Machine Learning, AI and How They Can Be Used in YOUR Business Today
It’s all the rage!! Since OpenAI launched its AI tool ChatGPT in November last year and Microsoft recently announced its US$10bn investment. Reportedly, ChatGPT collected over 1m users within days of launching.
According to Bloomberg, almost 30% of nearly 4,500 professionals surveyed by Fishbowl admitted to using OpenAI’s ChatGPT at work to help build marketing decks and fresh content, write code and even help draft emails and write memos.
Machine learning (ML) and artificial intelligence (AI) are technologies that are revolutionising the way we live and work. They are being used in various industries today to automate processes, improve decision-making, and provide new insights. In this blog, we will explore the basics of ML and AI and how they can be used in business today.
ML is a subset of AI that enables systems to learn and improve from experience without being explicitly programmed. It involves training models on large datasets, which enables them to make predictions or decisions based on new data. There are three main types of ML: supervised, unsupervised, and reinforcement learning. Supervised learning is the most common, where the model is trained on labelled data and is used for classification or regression tasks. Unsupervised learning, on the other hand, is used to discover patterns in unlabeled data. Reinforcement learning is used to train models to make decisions based on rewards or penalties.
AI, on the other hand, is a broader term that encompasses ML and other technologies such as natural language processing (NLP) and computer vision (CV). AI systems can be designed to perform a wide range of tasks, from image and speech recognition to decision-making and planning.
The Benefits of Machine Learning and AI in Business
In business, ML and AI are being used to automate processes, improve decision-making, and provide new insights. For example, in the healthcare industry, ML models are being used to analyse medical images and make diagnostic decisions. In the retail industry, ML models are being used to predict customer behaviour and optimise pricing. In the finance industry, ML models are being used to detect fraud and assess credit risk.
One of the most significant benefits of ML and AI in business is their ability to automate processes. For example, in manufacturing, ML models can be used to optimize production processes and reduce downtime. In logistics, ML models can be used to optimize routes and reduce transportation costs. In customer service, NLP models can be used to automate responses to frequently asked questions, which can save time and money.
Another benefit of ML and AI in business is their ability to improve decision-making. For example, in marketing, ML models can be used to analyze customer data and predict which products or services will be most successful. In sales, ML models can be used to predict which leads are most likely to convert to customers. In finance, ML models can be used to identify investment opportunities and manage risk.
ML and AI also provide new insights that can help businesses to improve their products and services. For example, in product development, ML models can be used to analyze customer feedback and identify areas for improvement. In research and development, ML models can be used to analyse data and identify new opportunities.
The Challenges of using Machine Learning & AI in Business
However, there are also challenges associated with the use of ML and AI in business. One of the main challenges is the need for large amounts of high-quality data to train ML models. Another challenge is the need for experts with the skills and knowledge to develop and implement ML and AI systems. Additionally, there is a concern that the use of ML and AI in business could lead to job losses as certain tasks are automated.
Another drawback is that the models are trained using long-term historical data to ‘learn’ and generate results and outputs. This means that analysing shorter-term data can be problematic and potentially less useful.
If the model is not trained on data that is similar to the data that it is expected to analyse, it may not be able to provide accurate or useful results. Additionally, if the data was collected at a specific time, it may not be reflective of the current state of the world, so the model might not be able to generalise well.
Examples for Small Businesses
Here are a few examples of how small businesses are using AI to improve their operations and gain a competitive edge:
Personalised recommendations: An online clothing retailer uses AI to personalise product recommendations for their customers. The AI model is trained on customer data such as purchase history and browsing behaviour and can make personalised recommendations for each customer. This has helped the company to increase sales and improve customer satisfaction.
Fraud detection: A small fintech company uses AI to detect fraudulent transactions on their platform. The AI model is trained on historical data and is able to identify patterns of fraudulent activity. This has helped the company to reduce losses from fraud and improve the security of their platform.
Predictive maintenance: A manufacturing company uses AI to predict when equipment will need maintenance. The AI model is trained on historical data and is able to predict when equipment is likely to fail. This allows the company to schedule maintenance proactively, reducing downtime and saving money.
