6 Tips to Help Harness the Power of Generative AI

In today’s rapidly changing business landscape, the buzz around generative AI (gen AI) is hard to ignore. This technology presents exciting opportunities for small businesses in Australia to improve efficiency, customer engagement, and profitability. However, to fully benefit from gen AI, it’s essential to approach it with a clear strategy that aligns with your business goals.

Here’s a breakdown of how Australian small businesses can make the most of this cutting-edge technology:

1. Identify Company-Specific Opportunities

Generative AI can be a powerful tool, but its actual value lies in how well it aligns with your business’s specific needs and goals. The key is to start with a clear understanding of your unique challenges and opportunities. Ask yourself: what are the pain points in your business? Where could you improve efficiency, enhance customer engagement, or drive profitability? Once you identify these areas, you can explore how AI can offer targeted solutions that create real value.

Trades

Managing schedules and client interactions in trade businesses can often be time-consuming. AI-powered scheduling tools can automatically allocate jobs based on location, job type, and staff availability, significantly reducing the admin burden. Additionally, AI-driven chatbots can manage client queries and bookings, freeing up your time to focus on delivering high-quality service. You could also use AI to forecast demand for certain services, helping you allocate resources more effectively and avoid over- or under-staffing.

Healthcare

AI can assist with everything from patient engagement to administrative efficiency in healthcare. For instance, AI-powered systems can automate appointment reminders, streamline billing, or analyse patient data to predict outcomes and suggest personalised treatment plans. Healthcare businesses, particularly small clinics or allied health professionals, can benefit by using AI to manage routine tasks, freeing up time for patient care while ensuring data is used effectively to enhance the quality of service.

Manufacturing

AI can optimise production processes, improve supply chain management, and enhance quality control. For example, AI systems can monitor machinery for signs of wear and tear, predicting maintenance needs before they cause disruptions. This not only saves on repair costs but also minimises downtime. AI can also analyse vast production data to identify inefficiencies, helping manufacturers improve output and reduce waste. Small manufacturing businesses can compete more effectively by implementing AI in critical areas and improving operational efficiency.

Professional services

Where time and expertise are critical, AI can streamline administrative tasks and provide insights into client behaviour. AI-driven software can automate tasks like invoicing, contract management, or client communication, allowing businesses to focus more on delivering value-added services. Moreover, AI can help professional services firms by analysing client data to spot trends or predict future needs, enabling a more proactive approach to service delivery. Whether a law firm uses AI to review documents faster or an accounting firm automates tax preparations, generative AI can help small professional services businesses deliver more for their clients in less time.

By focusing on areas where AI can have the most impact—whether automating routine tasks, improving decision-making through data, or enhancing customer interaction—you can ensure that AI becomes a valid value driver in your business. The opportunities are vast, but the key is to start with your specific business context and grow from there.

2. Get Your Data in Order

To fully unlock the potential of generative AI, having well-organised and accessible data is essential. For many small businesses, data often exists in various formats across different systems, making it challenging to harness the true power of AI. AI-driven solutions can fall short of expectations without a clear strategy for managing and utilising your data. The first step is ensuring that your business has a strong data foundation—whether you’re a trades business, a healthcare provider, a manufacturer, or part of the professional services sector.

Trades

Your data might come from various sources, such as job management systems, invoicing software, and customer feedback platforms. AI can only be as effective as the data it processes, so ensuring that all your data is centralised and up-to-date is crucial. For example, combining data from job logs, customer reviews, and financial records into a unified system allows AI to provide valuable insights into which services are most profitable or when peak demand occurs, enabling better decision-making for resource allocation and marketing strategies.

Healthcare

In smaller clinics or allied health providers, data is typically generated from patient records, appointment schedules, billing systems, and wearable health devices. Organising this data into a central, secure system allows AI to identify trends that might otherwise be missed, such as patient no-shows or common treatment paths that lead to successful outcomes. This helps create a more efficient, patient-centric service model while ensuring compliance with privacy regulations like the Australian Privacy Principles (APPs).

