Most glazing businesses are starting to dabble with AI—whether it’s automating quotes, streamlining delivery routes, or analysing customer feedback. But as helpful as it is, AI can bring up a tricky question: is it fair?

When machines play a role in hiring your next team member, pricing a job, or choosing which supplier to order from, fairness suddenly becomes very real. After all, you’d want an installer to be picked based on skill and availability, not because the AI automatically favours early shifts or bigger projects. So, what does fairness really mean when you bring AI into your business—and how do you keep it in check?

Understanding AI Fairness in Glazing

Fairness in AI simply means making sure that automated decisions treat everyone equally—and reasonably. In the glazing world, that could mean:

• Equitable scheduling: AI platforms that choose which jobs go to which teams should balance workloads fairly—no favouritism or unconscious bias toward specific installers or routes.
• Unbiased material sourcing: Procurement tools that suggest suppliers shouldn’t lean heavily toward preferred vendors unless there’s clear, data-backed reasons like price or quality.
• Inclusive hiring: When using AI-assisted recruitment platforms, it’s important they don’t unintentionally rule out great candidates based on irrelevant info like postcode or education history.

Many glazing businesses, particularly smaller ones, may not realise that AI tools can reflect unintentional preferences baked into the data they’re trained on. That’s why fairness matters—even the most helpful automation needs a bit of human oversight to ensure it’s treating people right.

How Bias Creeps Into Glazing AI Systems

Here’s the thing: AI learns from past data. If the data has bias, so will the AI. In glazing, this can show up in unexpected ways:

• Historical data: If your sales team has mostly closed deals with large commercial clients, your pricing tool might start prioritising those types of clients—leaving one-off homeowners or small builders out in the cold.
• Poorly defined objectives: Let’s say an AI tool is built to prioritise ‘fast installations’. Without context, it might keep assigning jobs to only the fastest team—overloading them and underusing others.
• Lack of data diversity: If your AI hasn’t ‘seen’ enough variety—be it project types, team members, or job locations—it can struggle to make fair decisions across the board.

This doesn’t mean you shouldn’t use AI. It just means the results are only as good as the thinking behind them. Bias doesn’t have to be intentional to cause real problems.

The Impact of Unfair AI on Glazing Teams and Clients

When AI gets it wrong, it’s not just a technical glitch—it can affect real people in your business. Here’s how:

• Installer morale: If certain team members always get the toughest jobs or the worst time slots, resentment builds. That’s not just bad for morale—it leads to higher staff turnover.
• Customer experience: Think of a scheduling AI that deprioritises smaller, domestic jobs. Those customers get longer wait times and patchier service, all without realising a tool was ranking them lower from the start.
• Regulatory risks: In hiring, if AI-based screening unknowingly favours (or ignores) certain demographics, you could end up breaching employment laws—especially as regulations around AI fairness grow stronger.

Fairness isn’t just a moral issue here—it directly influences your team’s performance, customer satisfaction, and your business’s legal footing.

Can Machines Be Trained to Be Fair?

Good news: we can build fairness into AI—it’s not just something that ‘happens’ by luck. There are techniques and tools specifically designed to reduce bias in AI systems, including:

• Data anonymisation: Removing personally identifiable details so hiring decisions are based on merit, not hidden bias.
• Algorithm auditing: Regularly reviewing how AI arrives at a decision—such as why a specific supplier keeps getting chosen by your procurement tool.
• Fairness indicators: These track outcomes to highlight potential inequalities, like whether your scheduling tool is consistently favouring certain postcodes or team members.

For example, if you’re using an AI-based estimator that always suggests higher markups for small domestic jobs than commercial ones, you can review how that decision is being made and adjust the rules it’s working with. It’s like teaching a new apprentice—you correct technique before bad habits form.

Strategies to Promote AI Fairness in Your Glazing Business

If you want to use AI but ensure it aligns with your business values, here are some practical ways to start:

• Use diverse data sets: When training an AI tool (or choosing a pre-built one), make sure the data represents a wide range of clients, projects, and team experiences.
• Set fairness benchmarks: For example, if your procurement software is helping choose glass suppliers, ask: is every vendor getting a fair assessment based on cost, delivery time, and product quality?
• Review third-party platforms: Before you plug a new AI tool into your business, ask vendors how they handle fairness and bias. Some provide transparency reports or fairness testing results to back up their claims.
• Make fairness an ongoing conversation: It’s not a once-and-done thing. Set regular check-ins (even just quarterly) to ask: “Is this tool working equally well for everyone in our business?”

Fairness isn’t just a tech issue—it’s an operational goal. And with a little planning, it’s completely possible to build it into your day-to-day systems.

Fairness in AI might seem like a big, abstract concept—but in glazing, it’s all about the day-to-day decisions that affect your team, suppliers, and clients. Whether it’s allocating installers to jobs, deciding how quotes are generated, or choosing which orders to prioritise, AI can help—but only if it’s built thoughtfully.

That’s where Thinkivity comes in. We help glazing businesses keep an eye on the fairness of their AI tools—so you’re moving forward with confidence, not crossing your fingers. By taking a few proactive steps today, you’re not just avoiding problems—you’re setting your business up for better outcomes across the board.

Because at the end of the day, technology should work for everyone—not just a lucky few.