AI is no longer the playground of tech giants. Today, mid-sized enterprises across sectors-from manufacturing to logistics to professional services-are implementing AI to streamline operations, improve customer experiences, and gain a strategic edge.
If you’re still discovering what AI can do for your organisation, then this short playbook will be valuable for you to quickly understand some illustrative examples of AI applications and how to get the ball rolling without committing to a large project.
Contents
- Why Mid-Sized Businesses Are Primed for AI
- #1: Brickworks Streamlines Operations with Predictive Maintenance
- #2: GPT-Powered Customer Service at Fergus (NZ)
- #3: Retailer Accent Group Boosts Sales with Personalised AI Marketing
- #4: Australian Manufacturing Firm Uses AI for Quality Control
- #5: MedHealth Transforms Document Processing with NLP
- Lessons Learned Across the Board
- How to Apply These Lessons in Your Organisation
- Final Thoughts
Why Mid-Sized Businesses Are Primed for AI
Contrary to common belief, mid-sized businesses often have more flexibility to innovate than their larger counterparts. They typically:
- Have less bureaucratic inertia than large enterprise businesses
- Can more easily deploy targeted AI solutions to specific pain points
- Are under pressure to stay competitive and efficient
Crucially, the falling cost of compute, rise of SaaS AI platforms, and no-code/low-code tools have made AI adoption increasingly accessible.
For mid-sized businesses that decide to take the leap into sensible AI based solutions now will see an advantage over their competitors that decide to stay on the side-lines.
#1: Brickworks – Predictive Maintenance in Manufacturing
Industry: Building materials
Headquarters: New South Wales, Australia
Challenge: Downtime and asset maintenance inefficiencies
Solution: IoT sensors + AI-powered predictive analytics
Outcome: 20% reduction in equipment downtime; better maintenance scheduling
Why it worked: Brickworks started small-piloting AI on a few production lines. Their data science team used machine learning models to identify failure patterns. This resulted in operational cost savings without full plant digitisation.
Strategic insight: Focus on one critical pain point. Build a narrow, high-impact model before scaling. This will ensure that your first AI project is kept small, with focus on one thing allowing you to see results quickly and without confusion.
#2: Fergus – GPT-Powered Customer Support
Industry: SaaS for tradies
Headquarters: Auckland, New Zealand
Challenge: Slow and costly customer support scaling
Solution: GPT integration into knowledge base chat
Outcome: 40% reduction in first-response time; increased user satisfaction
Why it worked: Fergus implemented an AI chatbot trained specifically on its product documentation and support tickets. Instead of a general-purpose assistant, it was a domain-specific tool.
Strategic insight: Use your existing structured and unstructured content to train support bots. GPT doesn’t need retraining, just smart prompt design and embedding.
#3: Accent Group – Personalised AI Marketing in Retail
Industry: Footwear & fashion retail
Headquarters: Melbourne, Australia
Challenge: Low engagement from generic campaigns
Solution: AI-driven personalisation using customer segmentation models
Outcome: 28% increase in click-through rate and 15% lift in sales
Why it worked: Accent Group analysed purchase histories and browsing patterns to create dynamic customer segments. With tools like Lexer and Adobe Sensei, they delivered highly targeted email campaigns and SMS offers.
Strategic insight: Retailers already have goldmines of customer data. AI makes it actionable at scale-without hiring a full data science team.
#4: Australian Manufacturer Uses AI for Visual Quality Control
Industry: Precision engineering
Headquarters: Victoria, Australia (anonymised)
Challenge: Manual inspection of complex components
Solution: Computer vision models for defect detection
Outcome: 90%+ accuracy rate; reduced inspection time by 50%
Why it worked: Using off-the-shelf image recognition models from AWS Rekognition and customised training, the firm automated a formerly manual QA step. Engineers retrained the model using their own images.
Strategic insight: Vision AI is one of the most mature and accessible tools for manufacturing. A few hundred labelled images can drive real results.
#5: MedHealth – NLP for Document Processing
Industry: Healthcare consulting
Headquarters: Australia
Challenge: Processing and summarising complex medical reports
Solution: Natural language processing to extract structured insights
Outcome: Reduced report turnaround by 65%; better clinician efficiency
Why it worked: Rather than replacing medical professionals, AI helped augment their workflow by summarising and highlighting key points in dense reports using tools like Azure AI services.
Strategic insight: The value of AI isn’t just automation-it’s augmentation. Look for bottlenecks where AI can improve human decision speed and quality, even when the end result may not be automation.
Lessons Learned Across the Board
After reviewing these examples, a few key success factors were common among them:
- Start small: Every business began with a single use case.
- Use existing data: Most leveraged internal data rather than buying datasets.
- Pick the right partners: AI implementation was often led by local vendors or integrators.
- Measure ROI fast: All projects demonstrated a clear improvement in cost, time, or satisfaction within 6-12 months.
- Avoid perfection: AI doesn’t need 100% accuracy to be valuable-just better than the current baseline.
How to Apply These Lessons in Your Organisation
Here’s a 4-step process to get started:
1. Identify Pain Points
Focus on high-friction, repetitive or error-prone workflows.
2. Audit Your Data
Map out what data you currently collect and what’s possible to instrument.
3. Run a Low-Risk Pilot
Choose a use case that is easy to measure and isolate (e.g. support response times).
4. Collaborate with Experts
Bring in an AI partner or consultancy (like us) to scope, develop and deploy responsibly.
Final Thoughts
The AI revolution is already underway in mid-sized enterprises. You don’t need deep pockets-just a clear problem, accessible data, and the right execution strategy.
The next step is yours.
