Imagine a scenario. You are a medium-sized company that has around half a thousand business relations with your partners. Each relation contains at least three documents (non-confidentiality agreement, framework agreement, and specific rules). Each relation and each document have different expiry dates and extension provisions. Now, you are looking for a specific agreement for a particular client. All this – done on paper. Tones of documents, tones of irrelevant data, and waste of time and money. Nightmare? For many – everyday life.
Now try to look at a different angle. Documents are in digital form, sorted and tagged, packed in the ERP or CRM system. Searching for particular information for a particular client takes not hours but seconds. You just type the required information and get results immediately to process them further. Miracle? Not achievable? Not anymore.
The fast development of machine and deep learning together with natural language processing improvement will change the state of play in many areas, including document management. Previously manual processes will be automated to not only reduce the operational cost (man-days) but also to let people focus on more creative and non-repetitive tasks. This will not only bring savings but also better management, employees’ satisfaction, and increase in terms of effectiveness.
Technologies like ‘artificial intelligence’ can search and extract data from even the most ‘sophisticated’ documents in seconds if implemented properly. All models that are used for training and implementation, however, require training and feedback that will guarantee that patterns and conclusions were identified properly. With appropriate data – in terms of quality and quantity – your model or just an NLP tool may help you in repetitive and boring tasks that usually generating high costs – even if you are not aware of it.
By simply implementing natural language processing tools you will be able to focus on important parts of your business. Rest will be done automatically safely and effectively.
Natural language processing is a way that machines get, interpret, and generate an outcome. This outcome can be set and defined – decision, prediction, or exact information that will be gathered by the algorithm (model), for example, a date on which a particular contract will expire or what are the conditions that have to be met to execute it. Such a tool – as mentioned before – will require some engagement from persons involved in the process, but in the relatively short term will bring not only positive results and satisfaction but also significant savings.