There’s a lot of buzz around Artificial Intelligence these days, specifically ChatGPT and the various competitors that are coming out. It’s amazing technology and an indication of what’s coming in the world of lifestyle AI.
How does this help you as a business trying to compete in a modern world?
If you’re here asking how ChatGPT can help you - it’ll help you create documents, respond to emails and a range of other time saving activities. It’ll also help all of your competitors in the same way and is easily accessible.
So how does your business leverage artificial intelligence in a unique way?
Predictive Sales Revenue
One area that can be particularly interesting is in predictive analytics. Consider this scenario:
- You’ve got a sales team with a pipeline and they’re giving a forecast on what they think will actually close,
- You can see in your CRM that the level of activity on certain opportunities is minimal, despite the opportunities having a predicted close date of this quarter.
Intuitively, we can question whether these opportunities are really going to close and push our sales people to be more pro-active. However, this is very time consuming and it can be hard to find the problematic opportunities when there is a high volume and without a high level of effort.
Artificial Intelligence can highlight and flag which opportunities may be at risk as well as forecast more realistic sales for your organisation.
Anomaly Detection
Another area of value within larger organisations can be around anomaly detection. In WA, for example, we’ve had a number of high profile corruption cases within the last few years. These can be difficult to identify in large organisations due to the number of people involved and the disparate data sets.
Artificial Intelligence can help this process by identifying potential anomalies in transactions and highlighting them for further analysis by analysing a large number of indicators within the financial records.
What are the Key Factors in Whether AI can be Used?
The first step in this process is to consider what type of data is available and whether a machine learning model can assist in solving this particular challenge. Some of the aspects that are considered in this space include:
- Data quality and quantity: The quality and quantity of the data available for training the machine learning model is critical. A large dataset that is representative of the problem domain can help to train a more accurate and generalisable model. The data should also be clean, well-structured, and free from errors, outliers, and missing values.
- Feature selection: The selection of relevant features that are related to the problem being solved is important. The features should be diverse, informative, and not highly correlated with one another.
- Model selection: Choosing the right machine learning algorithm that is appropriate for the dataset is crucial. The choice of model will depend on the type of problem being solved (classification, regression, clustering, etc.), the size of the dataset, and the distribution of the data.
- Bias and fairness: It is important to consider the potential for bias and fairness issues when training a machine learning model. This includes issues such as data bias, algorithmic bias, and fairness in the selection of features and models.
- Data privacy and security: The protection of sensitive data and the privacy of individuals should also be considered when using a machine learning model with a dataset. This includes ensuring that the data is stored securely and that appropriate measures are taken to protect against unauthorised access and use.
If you’re considering whether artificial intelligence can help your business solve critical challenges, please reach out and talk to one of the experts at Datamagic today!
