Manufacturing AI: Where to Start? A Decision Framework

“Where should we start with AI?”

It’s the question I get asked most often by manufacturing leaders. They know AI has potential. They’ve seen the case studies. But translating general opportunity into specific action for their particular factory is where things get stuck.

Here’s a framework to help you identify your best starting point.

Step 1: Assess your readiness

Before choosing an application, honestly assess where you stand.

Data infrastructure

Can you answer yes to these questions?

  • Do you collect production data systematically (not just in spreadsheets)?
  • Can you track individual batches or products through your process?
  • Is equipment status captured digitally?
  • Can you access historical data for analysis?

If most answers are no, your first step is data infrastructure, not AI applications. Implementing AI without data is like trying to cook without ingredients.

Technical capability

Do you have people who can:

  • Manage data systems and integrations?
  • Understand basic statistics and data analysis?
  • Interface between IT/OT and operations?
  • Manage vendor relationships for technical projects?

If these skills are absent, you need to build or acquire them before complex AI projects.

Organisational readiness

Does your organisation:

  • Have executive support for technology investment?
  • Allow time and resources for improvement projects?
  • Successfully implement technology changes?
  • Maintain improvements over time?

Organisations that struggle with basic change management will struggle with AI regardless of the technology.

If you’re not ready

If your readiness assessment reveals gaps, that’s not failure—it’s information.

Data gaps: Invest in basic data collection before AI. This foundation enables everything that follows.

Skill gaps: Consider training, hiring, or partnering to build capability.

Organisational gaps: Address change management and sponsorship issues first.

Starting AI projects before you’re ready is a recipe for failure and disillusionment.

Step 2: Identify your biggest pain points

If you’re ready for AI, the next question is where to apply it. Start with pain points rather than technology.

Quality problems

  • High scrap or rework rates?
  • Inconsistent quality despite same specifications?
  • Defects that escape to customers?
  • Quality that depends too much on specific operators?

AI direction: Quality prediction, process optimisation, inspection automation

Equipment reliability issues

  • Unexpected breakdowns disrupting production?
  • High maintenance costs from reactive repairs?
  • Critical equipment that “just decides” to fail?
  • Uncertainty about equipment condition?

AI direction: Predictive maintenance, condition monitoring, remaining useful life prediction

Operational efficiency challenges

  • Scheduling that’s chaotic or suboptimal?
  • Energy costs that seem too high?
  • Throughput limited by bottlenecks that are hard to understand?
  • Production that varies significantly between shifts/operators?

AI direction: Scheduling optimisation, energy management, process analytics, knowledge capture

Labour and skills issues

  • Can’t find or keep skilled workers?
  • Too much depends on a few experienced people?
  • Training takes too long?
  • Operators overwhelmed by complexity?

AI direction: Decision support systems, knowledge assistants, training tools, workforce augmentation

Supply chain challenges

  • Forecasting that’s consistently wrong?
  • Inventory that’s either too high or stocked out?
  • Supplier problems you learn about too late?
  • Logistics costs that are hard to optimise?

AI direction: Demand forecasting, inventory optimisation, supplier monitoring, logistics planning

Step 3: Prioritise based on value and feasibility

You probably identified multiple pain points. Now prioritise.

Value assessment

For each pain point, estimate:

  • What does this problem cost annually? (Be specific: downtime costs, scrap costs, labour costs, opportunity costs)
  • If AI solved 30-50% of this problem, what would that be worth?
  • Is this a growing or stable problem?

Feasibility assessment

For each potential application, assess:

  • Do you have the data needed?
  • Is this a proven AI application or experimental?
  • Are vendors/solutions available?
  • How complex is implementation?
  • What skills are required?

The prioritisation matrix

Map your options on two axes: value (high/low) and feasibility (easy/hard).

High value, easier feasibility: Start here. These are your quick wins that also matter.

High value, harder feasibility: Plan for these as second-phase projects.

Lower value, easier feasibility: Maybe pursue if resources allow.

Lower value, harder feasibility: Avoid until you’ve exhausted better options.

Step 4: Common starting points that work

Based on the framework above, here’s where most manufacturers should start:

If quality is your biggest pain: Start with process analytics

Before automated inspection, understand your process better. Analyse existing quality and process data to identify correlations. This often reveals improvement opportunities that don’t require AI at all—and builds the foundation for more sophisticated approaches.

Investment: $20,000-50,000 for analysis and basic tooling Timeline: 3-6 months to insights

If equipment reliability is critical: Start with condition monitoring

Add basic monitoring to your most critical equipment. Don’t jump to AI predictions—start by capturing and visualising condition data. Human engineers often spot patterns that inform both maintenance practices and later AI development.

Investment: $30,000-100,000 depending on scope Timeline: 3-6 months to operational monitoring

If efficiency is the priority: Start with energy management

Energy optimisation often has the fastest, clearest ROI. Install monitoring, understand consumption patterns, identify waste. The path from data to savings is usually straightforward.

Investment: $20,000-80,000 depending on facility size Timeline: 3-6 months to initial savings

If skills are the constraint: Start with knowledge capture

Document the knowledge in experienced workers’ heads before they leave. This might be low-tech (structured documentation) or AI-assisted (knowledge bases with natural language interfaces).

Investment: $10,000-50,000 depending on approach Timeline: Ongoing, with early value in 2-3 months

Step 5: Make it happen

Once you’ve chosen a starting point:

Define success clearly

What specific outcomes will you measure? What numbers indicate success? Without clear metrics, projects drift.

Find your champion

Someone internal who owns the project’s success and will push through obstacles.

Start small

Resist the urge to expand scope. Prove the concept in a limited area first.

Plan for learning

Your first AI project will teach you things. Build in time to learn and adjust.

Budget realistically

Include internal effort, not just vendor costs. Implementation is never free.

A final thought

There’s no universally correct starting point. The right answer depends on your specific situation—your problems, your data, your people, your resources.

This framework helps you think through that situation systematically rather than chasing whatever AI application is trendy.

The worst starting point is “we should do AI” without connection to business problems. The best starting point is a specific, painful, valuable problem where AI can genuinely help.

Find that problem. Start there. Learn. Expand.

That’s how manufacturing AI succeeds.