Manufacturing

Data may be spread across production lines, inventory management systems, and supplier networks. AI can analyse this data to optimise production schedules, forecast demand, and even predict machinery maintenance needs. However, AI’s effectiveness is severely limited if the data is fragmented or siloed. By centralising data from different parts of your manufacturing process, AI can deliver insights that streamline operations, reduce waste, and enhance product quality. A well-organised data system enables manufacturers to respond faster to supply chain disruptions or production inefficiencies, giving them a competitive edge.

Professional services

Client data, billing information, and project documentation can be scattered across multiple platforms. AI-powered tools can help automate client communication, generate invoices, and analyse work patterns—but only if the underlying data is accessible and well-organised. By consolidating data from client interactions, financial records, and case histories into a unified system, AI can provide insights into client needs, improve forecasting, and enhance the efficiency of service delivery. This allows professional services firms to deliver more personalised, high-quality services while reducing time spent on administrative tasks.

Regardless of the industry, getting your data in order is more than just a one-time task; it is an ongoing process. Regular data audits, clear governance frameworks, and integrated software solutions can help ensure your data remains clean, accurate, and accessible. Once your data is well-organised, AI can be deployed to identify trends, automate processes, and drive strategic decisions that impact your bottom line. Properly structured data is the foundation for successful AI implementations, helping small businesses move faster and more efficiently in today’s competitive landscape.

3. Choose the Right Partners

When integrating generative AI into your business, the choice of partners can significantly impact your success. For small businesses, the right partnerships—whether with technology providers, cloud platforms, or AI specialists—can make the difference between a smooth AI implementation and an overly complex, costly experience. Each industry has unique needs, and selecting partners that understand these can help your business maximise AI’s potential.

Trades

Choosing the correct AI-driven job management or scheduling platform can significantly improve operational efficiency. You may need a partner that provides tailored solutions for managing job bookings, optimising routes for tradespeople, and automating invoicing and customer communication. By selecting a cloud-based system that integrates well with your existing tools, you ensure that your business remains agile and responsive and can scale as demand increases. Additionally, the right partner will allow you to maintain flexibility—ensuring you’re not locked into a system that can’t evolve as your business grows.

Healthcare

Data security and patient confidentiality are paramount. Partnering with AI solution providers who understand and comply with Australian healthcare regulations, such as the Australian Privacy Principles (APPs), is critical. You’ll also want to ensure that your AI partners provide systems that can integrate seamlessly with existing electronic health records (EHR) systems and telehealth platforms. For example, an AI-powered diagnostic tool that assists with patient assessment should be able to securely access and analyse patient data in real time, improving care without compromising privacy.

Manufacturing

The landscape is often more complex, involving multiple partners across supply chains, production processes, and technology providers. AI solutions might focus on predictive maintenance, supply chain optimisation, or quality control automation. The right partner here could be a cloud service provider that supports scalable data analysis across production lines, helping you prevent downtime by predicting when equipment might fail. Additionally, AI partners who offer industry-specific expertise, such as manufacturing automation, can help ensure that your systems work seamlessly across all areas of your operation, from design to delivery.

Professional services

Choosing the right AI partner is about enhancing the value of your expertise through automation and data-driven insights. Whether it’s legal firms using AI to speed up contract reviews or accounting practices automating financial forecasting, the right AI tools can transform service delivery. Professional services firms should partner with providers offering robust AI solutions to streamline client management, improve operational efficiency, and ensure compliance with industry standards. For instance, an accounting firm might partner with an AI company that integrates accounting software with machine learning tools for predictive financial analysis, helping clients make informed decisions faster.

Regardless of your industry, it’s important to avoid choosing a partner based solely on current market trends. Instead, focus on building long-term, flexible partnerships that allow you to pivot as technology evolves. Your AI partner should meet your immediate business needs and provide scalability, adaptability, and ongoing support.

