Almost every business today is looking to embrace AI and reap the advantages of its subsets with an intelligence-driven system that captures, processes and synthesizes data resulting in automated data analysis as well as content management. Despite the tremendous success and adoption of Big Data, research shows that only 20% of employees with access to business intelligence tools have literacy or enough domain expertise to utilize them. On the other hand, data presented through charts and graphs do not appear eye-friendly, often leading to misinterpretation and poor decision making. This is where the subset of AI technologies – Natural Language Processing, Natural Language Understanding and Natural Language Generation – and their analytical algorithms come into the picture.
Earlier, businesses needed certain amount of manpower and constant monitoring for semi-smart machines to understand and follow a pre-programmed algorithm. But with time, Artificial Intelligence along with machine learning, artificial neural network, deep learning, natural language processing and natural language generation, machines became intelligent enough to address specific business requirements and goals.
When streamlined and harnessed strategically, these AI-based technologies can comprehend huge datasets to generate valuable insights that eventually help develop customized and impactful solutions. IT giants like Google, Apple, Microsoft and Amazon rely on such algorithms for improving product recommendations, online search, voice-enabled mobile services, etc.
Although they may come across as daunting technical jargons – NLP, NLG, and NLU are seemingly complex acronyms used to explain straightforward processes. Here the breakdown:
The reading part of Natural Language Processing is complicated and includes many functions such as:
Natural Language Understanding is an important subset of Artificial Intelligence and comes after Natural Language Processing to genuinely understand what the text proposes and extracts the meaning hidden in it. Conversational AI bots like Alexa, Siri, Google Assistant incorporate NLU and NLG to achieve the purpose.
Humans have always needed data in order to formulate new ideas and communicate them. However, with a major influx of data that needs to be assessed along with the need to reduce costs significantly, enterprises need to identify ways to streamline.
Coming to Natural Language Generation, the primary advantage lies in its ability to convert the dataset into legible narratives understood by humans. Upon processing statistical data present in spreadsheets, NLG can produce data-rich information unlike Natural Language Processing that only assesses texts to form insights.
With Natural Language Generation, data can be assessed, analyzed and communicated with precision, scale and accuracy. With smart automation of routine analysis and related tasks, productivity surges and humans can focus on more creative, high value – high return activities.
In an interesting use case, The Associated Press leveraged the report-generating capability of Natural Language Generation to develop reports from corporate earnings data. This means they no longer need human reporters dedicating their time and energy wading through pools of data and then writing a report. Instead, as NLG produces thousands of narratives automatically once perfectly set up, they can invest their resources in performing more critical tasks.
The advantages of Natural Language Generation go beyond the usual perception that people have when it comes to AI adoption. Some of its benefits for marketing and business management are:
Automated Content Creation
What NLG is mainly capable of is its ability to create on organized structure of data from the information processed in previous stages of NLP and NLU. By placing this well-structured data in a carefully configured template, NLG can automate the output and supply documentable form of data such as analytics reports, product description, data-centric blog posts, etc. In such case, algorithmically programmed machines are at complete liberty to create content in a format as desired by content developers. The only thing left for them to do then is to promote it to the target audience via popular media channels. Thus, Natural Language Generation fulfils two purposes for content developers & marketers:
Content Generation revolves around web mining and relies on search engine APIs to develop effective content made from using various online search results and references.
So far, several NLG-based text report generation systems have been built to produce textual weather forecast reports from input weather data.
Additionally, a firm destined to generate accurate weather forecast reports will be able to translate the statistical structure of weather forecast data into an organized, reader-friendly textual format using the real-time analytical power of Natural Language Generation.
Significant Reduction in Human Involvement
With Natural Language Generation in place, it becomes inessential to hire data-literate professionals and train them for the job they do. So far, as corporate theories go, human force is key to understanding consumer’s interests, their needs and converting them in written stories.
However, with Natural Language Generation, machines are programmed to scrutinize what customers want, identify important business-relevant insights and prepare the summaries around it.
The value of NLG is doubled after realizing how expensive and ineffective it is to employ people who spend hours in understanding complex data. Even Gartner predicts that 20% of business content will be authored through machines using Natural Language Generation and will be integrated into major smart data discovery platforms by 2018. Legal documents, shareholder reports, press releases or case studies will no longer require humans to create.
