AI is the ability of machines to perform tasks that would typically require human intelligence, such as understanding natural language, recognising images, and making decisions. ML is a subset of AI that enables machines to learn from data, allowing them to improve their performance over time without being explicitly programmed.

One of the key ways that AI and ML are driving digital transformation is by automating repetitive and time-consuming tasks. This can include everything from data entry and analysis to customer service and decision-making. By taking on these tasks, AI and ML can free up human employees to focus on more complex and value-added activities.

Another important way that AI and ML are driving digital transformation is by providing businesses with insights and predictions that they would not be able to access otherwise. For example, using ML algorithms, companies can analyse large amounts of data to identify patterns and trends that can inform decision-making. This can include everything from predicting customer behavior and identifying new market opportunities to detecting fraud and improving supply chain efficiency.

Whilst Amazon, Netflix, and Uber are often cited as examples of effective use of AI, local companies here in New Zealand are adopting AI to transform their businesses.

Xero uses AI and ML to improve its product offerings. For example, the company's machine learning-powered features such as the Cashflow Projector, uses a business' financial data to predict future cash flow, and its automated bookkeeping service uses machine learning to automatically categorise transactions.

Fonterra uses AI and ML to improve the efficiency of its operations. For example, the company uses predictive analytics and machine learning to optimise its supply chain, predict demand, and improve the quality of its products.

Spark uses AI and ML to improve its customer service. For example, the company's chatbot uses natural language processing (NLP) to understand customer queries and provide personalized assistance, and its predictive analytics system analyses customer data to identify potential issues and proactively resolve them.

NZTA uses AI and ML to improve the safety and efficiency of its operations. For example, the agency's predictive analytics system analyses traffic data to identify and predict congestion and to optimise traffic flow, and its image recognition system automatically reads license plates to enforce parking regulations and tolls.

Foodstuffs uses AI and ML to optimise its supply chain and improve the customer experience. For example, the company uses predictive analytics to forecast demand and optimise inventory, and it uses computer vision and machine learning to automate the process of checking for expired products in its stores.

However, it's important to note that the success of AI and ML projects requires the right data and infrastructure and the right team with the right skills, so it's crucial that companies invest in data governance, data management, and data engineering, to be able to make the most of their data and to get the most of their AI and ML projects.

Finally, it's important to keep in mind that AI and ML are not silver bullets, and they are not going to solve all problems, but they can be powerful tools that can help businesses to improve their operations, increase their efficiency, and create new revenue streams.

AI and ML are key technologies that are driving digital transformation by automating tasks, providing new insights, and improving decision-making. As such, it's important for businesses to be aware of the opportunities and challenges that these technologies present and to invest in the necessary data and infrastructure, as well as the right team, to be able to make the most of them.