The right partner will help you navigate the complexities of AI while allowing your business to remain agile and competitive in the future.

By strategically choosing your AI partners, you ensure that your investment in generative AI delivers lasting value. The right partnerships will allow your business to innovate, improve efficiency, and better serve your customers, regardless of your sector.

4. Balance Risk with Value Creation

One of the critical challenges for any business implementing generative AI is balancing the potential for value creation with the associated risks. AI can offer significant benefits—enhancing efficiency, improving decision-making, and unlocking new growth opportunities—but it also carries risks, such as data security concerns, regulatory issues, and the possibility of AI-generated errors. Understanding how to mitigate these risks while maximising AI’s value is critical for Australian small businesses.

Trades

AI can streamline operations by automating job scheduling, customer communications, and inventory management. However, there are risks, such as relying too heavily on AI systems without a contingency plan if the technology fails. What happens if an AI-powered scheduling system goes offline or malfunctions? To balance this risk, start with smaller-scale AI implementations—automating routine tasks, for instance—before fully integrating AI into core business functions. Over time, you can scale these AI tools while maintaining manual systems as backup, ensuring that your business doesn’t face significant disruptions if technology issues arise.

Healthcare

AI offers great potential for improving patient care, from automating administrative processes to aiding in diagnosis. However, healthcare businesses must be particularly mindful of privacy concerns, as patient data is highly sensitive. The risk of data breaches or non-compliance with privacy regulations can be significant. To mitigate these risks, healthcare providers should implement AI in non-critical areas, such as automating appointment reminders or billing processes. As the system proves reliable, AI can then be extended to more complex tasks like predictive diagnostics or personalised treatment plans, but always with strict data governance measures in place. A risk-averse, gradual approach helps health businesses harness the value of AI while maintaining trust and compliance.

Manufacturing

AI can optimise production lines, predict machinery maintenance, and improve supply chain efficiency. For example, during our recent trip to Europe in March (2024), we were invited to two Tulip farms where we saw some fantastic automation in action. From moving the tulips around based on growth and readiness for harvest to providing the exact amount of water or recognising when there are issues, the plants aren’t growing to expectation and, therefore, moved back in the production line. Right through to harvest, where tulips of the same size are collated into bunches, ready for sale.

When we visited the packaging warehouse, staff would place 6 of whatever vegetable onto individual conveyors. All were weighed, and 3 had a total combined weight of just over 500 grams and were pushed forward to be packed and wrapped together. Whilst this automation is rudimentary, the savings provided are significant.

However, the risk of over-automation—where too much reliance on AI could lead to unforeseen errors or bottlenecks—is real. For example, an AI system might make inaccurate predictions about equipment maintenance, leading to costly downtime. Manufacturers can balance this risk by starting with AI in low-risk areas, such as automating inventory tracking or using AI to improve energy efficiency. By monitoring the results and understanding the potential risks, businesses can expand their AI usage incrementally, using human oversight to verify AI-driven decisions, especially in high-stakes areas like quality control or production planning.

Professional services

AI can help automate repetitive tasks like contract analysis, financial forecasting, or customer communication. However, these businesses face risks regarding the quality and accuracy of AI-generated work. AI systems can make errors, and even tiny mistakes can have significant consequences when dealing with legal documents or financial reports. Professional services firms can mitigate these risks using AI as a support tool rather than replacing human expertise. For instance, AI can quickly sift through large volumes of data, identifying trends or red flags that professionals can review in detail. This approach allows businesses to take advantage of AI’s efficiency while maintaining high accuracy and oversight.

Developing a risk management framework in all sectors is essential when implementing AI. This involves assessing the technical risks—such as system failures or data breaches—and considering broader risks like regulatory compliance and ethical concerns. Establish clear guidelines for how AI will be used in your business and ensure employees are trained to manage the technology and its associated risks. Cross-functional teams that include IT, operations, legal, and compliance experts can help to develop a balanced approach, ensuring that AI-driven innovations are aligned with your business’s risk tolerance.

