From reports and contracts, to specifications and evaluations, the engineering industry generates huge volumes of data on a daily basis but 90% of engineering and construction industry data is unstructured, making it tortuous to access and harvest business insights from. When you consider that the average engineering designer spends up to a quarter of their day gathering information, it is clear that data access is a massive drain on valuable resources. So, what can companies do to free up their employees so they can put their skills and experience to better use? 

Large firms are routinely hampered by the limited visibility of information stored as unstructured data and new research estimates that Artificial Intelligence (AI) tools represent a potential productivity saving of £37,000 per year for average engineering organisations. 

Preparing internal company data for search access is resource intensive. It takes a significant amount of human time to search for information that is often stored in different legacy systems and disparate geographical locations, and to interpret, label and rank data, ready for inclusion in a search database. This is where technologies such as AI present a clear opportunity for efficiency savings. 

AI technology can process unstructured data such as emails, video chats, and data on other non-traditional channels, and collate it in a way that makes it easily accessible. By accelerating the examination of vast amounts of information to a fraction of the time of manual processes, AI enables engineers to focus their expertise on reviewing and analysing the search results.

This is where knowledge management software, underpinned by AI, can offer real advantages and help to enhance employee productivity. New cloud-based data discovery software solutions, such as those offered by iKVA, have the ability to break down data siloes and process information quickly and with extreme accuracy. iKVA’s solutions, which harness AI, Advanced Machine Learning and vector mapping technology, enable organisations to leverage the unstructured data being generated from across their business in real time to reduce wasted time and empower their teams’ knowledge.   

By enabling access to historic and previously undiscoverable data, AI can also help engineering consultancies to improve planning processes. For example, if an engineer is looking to create a Statement of Work for a new project, AI-enabled discovery tools make it possible to use entire multi-page documents, such as commercial contracts, as a search query, as opposed to just a few keywords, which means that the results are considerably more accurate and relevant. With this knowledge, engineers can refer to relevant customer details or use critical information from similar approved pieces of work, saving precious time, creating fail-proof documents and reducing business risk.

Conclusion

By leveraging advancements in AI technology to unlock insight from your existing undiscoverable data, you will be able to increase productivity, share knowledge and reduce business risk associated with meeting the global demand for new infrastructure and advances in engineering.

Professor Richard Mortier, CTO, iKVA

Richard is the CTO and founder of iKVA, which develops AI-enabled knowledge management software for organisations. 

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