Everyone should be automating everyday tasks like SEO copy through to debugging code. But with these off-the-shelf tools readily available to everyone, your competitors can also make easy headway too.
Where you can build a real competitive advantage is through the smart use of your own data, giving you insights that others cannot readily see, and the opportunity to achieve extra-ordinary performance relative to your competition.
While some of the tools to do this require coding skills, all require a deeper understanding of statistics and how to manage data. What is essential is to grasp the underlying AI and machine learning techniques, as these have the power to generate unique insights and enable you to unlock better business performance. We can help with learning, tool selection and implementation.
Predictions about important instances or events can be made by applying regression modelling to tabular, numerical data.
Typical use case examples include weather conditions, anticipating manufacturing bottlenecks and predicting house prices in a particular neighbourhood.
Classification modelling is most commonly applied to tabular, numerical data that has binary or distinct data classes to predict groupings.
Typical use case examples include predicting fraud, anticipating customer churn and sorting large product ranges into categories.
Unstructured data, that has no target variable, can be clustered into common groups using a variety of clustering algorithms.
Typical use case examples include clustering DNA patterns, improving customer segmentations, and social media analysis.
A common technique applied by many of the online service providers like Amazon, Tinder and Netflix to identify and make product recommendations to you based on what you and others like you liked.
It requires modelling on large structured data, but needs viligance to stay on top of processing costs and shifting customer preferences.
Historically the "unstructured" nature of images has made them hard to analyse accurately. The use of neural networks and other AI techniques now means they can be understood increasingly well.
Typical use case examples are wide-ranging and include detecting disease in crops, identification of abnormalities in medical images, and of course facial recognition.
The development of large language models (LLMs) and the use of neural networks in Natural Language Processing has enabled AI models to deliver extraordinary insights and results on text data.
Typical use case examples include chatbots, sentiment analysis, spam filtering, predictive text, legal analysis, clinical reasoning and educational tools.
Much of the recent buzz around AI has been about Generative AI. These tools create new data that is similar to the input data but differ from the other techniques outlined above; they are not focused on predictions or categorization but rather on data generation. These tools excel at generating text, images, video, music and other creative content.
A few specialist tools are referenced on the Efficiency page. More generalist tools include ChatGPT, Bard, Claude, Scribe, VEED, Amper, Soundraw, Pictory, MidJourney, and Dall-E. The category is growing quickly and becoming more sophisticated in the quality of material generated.