Ultimately, balancing risk with value creation requires a measured, thoughtful approach. By starting small, monitoring performance, and gradually expanding AI usage, your business can reap the rewards of AI innovation without exposing itself to unnecessary risk. The goal is to build confidence in AI systems over time, using them to enhance—not replace—existing processes while ensuring that the human element remains central to critical decision-making.

5. Understand the Tech Stack and Talent Requirements

Successfully implementing generative AI requires more than just adopting the latest technology—it demands a deep understanding of your business’s tech stack and the talent needed to drive AI solutions forward. For small businesses, particularly in Australia, the challenge lies in finding the right balance between having the necessary infrastructure and ensuring that your team has the skills to make the most of it. Generative AI is powerful, but it’s only as effective as the systems built upon and the people who manage it.

Firstly, let’s talk about the tech stack. The technology infrastructure behind AI includes everything from data storage and cloud services to AI models and integration platforms. Small businesses must ensure their tech stack is ready to support AI. If your systems are outdated or fragmented—where data is stored across multiple platforms with no central oversight—your AI initiatives are unlikely to succeed. For example, a trades business that relies on a patchwork of systems for job scheduling, invoicing, and customer management may struggle to integrate AI without modernising these systems.

Small businesses need a modular, scalable approach to their tech stack to avoid this. In practical terms, this means adopting cloud platforms that can quickly grow with your business. Cloud-based solutions provide flexibility and allow AI systems to process large amounts of data efficiently. For instance, in the health sector, where patient data must be securely stored and accessed in real time, a well-structured cloud infrastructure ensures that AI tools can access and analyse information quickly, improving service delivery and patient care.

The tech stack must support real-time data processing in manufacturing, especially when using AI for predictive maintenance or supply chain management. AI solutions depend on continuous data flow from sensors, machines, and logistics networks. AI will only function as intended if your technology is updated and fast enough to handle this. Manufacturers should invest in robust IoT (Internet of Things) systems and real-time data analytics platforms to ensure that AI can work seamlessly across the production line, preventing costly downtime and improving operational efficiency.

Once the tech stack is in place, it’s essential to consider the talent requirements. Generative AI is not a plug-and-play solution—it requires skilled personnel to manage, refine, and optimise its use. For trades businesses, this might involve hiring or training staff who understand how to use AI-driven tools for scheduling, customer service automation, or inventory management. In smaller teams, where employees wear multiple hats, it’s crucial to ensure that staff are comfortable working with technology and can adapt to new AI-driven systems.

In professional services, where AI can help automate tasks like contract analysis or financial forecasting, businesses need employees who can interpret AI outputs and integrate them into their workflows. Lawyers, accountants, and consultants may not need to be AI experts, but they do need to understand how AI tools can enhance their work and improve service delivery. Upskilling employees through training in AI applications and data analysis can help professional services firms remain competitive while leveraging the full power of AI.

In healthcare, AI requires a team that understands the technology and the regulatory requirements around patient data. Healthcare providers need staff who can manage AI systems, ensuring they comply with privacy laws and ethical standards while delivering meaningful insights. This might mean hiring data scientists or working closely with AI vendors who can provide the necessary expertise. Training existing staff on interacting with AI systems, such as AI-powered diagnostic tools, is also essential to improving patient outcomes without overwhelming healthcare workers.

As AI becomes more integral to business operations, the demand for AI-savvy talent will only increase. Small businesses need to think strategically about recruiting and retaining employees who can support AI initiatives. This might involve partnering with external consultants or technology providers in the short term while developing long-term strategies for upskilling your existing workforce. For many small businesses, especially those with limited resources, building a team with the right skills and fostering a culture of continuous learning is critical to staying ahead in a rapidly evolving digital landscape.