Voice-enabled virtual assistant: A business uses AI-powered virtual assistants to provide customer service and answer frequently asked questions. This allows the company to improve customer satisfaction and reduce the workload of their customer service team.
Chatbot customer service: A e-commerce company uses an AI-powered chatbot to provide 24/7 customer service on their website. The chatbot can answer frequently asked questions, track orders, and even process returns. This has helped the company to improve customer satisfaction and reduce the workload of their customer service team.
Supply Chain Optimisation: A logistics company uses AI to optimise their supply chain operations. By analysing large amounts of data, the AI model can predict demand, optimise routes and even predict maintenance needs for the company's fleet of vehicles. This has helped the company to save costs and improve the efficiency of their operations.
Marketing: A small startup uses AI to analyse customer data and predict.
Data analysis: A professional service business uses AI to analyse large amounts of data and generate reports and summaries for their clients. This allows the company to provide data-driven insights and improve decision-making for their clients.
These are just a few examples of how small businesses might use AI to improve their operations and gain a competitive edge. The specific use cases will vary depending on the industry and the specific business, but AI is becoming increasingly accessible and useful for small businesses.
ML and AI are technologies that are revolutionising the way we live and work. They are being used in various industries today to automate processes, improve decision-making, and provide new insights. The benefits of ML and AI in business are significant, but there are also challenges associated with their use. As the use of ML and AI continues to grow, it will be important for businesses to stay informed about these technologies and develop strategies to take advantage of their benefits while managing their risks.
How could you use AI or ML in your business today?
Current Interest Rates (Big 4)
What’s the outlook?
Inflation eased slightly last month, according to new monthly Australian Bureau of Statistics (ABS) figures, however, both businesses and economists say it may not have peaked just yet.
The monthly consumer price index (CPI) rose 6.9% over the year to October, down a little from the 7.3% headline inflation rate recorded in September.
There is good reason to believe inflation in Australia is near a peak for this cycle, according to Reserve Bank of Australia (RBA) Deputy Governor Michele Bullock, pointing to a loosening in global supply chains and lower oil prices but warned there is still plenty of risks to the upside including energy prices, rents and geopolitics.
In spite of that, she reiterated that further increases in interest rates would be needed to ensure inflation receded from 32-year peaks around 8.0%, with the size and timing of future increases, dependent on the data the RBA receives. She was hopeful that higher borrowing costs and a slowing world economy would eventually do the trick.
Australian house prices are expected to rise in 2023 if the Reserve Bank pauses rate rises and inflation drops, according to a new report from property analysis firm SQM Research.
SQM Research's Housing Boom and Bust Report for 2023 forecasts capital city house prices will rise between 3% and 7%. The report's "base case" forecast hinges on the RBA not hiking the cash rate above 4%, inflation dropping to 5%, and unemployment staying below 5%.
In this scenario, house prices rise by between 5% on average, including rises of between 9% and 13% in Perth, 8% and 12% in Sydney, 3% to 7% in Brisbane and 2% to 6% in Melbourne.
And if the rates start to go down, Australia’s property market could be 9% higher than they are right now. On the flip side, properties could decline in value by as much as 6%in a worst-case scenario where interest rates continue to rise and inflation keeps going up.
The annual inflation is 7.3% and is set to hit 8% by the end of the year, while unemployment is at 3.4%.
The RBA is widely tipped to pause the rate rise cycle by mid-2023 and leave the cash rate unchanged for the rest of the year.
Despite all the doom and gloom news, long term investors can take comfort that property prices have always been in the uptrend. Australian house prices have doubled over the last two decades, while rental prices have grown at half that rate, according to a new report from the Real Estate Institute of Australia.
REIA’s latest Real Estate Market Facts report found that the weighted average median house price for the capital cities rose 103.8% to $1,011,208 between 2002 and 2022.
If you would like to review your home loan options, give Darren a call on 0452 339 778 or email him at [email protected] to discuss what options you have available.
