From “Searching” to “Finding”: How AI is Unlocking the Power of Unstructured Data
Document Understanding Drives Productivity
By combining search and analytics with pragmatic AI technologies like NLP and ML, document understanding automatically extracts relevant information from unstructured data sources, saving businesses the time and resources needed to search manually.
As these applications further develop, they can deliver advanced actionable insights to improve business processes and customer experience. In fact, businesses across many industries have started to apply document understanding to help surface insights, including:
♦ Legal departments—Reducing risks by automatically analyzing legal contracts for specific “red-flag” terms.
♦ Government agencies—Analyzing digitized incoming mail to route relevant letters to the right departments, eliminating manual effort and saving hundreds of thousands of agent hours.
♦ Recruiting—Taking on rote tasks like sifting through millions of resumes and automatically matching CVs to job postings.
♦ Banks and financial services—Automatically cross-analyzing loans or mortgages with the borrowers’ profiles from multiple independent sources to deliver better customer experience and engagement.
♦ Storage optimization—Using automated business rules to identify the appropriate action to take with documents stored in expensive on-premise storage—whether to move to lower-cost storage, delete, or archive. ML can also accurately and quickly detect duplicates, allowing for storage cost savings as well as a 360-degree view of enterprise data.
Poised to Reach New Potential
With the increasing range of pragmatic AI solutions available, from open source frameworks and evolving vendors to cloud-based APIs, enterprises stand to benefit more than ever from this ecosystem. They now have the flexibility to integrate appropriate approaches and technologies for their use cases.
While NLP is not perfect, it is being consistently enhanced. And the ML algorithms supporting NLP are seeing significant advances with industry giants like Google, Microsoft, and Amazon making strides to improve accuracy.
We’re also leveraging our own technology assets at Accenture to orchestrate different components of NLP applications, making them easily maintainable and scalable using both custom and ready-built algorithms. This means that NLP and ML are slowly gaining maturity, helping businesses to use document understanding to tackle increasingly complex challenges and finally begin to unlock the full potential of unstructured data.
Now that you know more about document understanding, what potential use cases do you see within your organization? What value will it help you unlock?