Adopting generative AI is more than just having the right tools; it is also about having the right infrastructure and people in place. By investing in a scalable tech stack and ensuring that your team has the necessary skills to manage and use AI effectively, your business can unlock the full potential of AI-driven solutions, no matter your industry. Understanding and addressing technical and talent requirements will ensure your AI implementation is successful, sustainable, and aligned with your business goals.

6. Start Small and Learn Fast

When integrating generative AI into your business, starting small and learning fast is the best approach. AI can feel overwhelming, especially for small businesses with limited resources. However, you don’t need to jump in with large-scale, complex AI projects immediately. Instead, focus on manageable, small-scale implementations that allow you to test the waters, understand how AI fits into your operations, and adapt as you go. This gradual approach helps mitigate risks and enables your team to build confidence and competence with AI over time.

The key is identifying simple use cases that AI can immediately impact and expand from as you gain experience. For trades businesses, this might mean starting with something as straightforward as automating appointment scheduling or using AI-powered chatbots to handle customer inquiries. These small implementations can free up valuable time, reduce administrative burdens, and improve customer service without requiring a massive investment. Once you see how AI works in these basic tasks, you can gradually scale it to more complex operations, such as predictive maintenance of tools or equipment based on AI-driven data analysis.

Small businesses such as clinics or allied health practices can start by automating administrative processes using AI, like appointment reminders or billing systems. This reduces the load on administrative staff and allows healthcare professionals to focus more on patient care. From there, you can explore more advanced AI applications, such as using AI to analyse patient data for personalised treatment plans or incorporating AI-driven diagnostics tools. Starting small also gives you time to navigate the complex regulatory requirements around AI in healthcare, ensuring your AI solutions fully comply with privacy laws.

In manufacturing, you can begin by using AI to optimise smaller production areas. For example, AI can monitor inventory levels and predict when supplies need to be reordered, reducing the risk of stockouts and overstocking. Once these systems are in place and working effectively, you can move on to more sophisticated AI applications, such as predictive maintenance or real-time data analysis for improving production efficiency. By starting small, manufacturers can avoid significant disruptions to their operations while gradually realising the benefits of AI.

Professional services firms can start by using AI to automate routine tasks like document review, data entry, or invoicing. AI tools can quickly scan documents for errors, inconsistencies, or patterns, saving time and reducing human error. Over time, as your team becomes more comfortable with these tools, you can expand AI’s role in more complex processes, such as using AI for data-driven decision-making or financial forecasting. The gradual implementation of AI allows professional services firms to maintain high-quality outputs while slowly transitioning to more AI-powered workflows.

The learning process is crucial in all sectors. Implementing AI is not a one-off task—it’s an ongoing journey where continuous learning is essential. What works in theory or a lab environment doesn’t always translate seamlessly into day-to-day business operations. Therefore, it’s vital to approach AI as a learning tool, where each new implementation offers insights into what works, what doesn’t, and where improvements can be made. This agile approach allows small businesses to pivot quickly, making necessary adjustments before committing to large-scale AI integration.

Furthermore, starting small lets you gather valuable data on how AI impacts your business. To evaluate AI’s effectiveness, you can measure key performance indicators (KPIs), such as increased efficiency, cost savings, or improved customer satisfaction. This data helps refine your AI strategy and provides a solid foundation for scaling AI initiatives across your business as you learn what works best for your unique needs.

As you gain confidence, you can expand AI’s role in your business. But starting with small, practical applications minimises the risks, lowers the initial investment, and gives your team time to adapt to new technology. This iterative process is the best way to ensure that your AI implementation delivers real value and aligns with your overall business strategy.

Starting small and learning fast is the most effective way to implement AI sustainably and scalable. By beginning with small, low-risk projects, you allow your business to explore AI’s potential, build a foundation of knowledge, and grow your AI capabilities at a pace that suits your business. This approach allows you to manage the learning curve while gradually integrating AI into more areas of your operations, ensuring long-term success.

The journey might be complex, but AI can be a game changer for your business with the right steps.