Artificial Intelligence
As businesses are now able to utilise more and more Artificial Intelligence (AI) and Machine Learning (ML) technologies than ever before, in fact the amount and sophistication can be bewildering.
Let’s look at the key advantages around AI and ML as they are reasonably straightforward. Here is our summary:
- Consistent application
- Reliability
- Accuracy
- Speed
- Integrated systemic approach.
The difficult part is to decide what success looks like in this context. In addition, ensure the output is exactly what is expected.
It is an important distinction between defining the answer and confirming expectation. The great benefit of AI is seeing patterns in data and application to improve functionality (ML) therefore before embarking on the journey a detailed set of parameters need to be designed, agreed and validated to ensure the output is credible and answers its primary purpose.
Regrettably we have all seen in the media programmes delivering way outside of expectation seriously damaging the project delivery and often the business reputation.
If we examine various formats of AI we could easily create masses more data and text for processing just to explain, but please accept our abridged comments to provide an overview.
To maximise machine learning a great deal of high quality data is essential. In most businesses this data exists in finance, logistics and sales and conceivably HR. The data held is certainly essential business data and would be cleansed of error and stored consistently over a reasonably long period.
- So AI is ideal for creating predictive models.
The benefit of ML in an AI context is where a tightly defined decision is required. As the data is regularly updated and validated it is perceived to be ‘clean’ so will provide reliable outputs. Errors will quickly show and can be corrected further increasing the accuracy of the learned algorithm. As an example, in a routine task such as cross - referencing payments to invoices are perfect whereas a prediction need is far more difficult and is prone to inaccuracy.
- The key point is ML is easiest to implement when the decision can be easily automated with a minimum number of variables. New processes and or Human intervention should be avoided as the variety and potential for error negate the benefit.
Digital assistants are an ideal way to help team members navigate through complex systems quickly and with minimum engagement. In many organisations the data is held in ways that are potentially not the most ‘human friendly’ so navigating and use can be slow or prone to error. An example is where data is used by multiple users and can be accessed for different purposes. As an example booking holidays or ‘out of office’ can be easily handled via a digital process as the action can be programmed to update all records without error and improve accuracy and reduce time on what are often seen as non-essential processes.
Chatbots are often used to provide users and ‘customers’ key information related to FAQ’s. This data has been derived by the scanning of myriads of technical reports, updates and feedback forms. This has improved many customer experiences and reduced call time for service assistants and customers alike.
- As data is profiled users can programme set questions that can be delivered in a report form, simple mail or via a digital assistant. Following on form our examples above, questions such as how many holiday days are yet to be taken and what number are already booked and agreed. This data can be used for budgeting, capacity planning, HR compliance checking- quickly and consistently. It is about the question as much as the answer.
Technology has progressed to help us to use data like never before. Data that was previously difficult to analyse and compare can be processed via an AI route.
For many applications such as predictive maintenance, buying patterns, fault reporting can be created and ‘compared’ using AI.
In addition, safety critical items can be constantly scanned and checked and the minutest change noted. If any change is critical then this can trigger action such as- auto stop or warning or if subject to the control parameters an inspection or planned repair can be scheduled.
Marketeers can request data on how the prospective customer audience is affected. As an example, during a TV broadcast of a major sporting event how many times did the stadium audience see a particular advertisement and similarly for the TV audience. This data can then be AI analysed to sales activity etc.
Where large amounts of written data are produced; such as insurance claims, these can be auto scanned and key information collated. There are numerous benefits including trends and the ability to spot unusual or potentially fraudulent activity. The data can be readied for delivery in report form via digital assistants or through a control mechanism particularly if sensitive or confidential.
Considerations
- As the uses of AI are somewhat infinite it is essential that businesses consider carefully how to use and the subsequent effect on employees and customers.
- The past has certainly revealed technology fears, resulting in distrust and often outright refusal to use.
- Business specifiers are well advised to consider use and communication of benefits carefully as it is not a giant leap of faith to appreciate that as a reward for use, employee support is diminished particularly if the user’s role vanishes as a direct consequence.
Summary
Artificial Intelligence is here to stay and has great and real benefits but must be introduced carefully and sensitively.
We help you understand the benefits, work with you to look at the relevant options and the risks of implementation. Certainly, one size does not fit all but not considering opportunities fully only; ensures those that do take on the change will prosper and grow as customer related services show constant innovation and improvement.
We are here to help you consider what is best for your business and ensure you grow sustainably with a plan and understanding. Contact us to discuss further.
