Back to Blog

Overcoming AI Adoption Barriers in Traditional Industries

September 17, 2025•5 min read•Team 400

"We're not a tech company."

I hear this from executives in manufacturing, construction, agriculture, and logistics. It's often said as a reason why AI won't work for them.

But here's the thing: some of the best AI ROI we've seen is in traditional industries. The Coast Smoke Alarms scheduling project delivered dramatic efficiency gains. Not because they were a tech company—they're a field service business—but because they approached it right.

Here's how to overcome AI adoption barriers in industries that don't think of themselves as "tech."

Understanding the Barriers

Barrier 1: "We Don't Have the Data"

What they mean: Our data is in spreadsheets, paper forms, or people's heads. We're not Google.

The reality: You probably have more useful data than you think. It's just not organised. And AI often doesn't need as much data as people assume.

How to overcome:

  • Audit what data actually exists (it's usually more than expected)
  • Start with processes that generate structured data
  • Accept that some data collection might be needed
  • Prove value with available data before investing in data infrastructure

Barrier 2: "Our Processes Are Too Complex"

What they mean: What we do requires human judgment. You can't automate craft and experience.

The reality: AI isn't about replacing judgment—it's about handling the routine parts so humans can focus judgment where it matters.

How to overcome:

  • Identify the repetitive parts within complex processes
  • Position AI as augmentation, not replacement
  • Start with lower-judgment tasks and build trust
  • Involve experienced workers in design (their knowledge is valuable)

Barrier 3: "Our People Won't Accept It"

What they mean: Workers fear job loss. Managers distrust new technology. Union concerns.

The reality: Fear is real and valid. Handled wrong, AI projects do fail due to resistance.

How to overcome:

  • Be honest about the goal (efficiency, not headcount reduction—if that's true)
  • Involve workers early and often
  • Train on how to use, not just that it's coming
  • Show "what's in it for me" at every level
  • Address job concerns directly (redeployment, upskilling, natural attrition)

Barrier 4: "We Can't Afford to Fail"

What they mean: Our operations are critical. Downtime costs money. Errors have consequences.

The reality: This is legitimate. But it argues for careful implementation, not no implementation.

How to overcome:

  • Start in non-critical areas (back office before shop floor)
  • Run parallel before cutover
  • Design for graceful degradation
  • Build human override into everything
  • Measure carefully before expanding

Barrier 5: "The Technology Keeps Changing"

What they mean: We invested in [previous technology] and it's already obsolete. Why would AI be different?

The reality: The pace of change is real. But waiting for stability means waiting forever.

How to overcome:

  • Focus on problems, not specific technologies
  • Build for flexibility (modular, loosely coupled)
  • Accept that iteration is necessary
  • Start small enough that pivoting is affordable

Industry-Specific Approaches

Manufacturing

Good starting points:

  • Predictive maintenance (clear ROI, existing sensor data)
  • Quality inspection (computer vision on existing cameras)
  • Production scheduling (optimisation with clear metrics)
  • Document automation (work orders, compliance, shipping)

Watch out for:

  • Don't touch the production line until you've built trust elsewhere
  • Integrate with existing OT systems carefully
  • Factory floor workers need ruggedised, simple interfaces

Construction

Good starting points:

  • Project documentation (AI-powered document search and extraction)
  • Safety compliance (incident analysis, hazard identification)
  • Scheduling optimisation (complex resource allocation)
  • Estimating support (historical data analysis)

Watch out for:

  • Site connectivity challenges
  • High worker turnover means constant training
  • Union considerations for workflow changes

Agriculture

Good starting points:

  • Yield prediction and planning
  • Supply chain coordination
  • Compliance documentation
  • Equipment monitoring

Watch out for:

  • Seasonal rhythms (can't deploy during harvest)
  • Rural connectivity
  • Cost sensitivity in tight-margin operations

Logistics and Transport

Good starting points:

  • Route optimisation
  • Demand forecasting
  • Customer service automation
  • Compliance monitoring

Watch out for:

  • Driver adoption is critical and challenging
  • Integration with logistics platforms
  • Real-time requirements add complexity

The Adoption Playbook

Phase 1: Find the Believers

Don't try to convert the skeptics first. Find the people who are curious:

  • The manager frustrated with manual processes
  • The worker who already uses personal tech tools
  • The executive who's seen competitors move

Start there. Build success. Let it spread.

Phase 2: Pick the Right First Project

Criteria for first AI project in traditional industry:

  • High pain, low risk: Problems everyone hates, failures won't be catastrophic
  • Measurable outcome: Clear before/after comparison
  • Contained scope: Can succeed in 3 months
  • Visible results: People can see and feel the difference

Don't start with the CEO's pet project. Start with something that builds credibility.

Phase 3: Involve Workers Meaningfully

Not just "tell them what's happening." Actually involve them:

  • Shadow current processes (learn from them)
  • Get input on design (they know what won't work)
  • Test with real users (not just managers)
  • Credit contributions (they built this too)

The people doing the work know more than you think. Tap that knowledge.

Phase 4: Communicate Relentlessly

Every phase, communicate:

  • What's happening and why
  • What the results are showing
  • What's changing (and what isn't)
  • What's next

Traditional industries often have strong rumor mills. Get ahead of them with facts.

Phase 5: Prove ROI and Expand

Document results rigorously:

  • Time saved
  • Errors reduced
  • Costs avoided
  • Worker feedback

Use first project success to justify second project. Build momentum.

The Human Side

Technical implementation is maybe 40% of the challenge. The rest is human:

Leadership commitment: Executive support isn't enough. They need to visibly champion it.

Middle management buy-in: These are the people who can kill adoption through benign neglect. Get them invested.

Worker confidence: People need to believe they can learn new tools. Many in traditional industries have been told they're "not tech people." Counter that narrative.

Cultural patience: Traditional industries often have cultures that value stability and skepticism of fads. Respect that. Prove value, don't promise it.

Our Approach

We've worked with businesses in traditional industries on AI adoption. Our approach:

  • Start with business problems, not AI solutions
  • Involve workers from day one
  • Build for the real environment (connectivity, devices, skills)
  • Prove value quickly with contained scope
  • Expand based on evidence

Not every project is right for AI. We'll tell you if yours isn't.

Talk to us about your industry's AI challenges.