools

If you’re buying new equipment, look for AI capabilities built in.

Modern CNC machines, laser cutters, and other equipment increasingly include:

• Predictive maintenance features
• Process optimisation
• Quality monitoring

You get AI capabilities through the equipment purchase rather than a separate project.

Specific service engagements

Rather than building AI capability internally, engage specialists for specific outcomes:

Data analysis: Have an analyst review your production data and identify patterns. This is consulting, not technology—but it delivers AI-like insights.

Process improvement: Engage experts who use AI tools to analyse and improve your processes.

Energy audits: Services that analyse energy data using AI to identify savings.

Cost: Project-based—$5,000-$50,000 depending on scope.

Effort: You provide access and time; they do the work.

Computer vision for quality

Off-the-shelf vision systems are increasingly accessible:

Simple inspection stations: Pre-configured camera systems for specific inspection tasks. Some are under $20,000.

Smartphone-based: Some applications use smartphone cameras with AI apps for simple inspections.

Vision service providers: Companies that will set up and manage vision inspection for you.

If you have manual quality inspection consuming significant time, vision AI may be accessible.

What to avoid

Custom AI development

Building custom AI models requires skills, data, and ongoing maintenance that most small manufacturers don’t have.

Unless you have a unique problem that off-the-shelf solutions can’t address, custom development isn’t the right path.

Enterprise platforms

SAP, Oracle, and similar enterprise systems with AI capabilities aren’t designed for small manufacturers. The cost, complexity, and implementation requirements don’t fit.

Point solutions that require integration

AI solutions that require significant integration with your existing systems will struggle without technical resources to do that integration.

Look for standalone solutions or those designed for common platforms (QuickBooks, Xero, common ERP systems).

Bleeding edge technology

Let larger companies be the early adopters. Wait for technologies to mature and simplify before investing.

A realistic implementation path

Phase 1: Embedded AI

Use AI features in tools you already have. Accounting software, inventory systems, productivity tools.

Time: A few hours to explore and enable features. Cost: Minimal (you’re already paying for the software).

Phase 2: Cloud monitoring

Add cloud-based monitoring for a specific pain point—production visibility, energy management, maintenance tracking.

Time: A few weeks to implement. Cost: $500-$2,000/month.

Phase 3: Specific projects

Engage specialists for focused projects—data analysis, process improvement, vision inspection for a specific problem.

Time: Project-dependent, typically 1-3 months. Cost: $10,000-$50,000 per project.

Phase 4: Strategic capability

As the business grows and AI proves value, consider more substantial investments in AI capability.

This might include dedicated technology staff, deeper analytics, or more comprehensive systems.

Most small manufacturers don’t need to reach Phase 4. Phases 1-3 deliver meaningful value without building internal AI capability.

Finding help

Smaller manufacturers often need help navigating AI options. Where to find it:

Industry associations: Ai Group, local manufacturing groups, and industry-specific associations often provide guidance and connections.

TAFE and university connections: Some offer SME engagement programs, student projects, or affordable consulting.

Government programs: Various programs support SME technology adoption. Worth investigating what’s available.

Consultants: Firms like Team400 work with manufacturers of various sizes. Even a few hours of consultation can help you identify the right starting points.

Technology vendors: Vendors of manufacturing software can advise on their AI capabilities (though remember their advice isn’t independent).

The real opportunity

Small manufacturers can be nimble. While larger competitors navigate internal bureaucracy, you can try something and see if it works.

AI doesn’t have to be a massive transformation project. It can be a series of small improvements that compound over time.

A bit better inventory management. Slightly fewer quality issues. Some energy savings. Faster quotes. Less manual data entry.

None of these is revolutionary alone. Together, they make a more efficient, competitive business.

Start small. Learn. Expand what works.

That’s a realistic AI strategy for manufacturers without deep pockets or technology teams.

If you’re a smaller manufacturer wondering where to start, even a brief consultation with AI consultants Brisbane or similar specialists can help you identify practical first steps. You don’t need a grand AI strategy. You need a good next step.

The good news: you can probably afford to take it.