With pandemic extending further and countries considering going into second phases of lockdowns and economies taking the hit in a very big way. Businesses have to navigate; often with very limited resources through a stormy market full of picky customers, shrewd competitors, and alert regulators, interactions between all these players make finding answers to business questions a complex process. Recently, machines have demonstrated great abilities to shed light on this chaos and provide answers and context that help guide executives in using Artificial Intelligence to handle complex business issues. Providing machines with the capabilities to find meaning in raw and unstructured data holds notable promise: By 2025, the global NLP market is expected to reach over $34 billion, growing at a CAGR of 21.5%.
NLP is the most common application that falls in the perimeter of the customer-facing side of the business. But NLP is not just confined to chatbots or other Artificial Intelligence or Machine Learning to manage customer relationships.
Businesses can deploy sophisticated communication strategy to uncover behavioural patterns and common pains during the process of generating reports to drive future R&D processes. NLP is used by computing systems to manipulate multiple human languages, this is done to extract meaning and context, generate text, or for multiple other requirements.
Statistics show 80% of all business data is unstructured, unmanaged and cannot be utilised further by the business and thus non-actionable for companies, leading to a lot of wastage in resources and duplication of efforts. Be it either contract, policies, or reports, businesses often struggle to classify and harvest knowledge efficiently and accurately and utilize them to generate value. By using NLP, they can demystify and document the content, access accurate overviews, and help simplify workflows.
Think of an industry associated with heavy content and knowledge management – such as a financial consultancy or an EdTech or even Aviation. Every Service Industry as various arenas where knowledge needs to be harnessed and deployed through various communication channels to the right professionals in need of the information in them. So planning, harnessing, deploying and making the content easily circulated and managed can be leveraged enormously with the help of NLPs.
For example: When filing safety reports, each pilot may describe a specific recurring deficiency differently than a mechanical engineer would. NLP can analyze the submitted information and make it more digestible. In conjunction with additional metadata, it can tell the engineers exactly where to look, helping to achieve greater operational efficiency.
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Business Strategies run on effective data collection as well as the right usage of the data and present it to the right decision-making professionals in the company. Most of the times a lot of data that is very important in the decision-making process are buried in the emails, newsletters, infographics, images, reports, and company websites. This data is usually not harnessable and can create a lot of operational deployment lag and redundant processes being followed leading to a lot of loss of revenue and ill optimisation of the processes.
Deploying human customer care support comes at a cost and Artificial Intelligence is not exactly well equipped with the amount of data it needs to deploy seamless customer service especially in the case of exceptional requests. NLPs are usually deployed where a hybrid of AI and humans needs to be deployed within the customer service industry. NPLs create an environment where the customers can interact partially with the bot and the rest of the conversation in case of exceptional requests can be forwarded to a human professional for better customer experience. This also reduces the time taken to harness data from the customers and understand their issues better. NLP deployment has also reduced the time which the business takes to cater to usually impatient customers.
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