Predictive Inventory Management
The success of inventory management for any store results in a great boost in terms of business goals and overall resultant profit given that certain products have very high margins. Data matters most and plays a key role in areas such as supply chain, production rate and sales analytics. Based on this information, store managers can make decisions about maintaining inventory to its optimal levels. However, it is not reliable to always expect managers to be sound with data and interpret them efficiently.
When it comes to advanced NLG, it can work as an interactive medium for data analysis and makes the overall reporting process seamless and insightful. Instead of having to go through several charts and bar graphs of data, store managers get clear narratives and analysis in desired format telling them whether or not they require specific item next week. With natural language generation, managers have the best predictive model with clear guidance and recommendations on store performance and inventory management.
Performance Activity Management at Call Centre
It is prudent to conduct performance reviews and accurate training for further improvements within a call centre. However, as covered in the above use cases, charts won’t help much in communicating the exact pain points and areas of improvement unless it has strong narratives in form of feedback. This is where the advantages of Natural Language Generation accompanied with NLP lies.
NLG can be strategically integrated in major call centre processes with in-depth analysis of call records and performance activities to generate personalized training reports. It can clearly state just how call centre employees are doing, their progress and where to improve in order to reach a target milestone.
For any business looking to adopt and garner the advantages of Natural Language Generation, it is vital to make sure that they keep meet certain guidelines such as –
You must have a matching use case
Not every content creation use case needs Natural Language Generation. It is a unique technology designed to generate specific answers. It is impossible to generate all content you see on blogs. If the story you convey regularly has numbers and consistent format to display, NLG could be the best resource for automating those tasks.
To give an example, a well-known marketing agency PR 20/20 has used the advantages of Natural Language Generation to minimize analysis and production time with Google Analytics reports by a staggering 80%.
Another example being The Washington Post who created Heliograf, an AI-based engine using Natural Language Generation to write stories for the Olympics and Election Races in 2016.
Nurture realistic goals
AI technologies need some time before they can automate all your operations in real time. To integrate and reap the advantages of Natural Language Generation, it requires certain time frame to be setup completely. The intelligence you choose has a price tag, so you should be realistic about your precise requirements, AI’s actual capabilities and scalability. If NLG practically cuts down time and cost for your organization while generating reports and narratives, you can opt for it.
Your Data must be structured enough
AI needs specific form of inputs and NLG will only function if it is fed structured data. Check if your dataset is organized and optimized. Make sure that the data you upload is clean, consistent and easy-to-consume or you will not get satisfactory results despite the relevant use case.
The growing movement towards having service-specific intelligent systems exhibits trust in advanced AI technologies. There is little doubt that Natural Language Processing is going from exceptional to essential as tech giants like Google, Apple, Amazon and IBM show promises of ample investment in this. Tractica claims that by 2025 the global NLP market is expected to reach $22.3 billion.
In a few years from now, intelligent systems are going to transform our daily interactions with technology as advanced NLG will grow more intuitive and conversational with information delivered in comprehensive formats. A powerful system that has capability to explain conclusions in a clear and concise manner is likely to drive much-needed business intelligence in the coming era.
Configured intelligently, chatbots will be far more intelligent and no longer be delivering just plain conversations for queries and resolutions but also engage, explain and illuminate through advanced NLG. Synchronized with enterprise-specific workflow management, advanced Natural Language Generation will help entrench a far superior network of engagement across managers, executives, employees and customers to empower business dynamics and yield accurate output in a minimal timeframe.
In the end, for businesses confronting the challenges pertaining to data analysis and multilanguage support, the real-time automation of report creation, content generation and deriving actionable insights can be achieved with the advantages of Natural Language Generation. With NLG in place, it is possible for struggling businesses to think beyond conversational chatbots and integrate an automatic, goal-oriented system of efficiently producing information in a format as expected by the end user. Enterprises seeking to deploy robust and dedicated Natural Language Generation based conversational interfaces, virtual assistants or software applications must collaborate with the right technology vendors and innovation partners who are versed in delivering comprehensive AI-powered system solutions.
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