Our consultancy work spans many sectors and, over the past six months, several projects have followed a similar theme where the client is searching for efficiency within human verification workflows – where everyone is busy performing their own tasks and simultaneously checking, and/or fixing, the work of others.
All the companies we have worked with are data and system rich, in that they all operate by utilising a mix of systems by capturing data for auditing purposes – so all changes, and the users who make those changes, are recorded. This historical data goes back many years.
In basic terms, we have access to the original data that was entered and whom it was entered by. We then have the support data of the network of people verifying the work. We then have the ‘issue data’ – where an error in the client job has slipped through the cracks.
This information is available across hundreds of client jobs and provides us with a ‘forensic’ overview of the lifespan of every project.
The information contained within the systems is a perfect set of data for training Machine Learning – and this is a very practical and cost effective starting point for testing whether Machine Learning can have a positive impact on your business.
By running a Machine Learning pilot programme alongside the current ‘human’ methodology and existing processes, we can suggest changes and improvements – from the original data entry point through to the support staff who accept and verify the changes – ascertaining time savings across the process.
Once the pilot programme has been completed – and the reduction in time and full implementation costs known – a fully integrated process can be put in place that will provide continuous improvement.
Optimising human time for business growth, or expansion of roles where their knowledge is better utilised, allows for a more efficient and time–aware workforce – and where a Machine Learning programme outlay and implementation used to be a only available to blue chip organisations, our approach and costs now means this is firmly available to SMEs.